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Searching for real biological neuron firing data [numeric]

Searching for real biological neuron firing data [numeric]


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I am searching for real neuron firing data, so I can look at each neuron for fire timing (frequency) and incoming/outgoing signal-strength. I already found short youtube videos of real neuron firing footage (measured via calcium), but I am searching for numeric data.

Data-Requirements

  • Region is not important
  • the longer time range, the better

The website crcns.org hosts open source data recorded from neurons in various brain areas.


CONCLUSION

Mirror neurons are a fascinating class of cells that deserve to be thoroughly investigated in the monkey, and explored systematically for possible homologues in humans. The early hypothesis that these cells underlie action understanding is likewise an interesting and prima facie reasonable idea. However, despite its widespread acceptance, the proposal has never been adequately tested in monkeys, and in humans there is strong empirical evidence, in the form of physiological and neuropsychological (double) dissociations, against the claim.

Why does the hypothesis remain prominent, indeed all but accepted as fact, despite solid evidence to the contrary? I suggest that Pillsbury was right. Motor theories are simple and easy to understand: “we understand action because the motor representation of that action is activated in our brain” (Rizzolatti et al., 2001, p. 661). We see someone pouring liquid from a bottle into a glass this activates a motor representation associated with our own liquid-pouring experiences, and voilà, we have understanding. But scratch the surface of action understanding and it is immediately clear that the problem is not that simple (Pinker, 1989, 2007). For example, the motor act of pouring liquid from a bottle into a glass could be understood as pouring, filling, emptying, tipping, rotating, inverting, spilling (if the liquid missed its mark), defying/ignoring/rebelling (if the pourer was instructed not to pour), and so on. A motor representation cannot distinguish between the range of possible meanings associated with such an action. A mirror neuron theorist might protest that it is the goal or intention that is coded by mirror neurons, not the specific actions (Fogassi et al., 2005). But a goal, say to fill a glass with water, can be accomplished with any number of individual actions or sequence of actions: pouring from a pitcher, turning a spigot, dipping the glass in a lake, setting the glass in the rain, positioning an array of leaves to collect and funnel dew into the glass, digging a well and pumping water into the glass, or even commanding someone else to do any of these! Given the range of meanings associated with a specific action and the range of actions that can achieve a specific goal, there must be a clear distinction between goals and the motor routines that are implemented in a given circumstance to achieve those goals. If mirror neurons are reflecting goals and not actions, then a statement about mirror neurons such as, “we understand action because the motor representation of that action is activated in our brain” (Rizzolatti et al., 2001, p. 661) is either false because mirror neurons do not code actions, or it is false because motor representations are not the basis of action understanding.

Unfortunately, more than 10 years after their discovery, little progress has been made in understanding the function of mirror neurons. I submit that this is a direct result of an overemphasis on the action understanding theory, which has distracted the field away from investigating other possible (and potentially equally important) functions.


Biological Rhythms

Circadian, infradian and ultradian and the difference between these rhythms

The physiological processes of living organisms follow repetitive cyclical variations over certain periods of time. These bodily rhythms have implications for behavior, emotion and mental processes.

There are 3 types of bodily rhythms:

  1. Circadian rhythms: follow a 24-hour cycle: e.g. the sleep-waking cycle
  2. Ultradian rhythms: occur more than once a day: e.g. the cycles of REM and NREM sleep in a single night’s sleep
  3. Infradian rhythms: occur less than once a day: e.g. menstruation (monthly) or hibernation (yearly)

All bodily rhythms are controlled by an interaction of:

  1. Endogenous pacemakers (EP’s). Internal biological structures that control and regulate the rhythm.
  2. Exogenous zeitgebers (time givers) (EZ’s). External environmental factors that influence the rhythm.
  • CIRCADIAN RHYTHMS
  • Heart rate, metabolic rate, breathing rate and body temperature all reach maximum values in the late afternoon/early evening and minimum values in the early hours of the morning. If we reverse our sleep-waking pattern these rhythms persist. This indicates human bodies are evolved for activity in the day and rest at night and, indeed, being nocturnal or disrupting the circadian cycle is highly stressful and physiologically and psychologically harmful.
  • The EP controlling the sleep-waking cycle is located in the hypothalamus. Patterns of light and darkness are registered by the retina, travel up the optic nerves to where these nerves join (optic chiasma), and then pass into the superchiasmatic nucleus (SCN) of the hypothalamus. If this nerve connection is severed circadian rhythms become random. The same effect is produced by damaging the SCN of rats, and people born without eyes cannot regulate bodily rhythms.
  • Ralph bred a group of hamsters to follow a (shortened) 20-hour circadian cycle. SCN cells were removed and transplanted into the brains of rat foetuses with normal rhythms. Once born, these rats adopted a 20-hour cycle. Their brains were then transplanted with SCN cells from 24-hour cycle hamsters and within a week their cycles had adopted this new 24 cycle.
  • When cells from the SCN were removed from rats the 24-hour cycle of neural activity persisted in the isolated cells. Recent research by Yakazaki found that isolated lungs and livers, and other tissues grown in a lab still persist in showing circadian rhythms. This suggests cells are capable of maintaining a circadian rhythm even when they are not under the control of any brain structures and that most bodily cells are tuned in to following a daily circadian rhythm.
  • All of this evidence points to the fact that circadian rhythms are primarily controlled by evolutionarily-determined, biological structures that exert a strong influence on us to maintain normal sleep-waking patterns.
  • However, circadian rhythms are also influenced by EZ’s - ‘cues’ in the environment- about what time of day or night it is. In 1975 Siffre spent 6 months underground in an environment completely cut off from all EZ’s. Although he organised his time in regular patterns of sleeping and waking his body seemed to have a preference for a 25 hour rather than a 24-hour cycle. This implies that circadian rhythms are mainly controlled by EP’s rather than EZ’s.
  • Another piece of evidence in support of this idea is that Innuit Indians who live in the Arctic Circle inhabit an environment that has hardly any darkness in summer and hardly any light in winter. If the sleep-waking cycle was primarily controlled by EZ’s they would tend to sleep a huge amount in winter and hardly at all in summer. However, this is not the case - they maintain a fairly regular pattern of sleeping and waking all year around.

  • ULTRADIAN RHYTHMS
  • With the development of certain scientific equipment, it became possible to study sleep more objectively.

• The electroencephalogram (EEG) measures electrical brain activity.

• The electrooculogram (EOG) measures eye movement.

• New-born - 16 hours’ sleep, 50% REM (patterns of REM are observed in foetuses).

• 3-year-old - 12 hours’ sleep, 25% REM.

• Adult - 8 hours’ sleep, 22% REM.

The effect of endogenous pacemakers and exogenous zeitgebers on the sleep/wake cycle

Circadian rhythms follow a 24-hour cycle (e.g. the sleep-waking cycle) and are controlled by an interaction of:

  1. Endogenous pacemakers (EP’s). Internal biological structures that control and regulate the rhythm.
  2. Exogenous zeitgebers (time-givers) (EZ’s). External environmental factors that influence the rhythm.

The EP controlling the sleep-waking cycle is located in the hypothalamus. Patterns of light and darkness are registered by the retina, travel up the optic nerves to where these nerves join (optic chiasma), and then pass into the suprachiasmatic nucleus (SCN) of the hypothalamus. If this nerve connection is severed circadian rhythms become random. The same effect is produced by damaging the SCN of rats, and people born without eyes cannot regulate bodily rhythms.

However, circadian rhythms are also influenced by EZ’s - ‘cues’ in the environment - about what time of day or night it is. Siffre spent 6 months underground in an environment completely cut off from all EZ’s. Although he organised his time in regular patterns of sleeping and waking his body seemed to have a preference for a 25 hour rather than a 24-hour cycle. This implies that circadian rhythms are mainly controlled by EP’s rather than EZ’s.

Another piece of evidence in support of this idea is that Innuit Indians who live in the Arctic Circle inhabit an environment that has hardly any darkness in summer and hardly any light in winter. If the sleep-waking cycle was primarily controlled by EZ’s they would tend to sleep a huge amount in winter and hardly at all in summer. However, this is not the case- they maintain a fairly regular pattern of sleeping and waking all year around.

Disruption of the circadian sleep-waking cycle (e.g. jet lag and shift work) has been shown to cause negative physical and psychological effects.

Jet Lag occurs when we cross several world time zones quickly. Circadian rhythms will be disrupted as although our endogenous pacemakers stay the same, the exogenous zeitgebers (patterns of light and dark in the new environment) have changed.


Neuromorphic Computing: Modeling The Brain

Competing models vie to show how the brain works, but none is perfect.

Can you tell the difference between a pedestrian and a bicycle? How about between a skunk and a black and white cat? Or between your neighbor’s dog and a colt or fawn? Of course you can, and you probably can do that without much conscious thought. Humans are very good at interpreting the world around them, both visually and through other sensory input.

Computers are not. Though their sheer calculation speed surpassed that of human “calculators” long ago, large data centers equipped with terabyte-scale databases are only beginning to match the image recognition capabilities of an average human child.

Meanwhile, humans are creating larger and more complex digital archives and asking more complex questions about them. How do you find the photo you want in a collection of thousands? How does a music service answer a customer request for “more like this?” How can computers support technical decision making when the source data is often noisy and ambiguous?

Neuromorphic computing seeks to build systems informed by the architecture of biological brains. Such systems have the potential to analyze data sets more rapidly, more accurately, and with fewer computing resources than conventional analysis.

In the current state of the art, people who discuss neuromorphic computing and big data analysis are usually talking about neural networks. While current-generation neural networks are important for practical problem solving and will be discussed in a future article, they don’t really have much resemblance to biological brains.


Fig. 1: Neuron cell diagram. Source: Wikimedia Commons.

How neurons work
The first important difference is the sheer scale of connectivity in biological brains. The nucleus of a nerve cell is at the center of a web of fibers, or axons, each of which branches into potentially thousands of dendrites. Each dendrite can connect to a neighboring neuron across a junction known as a synapse. Though electronic analogues often define this web of connections as fixed, it is not. Synaptic connections are made and broken constantly. As Jeff Hawkins, co-founder of Numenta, explained in a talk at the 2015 IEEE Electron Device Meeting, “[Biological] memory is a wiring problem, not a storage problem,” and a large wiring problem at that.

In the human neocortex—responsible for functions like sensory perception, spatial reasoning, and language—there are millions of neurons, each of which may communicate with thousands of neighbors. The neocortex alone has billions of connections. The brain as a whole has trillions. For comparison, the largest server-based neural networks have about 11 billion connections.

Furthermore, the brain is an analog system. Transistors in electronic circuits are either on or off. Memory elements store either 1 or 0. Synaptic connections are not directly equivalent to memory capacitors, but they can be strong or weak, and can be reinforced or depressed in response to stimuli.

More precisely, neurons communicate through electrical currents resulting from the flow of sodium and potassium ions. There are differences in ion concentrations between intracellular and extracellular fluids. When a pre-synaptic neuron releases a neurotransmitter compound, the ion channels in the post-synaptic neuron are either excited or depressed, increasing or decreasing the flow of ions between the cell and the extracellular fluid. Doo Seok Jeong, senior scientist at the Korea Institute of Science and Technology, explained that the cell membrane of the post-synaptic neuron acts as a capacitor. Ions accumulate until a critical threshold is reached, at which point a “synaptic current” spike propagates along the neural fibers to other synapses and other neurons.

The capacitor will charge and discharge repeatedly until the neurotransmitter concentration dissipates, so the synaptic current actually consists of a chain of related spikes. The length of the chain and the frequency of individual spikes depends on the original stimulus. The response of a particular neuron to a particular synaptic current chain is generally not linear. The relationship between the input and output signals is the “gain” of the neuron.

It must be emphasized, though, that the relationship between external stimuli and synaptic current is not clear. Biological brains produce chains of synaptic current spikes that appear to encode information. But it is not possible to draw a line between the image of “cat” received by the photoreceptors in the retina and a specific pattern of synaptic spikes generated by the visual cortex, much less the positive and negative associations with “catness” that the image might produce elsewhere in the brain. A number of factors, such as the non-uniform cell membrane potential, introduce “noise” into the signal and cause the loss of some information. However, the brain clearly has mechanisms for extracting critical information from noisy data, for discarding irrelevant stimuli, and for accommodating noise-induced data loss. The biological basis for these mechanisms is not known at this time.

Firing synaptic current spikes
In modeling the brain, at least two levels need to be considered. The first is the biological mechanism by which chains of synaptic current are generated and propagated. The second is the role of these spikes in memory and learning. Both levels face a tradeoff between biological accuracy and computational efficiency. For example, many commercial neural networks use a “leaky integrate and fire” (LIF) model to describe the propagation of synaptic spikes. Each neuron has a pre-determined threshold, and will “fire” a synaptic signal to its neighbors when that threshold is exceeded. In electronic networks, similarly, each neuron applies pre-determined weights to input signals to determine the output signal. Rapid determination of the appropriate weights for a particular problem is one of the central challenges of neural network design, but once the weights are known, the output signal is simply the dot product of the input signal with the weight matrix.

This approach is computationally efficient, but not biologically realistic. Among other things, the LIF model ignores the timing of synaptic spikes, and therefore the causal relationship between them. That is, signal “A” may precede or follow signal “B” and the response of biological neurons will depend on both the relative strength and the relative timing of the two signals. A strict LIF model will only recognize whether the combination of the two exceeded the node’s threshold. The biological behavior is analog in nature, while the electronic behavior of conventional neural networks is not.

Two alternatives to the LIF model incorporate additional biophysical pathways, increasing biological realism at the expense of computational efficiency. The spiking neuron model takes into account the cell membrane’s recovery rate—how quickly the membrane potential returns to its nominal value. This model can describe different kinds of neurons, but it preserves computational efficiency by only considering variations in the membrane potential.

A much more sophisticated alternative, the Hodgkin-Huxley model, considers several different biophysical contributions, including membrane potential and the sodium and potassium ion currents. It establishes the dependence between the conductance of ion channels and the membrane potential. Further extensions of the original Hodgkin-Huxley model recognize several different potassium and sodium currents and incorporate neurotransmitters and their receptors. The HH model is substantially more realistic, but also much more computationally complex.

These three models describe the fundamental mechanisms of synaptic current generation and propagation in increasing levels of detail. For the processes we call “thinking,” — memory, learning, analysis — an additional step is required. In biological brains, this is synaptic plasticity, which is the ability of the brain to strengthen and weaken, break and remake synaptic connections. The chains of synaptic spikes provide the input for learning rules, the next level in brain modeling.

From current to data: synaptic plasticity
One of the most basic learning rules—proposed in 1982 by Brown University researchers Elie Bienenstock, Leon Cooper, and Paul Munro (BCM)—expresses synaptic change as a product of the pre-synaptic activity and a nonlinear function of post-synaptic activity. It is expressed in terms of firing rates, and cannot predict timing dependent modification of synapses.

A somewhat more sophisticated model, spike timing-dependent plasticity, recognizes that the relative timing of two signals also matters. Is a positive or negative experience associated with a particular stimulus? How closely? These details affect the relative strengths of synaptic connections. The most basic STDP models compare the timing of pairs of spikes. If the pre-synaptic spike comes before the post-synaptic spike, the connection is enhanced. Otherwise, it is weakened. However, the basic STDP model does not reproduce experimental data as well as the BCM model does.

One proposed modification, a triplet-based STDP learning rule, compares groups of three spikes, rather than pairs. It behaves as a generalized BCM rule in that post-synaptic neurons respond to both input spiking patterns and correlations between input and output spikes. These higher order correlations are ubiquitous in natural stimuli, so it’s not surprising that the triplet rule reproduces experimental data more accurately.

Which of these models and learning rules is the “best” choice largely depends on the situation. Neuroscientists seek to develop models that can accurately reproduce the behavior of biological brains, hoping to gain insight into the biological mechanisms behind human psychology. Neuromorphic computing seeks to use biological mechanisms to inform the architecture of electronic systems, ultimately deriving improved solutions to practical data analysis problems. Reproduction of a specific chain of synaptic spikes or a specific learning behavior is secondary to accuracy and computational efficiency.

Part two of this series will show why the “best” neural networks are not necessarily the ones with the most “brain-like” behavior.

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Abstract

Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, “Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?” For another example, “How much of a neuron’s observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?” However, a neuron’s theoretical firing rate is not necessarily well-defined. Consequently, neuroscientific questions involving the theoretical firing rate do not have a meaning in isolation but can only be interpreted in light of additional statistical modeling choices. Ignoring this ambiguity can lead to inconsistent reasoning or wayward conclusions. We illustrate these issues with examples drawn from the neural-coding literature.


Silicon neuron: digital hardware implementation of the quartic model

This paper presents an FPGA implementation of the quartic neuron model. This approach uses digital computation to emulate individual neuron behavior. We implemented the neuron model using fixed-point arithmetic operation. The neuron model’s computations are performed in arithmetic pipelines. It was designed in VHDL language and simulated prior to mapping in the FPGA. We show that the proposed FPGA implementation of the quartic neuron model can emulate the electrophysiological activities in various types of cortical neurons and is capable of producing a variety of different behaviors, with diversity similar to that of neuronal cells. The neuron family of this digital neuron can be modified by appropriately adjusting the neuron model’s parameters.

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Reflections on Mirror Neurons

In 1992, a team at the University of Parma, Italy discovered what have been termed “mirror neurons” in macaque monkeys: cells that fire both when the monkey took an action (like holding a banana) and saw it performed (when a man held a banana). Giacomo Rizzolati, the celebrated discoverer, will deliver the Keynote Address at the APS Convention in Washington DC, USA, on May 26, 2011, and report on his latest findings. To tide us over until then, here’s a report on the state of mirror neuron science.

Like monkeys, humans have mirror neurons that fire when we both perceive and take an action. Locating the tiny cells means attaching electrodes deep inside the brain. As this has hardly been practical in humans, studies have had to rely on imaging, which shows which areas of the brain “light up” in different circumstances. By last year, a meta-analysis of 139 imaging studies confirmed mirroring activity in parts of the human brain where, in monkeys, mirror neurons are known to reside. Because the lit-up areas contain millions of neurons, for humans most researchers speak of a “mirror system,” rather than mirror cells. Last year, single mirror neurons were recorded in humans for the first time, using in-depth electrodes, in 21 epileptic patients.

The cells showed up unexpectedly in an area known for memory, the medial temporal lobe, as well as in areas where they were expected. The discovery suggests that memory is embedded in our mirror system, says Marco Iacoboni (University of California, Los Angeles), a leading authority in the field and a co-author of the epilepsy study. Perhaps, he says, we form memory “traces” whenever we see or observe an action. “It’s a lovely idea,” says Rizzolati, though he adds that it’s too early to say.

The mirroring system includes a mechanism that helps the brain record the difference between seeing and acting. In the epilepsy study, some neurons fired more during action and others fired more during observation. These same cells, Iacoboni proposes, help us distinguish between the self and others.

That’s an important issue, to say the least. We often confuse our own actions with those of other people. In a study published recently in Psychological Science, Gerald Echterhoff, University of Muenster, Germany, and his co-authors reported that people who had watched a video of someone else doing a simple action — shaking a bottle or shuffling a deck of cards — often mistakenly recalled two weeks later that they had done so themselves. The mistake occurred even when participants were warned that they could mix up other people’s actions with their own. Echterhoff and a co-author, Isabel Lindner, of the University of Cologne, Germany, plan to conduct imaging studies to test if the phenomenon is related to mirroring.

Mirror neurons are present in infant monkeys. Three years ago, the first abstract appeared reporting that surface electrodes had recorded mirroring in monkeys one- to seven-days old as they watched humans stick out their tongues and smack their lips. Says Pier Francesco Ferrari, of the University of Parma, and co-author of an upcoming study, “This is the first evidence that infants have a mirror mechanism at birth that responds to facial gestures. Without any experience of stimulation, they are able to focus their attention on the most relevant stimuli and respond.” Sometimes the days-old monkeys even stuck out their tongues when they saw the human tongue, Ferrari says.

In monkeys, mirror neurons are present in the insula, an emotion center. Despite all the claims linking mirror neurons to empathy, Rizzolatti says he is only now reporting the discovery of a few mirror neurons in the insula in monkeys, “a reservoir for disgust and pain. Many other factors control how we react,” he says, “but mirror neurons are how we recognize an emotion in others neurally.”

Mimicry, linked to mirror neurons, makes monkeys bond. The idea that mimicry helps humans bond is well-accepted, but the first controlled experiment, with a monkey, came last year, Ferrari says. In that study, reported in Science, his team presented monkeys with a token and rewarded them with treats if they returned it. The monkeys had a choice of returning the token to either of two investigators, only one of whom was imitating the monkey. The monkeys consistently chose to return the token to the person who imitated them and spent more time near that investigator.

Mimicry in humans reflects social cues. The idea that we’re primed in one part of our brain to like those who mimic us doesn’t rule out other discriminations. Unconscious mimicry is deeply social and, as such, reflects prejudice, says Rick van Baaren of Radboud University in the Netherlands. In a 2009 overview of the science of mimicry published in the Philosophical Transactions of The Royal Society, he points out that people are more likely to mimic a member of the same ethnic group, less likely to mimic a stigmatized person who is obese or has a scar, and less likely to mimic members of a group we view with prejudice. In fact, humans tend to react badly when mimicked by someone from an “out group.”

The mirror systems of two people can move in tandem. Many researchers had proposed that the brains of two people “resonate” with each other as they interact, with one person’s mirror system reflecting changes in the other. Last spring, the Proceedings of the National Academy of Sciences reported on the brain activity of people playing the game of charades. The observer and gesturer performing the charade did move neurologically in tandem, says co-author Christian Keysers, of the University Medical Center in Groningen, The Netherlands. Keysers says the discovery backs up the idea that mirroring plays a key role in the evolution of language. We’re exquisitely responsive to gestures, he says “Nobody had ever shown that during gestural communication the observer’s mirror system tracks the moment to moment state of the gesturer’s motor system.”

Mirror neurons respond to sound. In monkeys, mirror neurons fire at sounds associated with an action, such as breaking a peanut or tearing paper. Mirroring has been discovered in birds hearing bird song, and in humans. Recent work, led by Emiliano Ricciardi at the University of Pisa, Italy, found that blind people, using their hearing, interpret the actions of others by recruiting the same human mirror system brain areas as sighted people.

Mirror neurons code intentions. Whether mirror neurons register the goal of an action or other higher-level systems must chip in to judge other people’s intentions has been the subject of much debate. The evidence is accumulating that mirror neurons “implement a fairly sophisticated and rather abstract coding of the actions of others,” says Iacoboni. One clue is that while a third of all mirror neurons fire for exactly the same action, either executed or observed, the larger number — about two thirds — fire for actions that achieve the same goal or those that are logically related — for example, first grasping and then bringing an object to the mouth. And these neurons make fine distinctions: When a monkey observed an experimenter grasping an object and pantoming the same action, the neurons fired when the experimenter grasped the object but not during the pantomime. “In academia, there is a lot of politics and we are continuously trying to figure out the ‘real intentions’ of other people,” Iacoboni says. “The mirror system deals with relatively simple intentions: smiling at each other, or making eye contact with the other driver at an intersection.”

Mirroring increases with experience. In the first studies, monkeys mirrored when they saw a person grasping food but not if the person used a tool. That made sense because monkeys don’t use tools. In later research, monkeys did mirror humans using a tool Iacoboni suggests that their brains had “learned,” adjusting to seeing researchers with tools. In humans, more mirroring activity occurs when dancers see other dancers perform routines they know well. Mirroring in blind people is more active in response to more familiar action sounds.

Stimulating the mirror system helps stroke victims. If mirroring develops as we learn, perhaps triggering mirroring can teach. Two studies with stroke victims, for example, have found that stimulating the mirror system helped them recover particular motor actions, says Ferdinand Binkofski at the University of Luebeck, Germany. When stroke victims received “action observation therapy,” in which they observed an action repeatedly, they regained more ability. Compared to a control group, the stroke victims also showed more mirroring in brain scans.

Children with autistic syndromes have mirroring defects. As early as 2001, researchers hypothesized that a deficit in the mirror neuron system could explain some of the problems of autistic patients. As of September, 2010 twenty published papers using brain imaging, magnetoencephalography, electroencephalography, and transcranial magnetic stimulation support this idea, and four failed to support it, according to Iacoboni.

The hope is that basic science in the mirror system could lead to a better understanding of emotional difficulties. As Ferrari points out, some infant monkeys separated from their mothers show “symptoms like those in autistic kids. You see them rocking and avoiding your gaze.” Others develop normally. Ferrari and his colleagues plan to follow the infants they studied and measure whether strong mirror neuron activity in the first week of life indicates sociability later on. “We hope to create a picture of how brain activity interacts with the social environment to put some monkeys more at risk,” he says. “The obvious direction is to translate this to humans.”

Mirror neuron research continues to grow fast, across disciplines. Already the number of items produced by a PubMed search, for example, increased twenty-fold between 2000 and 2010, although that number only doubled for “Stroop and brain,” another popular topic. The ongoing technical challenge remains: Mirror neurons are not the majority of cells in the brain areas where they are located, so it is still difficult to pinpoint their role when those areas show spiking activity. Iacoboni suggests that mathematical modeling will help make more of this data useful. Such modeling allowed Keysers, for example, to establish the existence of resonance in the charades study. So what can we expect next? Most likely, Iacoboni, says, more work with depth electrodes in neurological patients and studies like Ferrari’s to test whether mirroring is a biomarker of sociality. A promising underexplored subject is the inhibitors that keep us from mimicking (but fail recovering addicts who relapse when they see others consume). Behind all this work will be a growing consensus that mirror neurons evolved in humans so we could learn from observation and communication. œ


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A Biological Theory Of Motivation

This biological theory of motivation (The Intuition Theory), suggests that motivation levels are regulated by neural pattern recognition events. Subconscious drives impel people to achieve excellence, or to spend exceptional energies on services to humanity. There have been five well known theories of motivation, which seek to explain the reasons why a few people spend more energy than others to achieve their goals. All these theories essentially outline the crucial impact of neural activities on motivation.

The Instinct Theory suggests that motivated behavior is a biological instinct. The Drive Reduction Theory suggests that motivated behavior seeks to reduce the tension of drives triggered by sensations such as hunger or pain. The Arousal Theory suggests that motivated behavior is the result of a search for an optimum level of arousal.

The Psychoanalytic Theory suggests that motivated behaviors follow fundamental drives to survive and avoid death. The Humanistic Theory presents the Maslow Hierarchy, where people strive to achieve their maximum potential. Instinctual responses, drive reduction, arousal, psychological and humanistic drives are the varied aspects of the powerful neural drives, which ultimately motivate people. The Intuition Theory suggests that these drives are powered by the intuitive choices of the mind.

  • Intuition focuses the nervous system on an activity. Wisdom, or emotions decide.
  • Strategic drives use coded knowledge to achieve objectives.
  • Motivation is limited by neural wisdom. Excellence is delivered by wider knowledge and skills.
  • Excellence results in flow.
  • When emotions dominate, an individual will persist in the task.
  • Speed dial circuits, created by painful experiences, focus people on specific goals.
  • Different people are motivated by different rewards.
  • Many people are not fortunate enough to work on rewarding careers.
  • The Intuition Theory suggests that inner wisdom and emotions motivate the system.

Can An Algorithm Be Controlling The Mind?
I am not a physician, but an engineer. Way back in 1989, I catalogued how the ELIMINATIO N approach of an AI Expert System ਌ould reveal a way by which the nervous system could store and retrieve astronomically large memories.  That insight is central to the six unique new premises presented in this website. 

These new premises could explain an enigma.   A physician is aware of thousands of diseases and their related symptoms.  How does he  note a symptom and focus on a single disease  in less than half a second?   How ਌ould he identify Disease X out of 8000 diseases with just a glance?  

First, the total born and learned knowledge available to the doctor could not exist anywhere other than as the stored/retrieved data within the 100 billion neurons in his brain.  The perceptions, sensations, feelings and physical activities of the doctor could only be enabled by the electrical impulses flowing through the axons of those neurons.  The data enabling that process could be stored as digital combinations.

Second, combinatorial decisions of neurons cannot be made by any entity other than the axon hillock, which decides the axonal output of each neuron.  The hillock receives hundreds of inputs from other neurons.  Each hillock makes the pivotal neuronal decision about received inputs within 5 milliseconds.  A xon hillocks could be storing digital combinations.    It could be adding each new incoming digital combination to its memory store.    The hillock could fire impulses, if it matched a stored combination. If not, it could inhibit further impulses.  Using stored digital data to make decisions about incoming messages could make the axon hillocks intelligent.

Third, combinations are reported to enable a powerful coding mode for axon hillocks.  Olfactory combinatorial data is known (Nobel Prize 2004) to store memories for millions of smells.  Each one of 100 billion axon hillocks have around a 1000 links  to other neurons.  The hillocks can mathematically store more combinations than there are stars in the sky. Each new digital combination could be adding a new relationship link.  In this infinite store, specific axon hillocks could be storing all the symptom = disease (S=D) links known to the doctor as digital combinations.

Fourth, instant communication is possible in the nervous system.  Within five steps, information in one hillock can reach all other relevant neurons.  Just 20 Ms for global awareness.  Within the instant the doctor observes a symptom,  feedback and feed forward links could inform every S=D link of the presence of the symptom.  Only the S=D link of Disease X could be recalling the combination and recognizing the symptom.

Fifth, on not recognizing the symptom, all other S=D hillocks could be instantly inhibiting their impulses. The S=D links of Disease X could be continuing to fire. Those firing S=D link would be recalling past complaints, treatments and signs of Disease X, confirming the diagnosis.  This could be enabling axon hillocks to identify Disease X out of 8000 in milliseconds.

W orldwide interest in this website is acknowledging its rationale. Not metaphysical theories, but processing of digital memories in axon hillocks could be explaining innumerable mysteries of the mind.  Over three decades, this website has been assembling more and more evidence of the manipulation of emotional and physical behaviors by narrowly focused digital pattern recognition.  It has also been receiving over 2 million page views from over 150 countries.

A Biological Theory Of Motivation 
What are the Engines of Motivation?
The choices a person makes in life are determined by the options available within his mind. Imagine a system, which runs through millions of possibilities to make each choice.  Imagine intuition, an algorithmic process, which enables the nervous system to deliver swift decisions. Animals cannot afford to freeze into immobility, unable to decide between chewing grass and drinking water. If the choice is to chew grass, the drive to quench thirst is instantly inhibited. 

Imagine  intuition ਊs a pattern recognition process.  Intuition  eliminates unfit possibilities within milliseconds to choose a single option for action. When an intuitively driven system knows the answers, actions flow with effortless energy. When answers are lacking, the system fumbles. In more complex situations, emotions guide system strategies. When emotions dominate, the system acts with passion for good or evil.

A Biological Theory Of Motivation 
What Are Neural Drives?
Since solutions are often not immediately available, neural drives constantly seek answers to problems faced by the system. Imagine purpose driven neural drives.   The human mind has immense knowledge, stored as coded answers from myriad evolutionary and real life experiences.   When you decide to move a piece on a chess board, sequences of motor impulses persist from the instant your hand picks up the piece, till it is set down in its new position. Muscle movements are sequences of micro-managed contractions, which last just milliseconds. Each signal invokes only a tiny contraction.

Myriad muscles contract and relax over thousands of cycles till your chess piece reaches its desired position. The motor codes continually issue precise instructions to meet a set objective. Your hand does not wander off on its own.  Imagine immense knowledge, stored as coded answers from evolutionary and life experiences. Imagine neural search processes, which constantly locate suitable answers from this lode of experience. But, answers are not always available. The information may not be there in the system.

A Biological Theory Of Motivation 
What Delivers Excellence & Knowledge?
Motivation is limited by neural wisdom. Successful people make millions of choices during the course of their lives. The wisdom in their words, the experiences they remember and even their social choices are all decisions and abilities of the system. Famous actors, statesmen and business leaders have access to the crucial physical and mental knowledge, which supports quick and effective decisions. Those choices carry them to the top.

The legendary management guide Peter Drucker defined excellence as the ability to easily do something, which others find difficult. The easy intuitive availability of answers is crucial in the motivation of successful people. When a person appears to lack motivation in a job, the real problem may also be an inability to locate suitable answers. He lacks the crucial insights and motor skills. Wiser decision making processes constitute one aspect of increased motivation. Such knowledge is the key to work flow.

A Biological Theory Of Motivation – 
What Is The Concept Of "Flow?"
At its highest level, motivation achieves flow. Flow is a state of mind, where people become totally immersed in their tasks and lose all sense of time. It is a state, where people work for the pure enjoyment of completing the task and not for any external reward. The solution of problems is in itself, a reward. Professor Wolfram Schultz discovered that reward oriented behavior is promoted by the release of a group of neurotransmitters by neurons in the early reptilian part of the human brain.

These neurons detect signals in the environment, which indicate the possibility of a reward within a specific time frame. By releasing dopamine, these neurons increase neural activity in the forebrain, mainly in the prefrontal regions, where attention and analysis take place. Schultz noted that the release continues only for the predicted time period, when a reward can be expected. The release reduces at the end of this period. The releases stop if the rewards have become a matter of routine. Novelty is essential for sustained interest.

The solution of each new problem, however simple, provides a reward. Dopamine increases alertness and provides clarity to immediate objectives and makes a person feel more energetic and elated. Research has shown that people achieve flow, when they feel that they are in control of tasks, which are goal directed, provide feedback and give them a sense of meaning. Studies indicate that flow does not require engagement in creative, or artistic tasks. Flow has been shown to be experienced even in tasks such as analyzing data, or filling out income tax returns. Flow occurs, because the system is rewarded with swift answers in the challenges of the job.

A Biological Theory Of Motivation – 
What Is The Effect Of Persistent Emotions?
Persistence is another aspect of motivation. Some people are said to be motivated, when they complete a job with speed and excellence. There are others, who bring extra-ordinary energy to a job. Energy results, when a person strikes harder as well as when he persists in his effort. Persistence is the result of a single minded focus, where an individual keeps after a single objective, regardless of setbacks. Such objectives are set by strong  emotions .

Varying emotions are triggered by specific organs, developed by nature over millions of years. Each subsystem triggers signals, which enable the achievement of a specific objective. A reptilian system initiates signals, which act to satisfy hunger and thirst. Anger and fear signals from the amygdala generate fight, or flight responses. The insula generates emotions like guilt and love, which act to support social cohesion.

Myriad competing emotions offer as many objectives to the system. Imagine an  intuitive decision making  process, which chooses the most powerful emotion as the current motor control option. When a specific emotional signal is strong and persistent, the system focuses on the objective of that emotion. The process causes people to become emotionally motivated.

A Biological Theory Of Motivation – 
What Is The Effect Of Neural Plasticity & LTP?
The amygdala dispatches fight, or flight responses to avoid pain. Love and compassion are emotions, which sense the pain of others. Jealousy and envy are emotions, which feel the pain of failure, when confronted by competition, or failure. The amygdala triggers avoidance behaviors, which seek to lessen pain. The amygdala also remembers. Neural plasticity and long term potentiation (LTP) are neural phenomena, which set off “speed dial circuits” which make the amygdala persist with its fight or flight signals.

Speed dial circuits are created in the organ by particularly painful experiences, or when a person dwells repeatedly on memories of painful events. The system focuses persistently on the objectives of the dominant emotion, which could be fear, anger, compassion, or envy. The system returns from any diversion to a single goal, which seeks to avoid the remembered pain of these emotions. When these emotions lead to positive results, people are said to be dedicated. When they lead to antisocial results, people are called fanatics.

A Biological Theory Of Motivation 
How Does Pleasure Contribute?
The potential for pleasure motivates. The feeling of pleasure had been shown to be located in the septal areas of the brain for rats. The animals were observed when they were able to self stimulate themselves, by pressing a lever, through electrodes implanted in the septal area. They continued pressing the lever till they were exhausted, preferring the effect of stimulation to normally pleasurable activities such as consuming food. For human beings, the highest pleasure is a sense of fulfilment in their careers. Such a sense of fulfilment varies between people.

Different things please different people. While one is thrilled by the sound of music, another delights in the exploration of history. Not everyone is lucky enough to be employed in a field which grants them a true sense of fulfilment. A talented musician may not enjoy bagging grocery. While people can seek employment in agreeable fields, the majority of people can only seek an adequate income, which can bring them joy in their favored fields. Money can also be a powerful motivator.

A Biological Theory Of Motivation – 
Do Some People Lack Motivation?
The characteristics of motivation are preset in the nervous system. Some people have great skills and talents. Others inherit, or subconsciously modulate neural circuits, which make them loving and compassionate. Still others find immense pleasure in the products and services, which their jobs provide to people. Society praises such people as being motivated.

The large majority of people are not so fortunate. They choose a career by accident. They pay little conscious attention to their work, which is usually a matter of unconscious habit. Such people have a few  options  to become more motivated. They can evaluate their own strengths and weaknesses and choose a career, which appeals to their passions, or where they can be excellent. They can learn on the job and bring excellence through continuous study and practice.

A Biological Theory Of Motivation – 
What Is The Intuition Theory?
The neural network is a biological system. It carries within it vast inherited and acquired knowledge. An intuitive process, which makes instant contextual decisions from available knowledge powers the activities of the mind. The Intuition Theory holds that, when this process is supported by the stimulus of talent, pleasure, passion, or learned ability, motivation is increased.

This page was last updated on 28-Jan-2016.

KNOW YOURSELF PODCAST  L isten each week, to one podcast. Based on practical self improvement principles. From the insight of an engineer, back in 1989, about the data processing structure of the human mind, recognizing and filtering patterns, without stopping. Storing patterns of data. Of guilt, shame, fear.  About silencing painful subconscious patterns, becoming self aware, strengthening common sense.   ON YouTube  Can Artificial Intelligence Replace Humans?   Mind Control Tips    Can


6 Answers 6

Some back of the envelope calculations :

number of neurons in AI systems

The number of neurons in AI systems is a little tricky to calculate, Neural Networks and Deep Learning are 2 current AI systems as you call them, specifics are hard to come by (If someone has them please share), but data on parameters do exist, parameters are more analogous to synapses (connections) than neurons (the nodes in between connections) somewhere in the range of 100-160 billion is the current upper number for specialized networks.

Deriving the number of neurons in AI systems from this number is a stretch since these AIs emulate certain types of connections and sub assemblies of neurons, but let's continue.

equal those of the human brain?

So now let's look at the brain, and again this are all contested numbers. Number of neurons

86 Billion, Number of Synapses

150 Trillion, another generalization: average number of synapses per neuron

So now we have something to compare, and I can't stress this enough, these are all wonky numbers, so let's make our life a little easier and divide :

Number of Synapses (Brain ) : 150 trillion / Number of parameters AIs : 150 billion = 1,000 or in other words current AIs would have to scale by a factor of one thousand their connections to be on par with the brain.

Number of Neurons (Brain ) : 86 Billion / Number of Neurons AIs ( 150 billion / 1,744 ) = 86 Million equivalent AI Neurons

Which makes sense, mathematically at least : you can multiply the factor ( 1000 ) times the current number of equivalent AI Neurons ( 86 million) to get the number of neurons in the human brain (86 Billion)

Well,let's use moore's law ( number of transistors processing power doubles about every 2 years ) as a rough measure of technological progress:


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Abstract

Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, “Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?” For another example, “How much of a neuron’s observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?” However, a neuron’s theoretical firing rate is not necessarily well-defined. Consequently, neuroscientific questions involving the theoretical firing rate do not have a meaning in isolation but can only be interpreted in light of additional statistical modeling choices. Ignoring this ambiguity can lead to inconsistent reasoning or wayward conclusions. We illustrate these issues with examples drawn from the neural-coding literature.


CONCLUSION

Mirror neurons are a fascinating class of cells that deserve to be thoroughly investigated in the monkey, and explored systematically for possible homologues in humans. The early hypothesis that these cells underlie action understanding is likewise an interesting and prima facie reasonable idea. However, despite its widespread acceptance, the proposal has never been adequately tested in monkeys, and in humans there is strong empirical evidence, in the form of physiological and neuropsychological (double) dissociations, against the claim.

Why does the hypothesis remain prominent, indeed all but accepted as fact, despite solid evidence to the contrary? I suggest that Pillsbury was right. Motor theories are simple and easy to understand: “we understand action because the motor representation of that action is activated in our brain” (Rizzolatti et al., 2001, p. 661). We see someone pouring liquid from a bottle into a glass this activates a motor representation associated with our own liquid-pouring experiences, and voilà, we have understanding. But scratch the surface of action understanding and it is immediately clear that the problem is not that simple (Pinker, 1989, 2007). For example, the motor act of pouring liquid from a bottle into a glass could be understood as pouring, filling, emptying, tipping, rotating, inverting, spilling (if the liquid missed its mark), defying/ignoring/rebelling (if the pourer was instructed not to pour), and so on. A motor representation cannot distinguish between the range of possible meanings associated with such an action. A mirror neuron theorist might protest that it is the goal or intention that is coded by mirror neurons, not the specific actions (Fogassi et al., 2005). But a goal, say to fill a glass with water, can be accomplished with any number of individual actions or sequence of actions: pouring from a pitcher, turning a spigot, dipping the glass in a lake, setting the glass in the rain, positioning an array of leaves to collect and funnel dew into the glass, digging a well and pumping water into the glass, or even commanding someone else to do any of these! Given the range of meanings associated with a specific action and the range of actions that can achieve a specific goal, there must be a clear distinction between goals and the motor routines that are implemented in a given circumstance to achieve those goals. If mirror neurons are reflecting goals and not actions, then a statement about mirror neurons such as, “we understand action because the motor representation of that action is activated in our brain” (Rizzolatti et al., 2001, p. 661) is either false because mirror neurons do not code actions, or it is false because motor representations are not the basis of action understanding.

Unfortunately, more than 10 years after their discovery, little progress has been made in understanding the function of mirror neurons. I submit that this is a direct result of an overemphasis on the action understanding theory, which has distracted the field away from investigating other possible (and potentially equally important) functions.


Biological Rhythms

Circadian, infradian and ultradian and the difference between these rhythms

The physiological processes of living organisms follow repetitive cyclical variations over certain periods of time. These bodily rhythms have implications for behavior, emotion and mental processes.

There are 3 types of bodily rhythms:

  1. Circadian rhythms: follow a 24-hour cycle: e.g. the sleep-waking cycle
  2. Ultradian rhythms: occur more than once a day: e.g. the cycles of REM and NREM sleep in a single night’s sleep
  3. Infradian rhythms: occur less than once a day: e.g. menstruation (monthly) or hibernation (yearly)

All bodily rhythms are controlled by an interaction of:

  1. Endogenous pacemakers (EP’s). Internal biological structures that control and regulate the rhythm.
  2. Exogenous zeitgebers (time givers) (EZ’s). External environmental factors that influence the rhythm.
  • CIRCADIAN RHYTHMS
  • Heart rate, metabolic rate, breathing rate and body temperature all reach maximum values in the late afternoon/early evening and minimum values in the early hours of the morning. If we reverse our sleep-waking pattern these rhythms persist. This indicates human bodies are evolved for activity in the day and rest at night and, indeed, being nocturnal or disrupting the circadian cycle is highly stressful and physiologically and psychologically harmful.
  • The EP controlling the sleep-waking cycle is located in the hypothalamus. Patterns of light and darkness are registered by the retina, travel up the optic nerves to where these nerves join (optic chiasma), and then pass into the superchiasmatic nucleus (SCN) of the hypothalamus. If this nerve connection is severed circadian rhythms become random. The same effect is produced by damaging the SCN of rats, and people born without eyes cannot regulate bodily rhythms.
  • Ralph bred a group of hamsters to follow a (shortened) 20-hour circadian cycle. SCN cells were removed and transplanted into the brains of rat foetuses with normal rhythms. Once born, these rats adopted a 20-hour cycle. Their brains were then transplanted with SCN cells from 24-hour cycle hamsters and within a week their cycles had adopted this new 24 cycle.
  • When cells from the SCN were removed from rats the 24-hour cycle of neural activity persisted in the isolated cells. Recent research by Yakazaki found that isolated lungs and livers, and other tissues grown in a lab still persist in showing circadian rhythms. This suggests cells are capable of maintaining a circadian rhythm even when they are not under the control of any brain structures and that most bodily cells are tuned in to following a daily circadian rhythm.
  • All of this evidence points to the fact that circadian rhythms are primarily controlled by evolutionarily-determined, biological structures that exert a strong influence on us to maintain normal sleep-waking patterns.
  • However, circadian rhythms are also influenced by EZ’s - ‘cues’ in the environment- about what time of day or night it is. In 1975 Siffre spent 6 months underground in an environment completely cut off from all EZ’s. Although he organised his time in regular patterns of sleeping and waking his body seemed to have a preference for a 25 hour rather than a 24-hour cycle. This implies that circadian rhythms are mainly controlled by EP’s rather than EZ’s.
  • Another piece of evidence in support of this idea is that Innuit Indians who live in the Arctic Circle inhabit an environment that has hardly any darkness in summer and hardly any light in winter. If the sleep-waking cycle was primarily controlled by EZ’s they would tend to sleep a huge amount in winter and hardly at all in summer. However, this is not the case - they maintain a fairly regular pattern of sleeping and waking all year around.

  • ULTRADIAN RHYTHMS
  • With the development of certain scientific equipment, it became possible to study sleep more objectively.

• The electroencephalogram (EEG) measures electrical brain activity.

• The electrooculogram (EOG) measures eye movement.

• New-born - 16 hours’ sleep, 50% REM (patterns of REM are observed in foetuses).

• 3-year-old - 12 hours’ sleep, 25% REM.

• Adult - 8 hours’ sleep, 22% REM.

The effect of endogenous pacemakers and exogenous zeitgebers on the sleep/wake cycle

Circadian rhythms follow a 24-hour cycle (e.g. the sleep-waking cycle) and are controlled by an interaction of:

  1. Endogenous pacemakers (EP’s). Internal biological structures that control and regulate the rhythm.
  2. Exogenous zeitgebers (time-givers) (EZ’s). External environmental factors that influence the rhythm.

The EP controlling the sleep-waking cycle is located in the hypothalamus. Patterns of light and darkness are registered by the retina, travel up the optic nerves to where these nerves join (optic chiasma), and then pass into the suprachiasmatic nucleus (SCN) of the hypothalamus. If this nerve connection is severed circadian rhythms become random. The same effect is produced by damaging the SCN of rats, and people born without eyes cannot regulate bodily rhythms.

However, circadian rhythms are also influenced by EZ’s - ‘cues’ in the environment - about what time of day or night it is. Siffre spent 6 months underground in an environment completely cut off from all EZ’s. Although he organised his time in regular patterns of sleeping and waking his body seemed to have a preference for a 25 hour rather than a 24-hour cycle. This implies that circadian rhythms are mainly controlled by EP’s rather than EZ’s.

Another piece of evidence in support of this idea is that Innuit Indians who live in the Arctic Circle inhabit an environment that has hardly any darkness in summer and hardly any light in winter. If the sleep-waking cycle was primarily controlled by EZ’s they would tend to sleep a huge amount in winter and hardly at all in summer. However, this is not the case- they maintain a fairly regular pattern of sleeping and waking all year around.

Disruption of the circadian sleep-waking cycle (e.g. jet lag and shift work) has been shown to cause negative physical and psychological effects.

Jet Lag occurs when we cross several world time zones quickly. Circadian rhythms will be disrupted as although our endogenous pacemakers stay the same, the exogenous zeitgebers (patterns of light and dark in the new environment) have changed.


6 Answers 6

Some back of the envelope calculations :

number of neurons in AI systems

The number of neurons in AI systems is a little tricky to calculate, Neural Networks and Deep Learning are 2 current AI systems as you call them, specifics are hard to come by (If someone has them please share), but data on parameters do exist, parameters are more analogous to synapses (connections) than neurons (the nodes in between connections) somewhere in the range of 100-160 billion is the current upper number for specialized networks.

Deriving the number of neurons in AI systems from this number is a stretch since these AIs emulate certain types of connections and sub assemblies of neurons, but let's continue.

equal those of the human brain?

So now let's look at the brain, and again this are all contested numbers. Number of neurons

86 Billion, Number of Synapses

150 Trillion, another generalization: average number of synapses per neuron

So now we have something to compare, and I can't stress this enough, these are all wonky numbers, so let's make our life a little easier and divide :

Number of Synapses (Brain ) : 150 trillion / Number of parameters AIs : 150 billion = 1,000 or in other words current AIs would have to scale by a factor of one thousand their connections to be on par with the brain.

Number of Neurons (Brain ) : 86 Billion / Number of Neurons AIs ( 150 billion / 1,744 ) = 86 Million equivalent AI Neurons

Which makes sense, mathematically at least : you can multiply the factor ( 1000 ) times the current number of equivalent AI Neurons ( 86 million) to get the number of neurons in the human brain (86 Billion)

Well,let's use moore's law ( number of transistors processing power doubles about every 2 years ) as a rough measure of technological progress:


A Biological Theory Of Motivation

This biological theory of motivation (The Intuition Theory), suggests that motivation levels are regulated by neural pattern recognition events. Subconscious drives impel people to achieve excellence, or to spend exceptional energies on services to humanity. There have been five well known theories of motivation, which seek to explain the reasons why a few people spend more energy than others to achieve their goals. All these theories essentially outline the crucial impact of neural activities on motivation.

The Instinct Theory suggests that motivated behavior is a biological instinct. The Drive Reduction Theory suggests that motivated behavior seeks to reduce the tension of drives triggered by sensations such as hunger or pain. The Arousal Theory suggests that motivated behavior is the result of a search for an optimum level of arousal.

The Psychoanalytic Theory suggests that motivated behaviors follow fundamental drives to survive and avoid death. The Humanistic Theory presents the Maslow Hierarchy, where people strive to achieve their maximum potential. Instinctual responses, drive reduction, arousal, psychological and humanistic drives are the varied aspects of the powerful neural drives, which ultimately motivate people. The Intuition Theory suggests that these drives are powered by the intuitive choices of the mind.

  • Intuition focuses the nervous system on an activity. Wisdom, or emotions decide.
  • Strategic drives use coded knowledge to achieve objectives.
  • Motivation is limited by neural wisdom. Excellence is delivered by wider knowledge and skills.
  • Excellence results in flow.
  • When emotions dominate, an individual will persist in the task.
  • Speed dial circuits, created by painful experiences, focus people on specific goals.
  • Different people are motivated by different rewards.
  • Many people are not fortunate enough to work on rewarding careers.
  • The Intuition Theory suggests that inner wisdom and emotions motivate the system.

Can An Algorithm Be Controlling The Mind?
I am not a physician, but an engineer. Way back in 1989, I catalogued how the ELIMINATIO N approach of an AI Expert System ਌ould reveal a way by which the nervous system could store and retrieve astronomically large memories.  That insight is central to the six unique new premises presented in this website. 

These new premises could explain an enigma.   A physician is aware of thousands of diseases and their related symptoms.  How does he  note a symptom and focus on a single disease  in less than half a second?   How ਌ould he identify Disease X out of 8000 diseases with just a glance?  

First, the total born and learned knowledge available to the doctor could not exist anywhere other than as the stored/retrieved data within the 100 billion neurons in his brain.  The perceptions, sensations, feelings and physical activities of the doctor could only be enabled by the electrical impulses flowing through the axons of those neurons.  The data enabling that process could be stored as digital combinations.

Second, combinatorial decisions of neurons cannot be made by any entity other than the axon hillock, which decides the axonal output of each neuron.  The hillock receives hundreds of inputs from other neurons.  Each hillock makes the pivotal neuronal decision about received inputs within 5 milliseconds.  A xon hillocks could be storing digital combinations.    It could be adding each new incoming digital combination to its memory store.    The hillock could fire impulses, if it matched a stored combination. If not, it could inhibit further impulses.  Using stored digital data to make decisions about incoming messages could make the axon hillocks intelligent.

Third, combinations are reported to enable a powerful coding mode for axon hillocks.  Olfactory combinatorial data is known (Nobel Prize 2004) to store memories for millions of smells.  Each one of 100 billion axon hillocks have around a 1000 links  to other neurons.  The hillocks can mathematically store more combinations than there are stars in the sky. Each new digital combination could be adding a new relationship link.  In this infinite store, specific axon hillocks could be storing all the symptom = disease (S=D) links known to the doctor as digital combinations.

Fourth, instant communication is possible in the nervous system.  Within five steps, information in one hillock can reach all other relevant neurons.  Just 20 Ms for global awareness.  Within the instant the doctor observes a symptom,  feedback and feed forward links could inform every S=D link of the presence of the symptom.  Only the S=D link of Disease X could be recalling the combination and recognizing the symptom.

Fifth, on not recognizing the symptom, all other S=D hillocks could be instantly inhibiting their impulses. The S=D links of Disease X could be continuing to fire. Those firing S=D link would be recalling past complaints, treatments and signs of Disease X, confirming the diagnosis.  This could be enabling axon hillocks to identify Disease X out of 8000 in milliseconds.

W orldwide interest in this website is acknowledging its rationale. Not metaphysical theories, but processing of digital memories in axon hillocks could be explaining innumerable mysteries of the mind.  Over three decades, this website has been assembling more and more evidence of the manipulation of emotional and physical behaviors by narrowly focused digital pattern recognition.  It has also been receiving over 2 million page views from over 150 countries.

A Biological Theory Of Motivation 
What are the Engines of Motivation?
The choices a person makes in life are determined by the options available within his mind. Imagine a system, which runs through millions of possibilities to make each choice.  Imagine intuition, an algorithmic process, which enables the nervous system to deliver swift decisions. Animals cannot afford to freeze into immobility, unable to decide between chewing grass and drinking water. If the choice is to chew grass, the drive to quench thirst is instantly inhibited. 

Imagine  intuition ਊs a pattern recognition process.  Intuition  eliminates unfit possibilities within milliseconds to choose a single option for action. When an intuitively driven system knows the answers, actions flow with effortless energy. When answers are lacking, the system fumbles. In more complex situations, emotions guide system strategies. When emotions dominate, the system acts with passion for good or evil.

A Biological Theory Of Motivation 
What Are Neural Drives?
Since solutions are often not immediately available, neural drives constantly seek answers to problems faced by the system. Imagine purpose driven neural drives.   The human mind has immense knowledge, stored as coded answers from myriad evolutionary and real life experiences.   When you decide to move a piece on a chess board, sequences of motor impulses persist from the instant your hand picks up the piece, till it is set down in its new position. Muscle movements are sequences of micro-managed contractions, which last just milliseconds. Each signal invokes only a tiny contraction.

Myriad muscles contract and relax over thousands of cycles till your chess piece reaches its desired position. The motor codes continually issue precise instructions to meet a set objective. Your hand does not wander off on its own.  Imagine immense knowledge, stored as coded answers from evolutionary and life experiences. Imagine neural search processes, which constantly locate suitable answers from this lode of experience. But, answers are not always available. The information may not be there in the system.

A Biological Theory Of Motivation 
What Delivers Excellence & Knowledge?
Motivation is limited by neural wisdom. Successful people make millions of choices during the course of their lives. The wisdom in their words, the experiences they remember and even their social choices are all decisions and abilities of the system. Famous actors, statesmen and business leaders have access to the crucial physical and mental knowledge, which supports quick and effective decisions. Those choices carry them to the top.

The legendary management guide Peter Drucker defined excellence as the ability to easily do something, which others find difficult. The easy intuitive availability of answers is crucial in the motivation of successful people. When a person appears to lack motivation in a job, the real problem may also be an inability to locate suitable answers. He lacks the crucial insights and motor skills. Wiser decision making processes constitute one aspect of increased motivation. Such knowledge is the key to work flow.

A Biological Theory Of Motivation – 
What Is The Concept Of "Flow?"
At its highest level, motivation achieves flow. Flow is a state of mind, where people become totally immersed in their tasks and lose all sense of time. It is a state, where people work for the pure enjoyment of completing the task and not for any external reward. The solution of problems is in itself, a reward. Professor Wolfram Schultz discovered that reward oriented behavior is promoted by the release of a group of neurotransmitters by neurons in the early reptilian part of the human brain.

These neurons detect signals in the environment, which indicate the possibility of a reward within a specific time frame. By releasing dopamine, these neurons increase neural activity in the forebrain, mainly in the prefrontal regions, where attention and analysis take place. Schultz noted that the release continues only for the predicted time period, when a reward can be expected. The release reduces at the end of this period. The releases stop if the rewards have become a matter of routine. Novelty is essential for sustained interest.

The solution of each new problem, however simple, provides a reward. Dopamine increases alertness and provides clarity to immediate objectives and makes a person feel more energetic and elated. Research has shown that people achieve flow, when they feel that they are in control of tasks, which are goal directed, provide feedback and give them a sense of meaning. Studies indicate that flow does not require engagement in creative, or artistic tasks. Flow has been shown to be experienced even in tasks such as analyzing data, or filling out income tax returns. Flow occurs, because the system is rewarded with swift answers in the challenges of the job.

A Biological Theory Of Motivation – 
What Is The Effect Of Persistent Emotions?
Persistence is another aspect of motivation. Some people are said to be motivated, when they complete a job with speed and excellence. There are others, who bring extra-ordinary energy to a job. Energy results, when a person strikes harder as well as when he persists in his effort. Persistence is the result of a single minded focus, where an individual keeps after a single objective, regardless of setbacks. Such objectives are set by strong  emotions .

Varying emotions are triggered by specific organs, developed by nature over millions of years. Each subsystem triggers signals, which enable the achievement of a specific objective. A reptilian system initiates signals, which act to satisfy hunger and thirst. Anger and fear signals from the amygdala generate fight, or flight responses. The insula generates emotions like guilt and love, which act to support social cohesion.

Myriad competing emotions offer as many objectives to the system. Imagine an  intuitive decision making  process, which chooses the most powerful emotion as the current motor control option. When a specific emotional signal is strong and persistent, the system focuses on the objective of that emotion. The process causes people to become emotionally motivated.

A Biological Theory Of Motivation – 
What Is The Effect Of Neural Plasticity & LTP?
The amygdala dispatches fight, or flight responses to avoid pain. Love and compassion are emotions, which sense the pain of others. Jealousy and envy are emotions, which feel the pain of failure, when confronted by competition, or failure. The amygdala triggers avoidance behaviors, which seek to lessen pain. The amygdala also remembers. Neural plasticity and long term potentiation (LTP) are neural phenomena, which set off “speed dial circuits” which make the amygdala persist with its fight or flight signals.

Speed dial circuits are created in the organ by particularly painful experiences, or when a person dwells repeatedly on memories of painful events. The system focuses persistently on the objectives of the dominant emotion, which could be fear, anger, compassion, or envy. The system returns from any diversion to a single goal, which seeks to avoid the remembered pain of these emotions. When these emotions lead to positive results, people are said to be dedicated. When they lead to antisocial results, people are called fanatics.

A Biological Theory Of Motivation 
How Does Pleasure Contribute?
The potential for pleasure motivates. The feeling of pleasure had been shown to be located in the septal areas of the brain for rats. The animals were observed when they were able to self stimulate themselves, by pressing a lever, through electrodes implanted in the septal area. They continued pressing the lever till they were exhausted, preferring the effect of stimulation to normally pleasurable activities such as consuming food. For human beings, the highest pleasure is a sense of fulfilment in their careers. Such a sense of fulfilment varies between people.

Different things please different people. While one is thrilled by the sound of music, another delights in the exploration of history. Not everyone is lucky enough to be employed in a field which grants them a true sense of fulfilment. A talented musician may not enjoy bagging grocery. While people can seek employment in agreeable fields, the majority of people can only seek an adequate income, which can bring them joy in their favored fields. Money can also be a powerful motivator.

A Biological Theory Of Motivation – 
Do Some People Lack Motivation?
The characteristics of motivation are preset in the nervous system. Some people have great skills and talents. Others inherit, or subconsciously modulate neural circuits, which make them loving and compassionate. Still others find immense pleasure in the products and services, which their jobs provide to people. Society praises such people as being motivated.

The large majority of people are not so fortunate. They choose a career by accident. They pay little conscious attention to their work, which is usually a matter of unconscious habit. Such people have a few  options  to become more motivated. They can evaluate their own strengths and weaknesses and choose a career, which appeals to their passions, or where they can be excellent. They can learn on the job and bring excellence through continuous study and practice.

A Biological Theory Of Motivation – 
What Is The Intuition Theory?
The neural network is a biological system. It carries within it vast inherited and acquired knowledge. An intuitive process, which makes instant contextual decisions from available knowledge powers the activities of the mind. The Intuition Theory holds that, when this process is supported by the stimulus of talent, pleasure, passion, or learned ability, motivation is increased.

This page was last updated on 28-Jan-2016.

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Neuromorphic Computing: Modeling The Brain

Competing models vie to show how the brain works, but none is perfect.

Can you tell the difference between a pedestrian and a bicycle? How about between a skunk and a black and white cat? Or between your neighbor’s dog and a colt or fawn? Of course you can, and you probably can do that without much conscious thought. Humans are very good at interpreting the world around them, both visually and through other sensory input.

Computers are not. Though their sheer calculation speed surpassed that of human “calculators” long ago, large data centers equipped with terabyte-scale databases are only beginning to match the image recognition capabilities of an average human child.

Meanwhile, humans are creating larger and more complex digital archives and asking more complex questions about them. How do you find the photo you want in a collection of thousands? How does a music service answer a customer request for “more like this?” How can computers support technical decision making when the source data is often noisy and ambiguous?

Neuromorphic computing seeks to build systems informed by the architecture of biological brains. Such systems have the potential to analyze data sets more rapidly, more accurately, and with fewer computing resources than conventional analysis.

In the current state of the art, people who discuss neuromorphic computing and big data analysis are usually talking about neural networks. While current-generation neural networks are important for practical problem solving and will be discussed in a future article, they don’t really have much resemblance to biological brains.


Fig. 1: Neuron cell diagram. Source: Wikimedia Commons.

How neurons work
The first important difference is the sheer scale of connectivity in biological brains. The nucleus of a nerve cell is at the center of a web of fibers, or axons, each of which branches into potentially thousands of dendrites. Each dendrite can connect to a neighboring neuron across a junction known as a synapse. Though electronic analogues often define this web of connections as fixed, it is not. Synaptic connections are made and broken constantly. As Jeff Hawkins, co-founder of Numenta, explained in a talk at the 2015 IEEE Electron Device Meeting, “[Biological] memory is a wiring problem, not a storage problem,” and a large wiring problem at that.

In the human neocortex—responsible for functions like sensory perception, spatial reasoning, and language—there are millions of neurons, each of which may communicate with thousands of neighbors. The neocortex alone has billions of connections. The brain as a whole has trillions. For comparison, the largest server-based neural networks have about 11 billion connections.

Furthermore, the brain is an analog system. Transistors in electronic circuits are either on or off. Memory elements store either 1 or 0. Synaptic connections are not directly equivalent to memory capacitors, but they can be strong or weak, and can be reinforced or depressed in response to stimuli.

More precisely, neurons communicate through electrical currents resulting from the flow of sodium and potassium ions. There are differences in ion concentrations between intracellular and extracellular fluids. When a pre-synaptic neuron releases a neurotransmitter compound, the ion channels in the post-synaptic neuron are either excited or depressed, increasing or decreasing the flow of ions between the cell and the extracellular fluid. Doo Seok Jeong, senior scientist at the Korea Institute of Science and Technology, explained that the cell membrane of the post-synaptic neuron acts as a capacitor. Ions accumulate until a critical threshold is reached, at which point a “synaptic current” spike propagates along the neural fibers to other synapses and other neurons.

The capacitor will charge and discharge repeatedly until the neurotransmitter concentration dissipates, so the synaptic current actually consists of a chain of related spikes. The length of the chain and the frequency of individual spikes depends on the original stimulus. The response of a particular neuron to a particular synaptic current chain is generally not linear. The relationship between the input and output signals is the “gain” of the neuron.

It must be emphasized, though, that the relationship between external stimuli and synaptic current is not clear. Biological brains produce chains of synaptic current spikes that appear to encode information. But it is not possible to draw a line between the image of “cat” received by the photoreceptors in the retina and a specific pattern of synaptic spikes generated by the visual cortex, much less the positive and negative associations with “catness” that the image might produce elsewhere in the brain. A number of factors, such as the non-uniform cell membrane potential, introduce “noise” into the signal and cause the loss of some information. However, the brain clearly has mechanisms for extracting critical information from noisy data, for discarding irrelevant stimuli, and for accommodating noise-induced data loss. The biological basis for these mechanisms is not known at this time.

Firing synaptic current spikes
In modeling the brain, at least two levels need to be considered. The first is the biological mechanism by which chains of synaptic current are generated and propagated. The second is the role of these spikes in memory and learning. Both levels face a tradeoff between biological accuracy and computational efficiency. For example, many commercial neural networks use a “leaky integrate and fire” (LIF) model to describe the propagation of synaptic spikes. Each neuron has a pre-determined threshold, and will “fire” a synaptic signal to its neighbors when that threshold is exceeded. In electronic networks, similarly, each neuron applies pre-determined weights to input signals to determine the output signal. Rapid determination of the appropriate weights for a particular problem is one of the central challenges of neural network design, but once the weights are known, the output signal is simply the dot product of the input signal with the weight matrix.

This approach is computationally efficient, but not biologically realistic. Among other things, the LIF model ignores the timing of synaptic spikes, and therefore the causal relationship between them. That is, signal “A” may precede or follow signal “B” and the response of biological neurons will depend on both the relative strength and the relative timing of the two signals. A strict LIF model will only recognize whether the combination of the two exceeded the node’s threshold. The biological behavior is analog in nature, while the electronic behavior of conventional neural networks is not.

Two alternatives to the LIF model incorporate additional biophysical pathways, increasing biological realism at the expense of computational efficiency. The spiking neuron model takes into account the cell membrane’s recovery rate—how quickly the membrane potential returns to its nominal value. This model can describe different kinds of neurons, but it preserves computational efficiency by only considering variations in the membrane potential.

A much more sophisticated alternative, the Hodgkin-Huxley model, considers several different biophysical contributions, including membrane potential and the sodium and potassium ion currents. It establishes the dependence between the conductance of ion channels and the membrane potential. Further extensions of the original Hodgkin-Huxley model recognize several different potassium and sodium currents and incorporate neurotransmitters and their receptors. The HH model is substantially more realistic, but also much more computationally complex.

These three models describe the fundamental mechanisms of synaptic current generation and propagation in increasing levels of detail. For the processes we call “thinking,” — memory, learning, analysis — an additional step is required. In biological brains, this is synaptic plasticity, which is the ability of the brain to strengthen and weaken, break and remake synaptic connections. The chains of synaptic spikes provide the input for learning rules, the next level in brain modeling.

From current to data: synaptic plasticity
One of the most basic learning rules—proposed in 1982 by Brown University researchers Elie Bienenstock, Leon Cooper, and Paul Munro (BCM)—expresses synaptic change as a product of the pre-synaptic activity and a nonlinear function of post-synaptic activity. It is expressed in terms of firing rates, and cannot predict timing dependent modification of synapses.

A somewhat more sophisticated model, spike timing-dependent plasticity, recognizes that the relative timing of two signals also matters. Is a positive or negative experience associated with a particular stimulus? How closely? These details affect the relative strengths of synaptic connections. The most basic STDP models compare the timing of pairs of spikes. If the pre-synaptic spike comes before the post-synaptic spike, the connection is enhanced. Otherwise, it is weakened. However, the basic STDP model does not reproduce experimental data as well as the BCM model does.

One proposed modification, a triplet-based STDP learning rule, compares groups of three spikes, rather than pairs. It behaves as a generalized BCM rule in that post-synaptic neurons respond to both input spiking patterns and correlations between input and output spikes. These higher order correlations are ubiquitous in natural stimuli, so it’s not surprising that the triplet rule reproduces experimental data more accurately.

Which of these models and learning rules is the “best” choice largely depends on the situation. Neuroscientists seek to develop models that can accurately reproduce the behavior of biological brains, hoping to gain insight into the biological mechanisms behind human psychology. Neuromorphic computing seeks to use biological mechanisms to inform the architecture of electronic systems, ultimately deriving improved solutions to practical data analysis problems. Reproduction of a specific chain of synaptic spikes or a specific learning behavior is secondary to accuracy and computational efficiency.

Part two of this series will show why the “best” neural networks are not necessarily the ones with the most “brain-like” behavior.

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Old concept gets new attention as device scaling becomes more difficult.


Reflections on Mirror Neurons

In 1992, a team at the University of Parma, Italy discovered what have been termed “mirror neurons” in macaque monkeys: cells that fire both when the monkey took an action (like holding a banana) and saw it performed (when a man held a banana). Giacomo Rizzolati, the celebrated discoverer, will deliver the Keynote Address at the APS Convention in Washington DC, USA, on May 26, 2011, and report on his latest findings. To tide us over until then, here’s a report on the state of mirror neuron science.

Like monkeys, humans have mirror neurons that fire when we both perceive and take an action. Locating the tiny cells means attaching electrodes deep inside the brain. As this has hardly been practical in humans, studies have had to rely on imaging, which shows which areas of the brain “light up” in different circumstances. By last year, a meta-analysis of 139 imaging studies confirmed mirroring activity in parts of the human brain where, in monkeys, mirror neurons are known to reside. Because the lit-up areas contain millions of neurons, for humans most researchers speak of a “mirror system,” rather than mirror cells. Last year, single mirror neurons were recorded in humans for the first time, using in-depth electrodes, in 21 epileptic patients.

The cells showed up unexpectedly in an area known for memory, the medial temporal lobe, as well as in areas where they were expected. The discovery suggests that memory is embedded in our mirror system, says Marco Iacoboni (University of California, Los Angeles), a leading authority in the field and a co-author of the epilepsy study. Perhaps, he says, we form memory “traces” whenever we see or observe an action. “It’s a lovely idea,” says Rizzolati, though he adds that it’s too early to say.

The mirroring system includes a mechanism that helps the brain record the difference between seeing and acting. In the epilepsy study, some neurons fired more during action and others fired more during observation. These same cells, Iacoboni proposes, help us distinguish between the self and others.

That’s an important issue, to say the least. We often confuse our own actions with those of other people. In a study published recently in Psychological Science, Gerald Echterhoff, University of Muenster, Germany, and his co-authors reported that people who had watched a video of someone else doing a simple action — shaking a bottle or shuffling a deck of cards — often mistakenly recalled two weeks later that they had done so themselves. The mistake occurred even when participants were warned that they could mix up other people’s actions with their own. Echterhoff and a co-author, Isabel Lindner, of the University of Cologne, Germany, plan to conduct imaging studies to test if the phenomenon is related to mirroring.

Mirror neurons are present in infant monkeys. Three years ago, the first abstract appeared reporting that surface electrodes had recorded mirroring in monkeys one- to seven-days old as they watched humans stick out their tongues and smack their lips. Says Pier Francesco Ferrari, of the University of Parma, and co-author of an upcoming study, “This is the first evidence that infants have a mirror mechanism at birth that responds to facial gestures. Without any experience of stimulation, they are able to focus their attention on the most relevant stimuli and respond.” Sometimes the days-old monkeys even stuck out their tongues when they saw the human tongue, Ferrari says.

In monkeys, mirror neurons are present in the insula, an emotion center. Despite all the claims linking mirror neurons to empathy, Rizzolatti says he is only now reporting the discovery of a few mirror neurons in the insula in monkeys, “a reservoir for disgust and pain. Many other factors control how we react,” he says, “but mirror neurons are how we recognize an emotion in others neurally.”

Mimicry, linked to mirror neurons, makes monkeys bond. The idea that mimicry helps humans bond is well-accepted, but the first controlled experiment, with a monkey, came last year, Ferrari says. In that study, reported in Science, his team presented monkeys with a token and rewarded them with treats if they returned it. The monkeys had a choice of returning the token to either of two investigators, only one of whom was imitating the monkey. The monkeys consistently chose to return the token to the person who imitated them and spent more time near that investigator.

Mimicry in humans reflects social cues. The idea that we’re primed in one part of our brain to like those who mimic us doesn’t rule out other discriminations. Unconscious mimicry is deeply social and, as such, reflects prejudice, says Rick van Baaren of Radboud University in the Netherlands. In a 2009 overview of the science of mimicry published in the Philosophical Transactions of The Royal Society, he points out that people are more likely to mimic a member of the same ethnic group, less likely to mimic a stigmatized person who is obese or has a scar, and less likely to mimic members of a group we view with prejudice. In fact, humans tend to react badly when mimicked by someone from an “out group.”

The mirror systems of two people can move in tandem. Many researchers had proposed that the brains of two people “resonate” with each other as they interact, with one person’s mirror system reflecting changes in the other. Last spring, the Proceedings of the National Academy of Sciences reported on the brain activity of people playing the game of charades. The observer and gesturer performing the charade did move neurologically in tandem, says co-author Christian Keysers, of the University Medical Center in Groningen, The Netherlands. Keysers says the discovery backs up the idea that mirroring plays a key role in the evolution of language. We’re exquisitely responsive to gestures, he says “Nobody had ever shown that during gestural communication the observer’s mirror system tracks the moment to moment state of the gesturer’s motor system.”

Mirror neurons respond to sound. In monkeys, mirror neurons fire at sounds associated with an action, such as breaking a peanut or tearing paper. Mirroring has been discovered in birds hearing bird song, and in humans. Recent work, led by Emiliano Ricciardi at the University of Pisa, Italy, found that blind people, using their hearing, interpret the actions of others by recruiting the same human mirror system brain areas as sighted people.

Mirror neurons code intentions. Whether mirror neurons register the goal of an action or other higher-level systems must chip in to judge other people’s intentions has been the subject of much debate. The evidence is accumulating that mirror neurons “implement a fairly sophisticated and rather abstract coding of the actions of others,” says Iacoboni. One clue is that while a third of all mirror neurons fire for exactly the same action, either executed or observed, the larger number — about two thirds — fire for actions that achieve the same goal or those that are logically related — for example, first grasping and then bringing an object to the mouth. And these neurons make fine distinctions: When a monkey observed an experimenter grasping an object and pantoming the same action, the neurons fired when the experimenter grasped the object but not during the pantomime. “In academia, there is a lot of politics and we are continuously trying to figure out the ‘real intentions’ of other people,” Iacoboni says. “The mirror system deals with relatively simple intentions: smiling at each other, or making eye contact with the other driver at an intersection.”

Mirroring increases with experience. In the first studies, monkeys mirrored when they saw a person grasping food but not if the person used a tool. That made sense because monkeys don’t use tools. In later research, monkeys did mirror humans using a tool Iacoboni suggests that their brains had “learned,” adjusting to seeing researchers with tools. In humans, more mirroring activity occurs when dancers see other dancers perform routines they know well. Mirroring in blind people is more active in response to more familiar action sounds.

Stimulating the mirror system helps stroke victims. If mirroring develops as we learn, perhaps triggering mirroring can teach. Two studies with stroke victims, for example, have found that stimulating the mirror system helped them recover particular motor actions, says Ferdinand Binkofski at the University of Luebeck, Germany. When stroke victims received “action observation therapy,” in which they observed an action repeatedly, they regained more ability. Compared to a control group, the stroke victims also showed more mirroring in brain scans.

Children with autistic syndromes have mirroring defects. As early as 2001, researchers hypothesized that a deficit in the mirror neuron system could explain some of the problems of autistic patients. As of September, 2010 twenty published papers using brain imaging, magnetoencephalography, electroencephalography, and transcranial magnetic stimulation support this idea, and four failed to support it, according to Iacoboni.

The hope is that basic science in the mirror system could lead to a better understanding of emotional difficulties. As Ferrari points out, some infant monkeys separated from their mothers show “symptoms like those in autistic kids. You see them rocking and avoiding your gaze.” Others develop normally. Ferrari and his colleagues plan to follow the infants they studied and measure whether strong mirror neuron activity in the first week of life indicates sociability later on. “We hope to create a picture of how brain activity interacts with the social environment to put some monkeys more at risk,” he says. “The obvious direction is to translate this to humans.”

Mirror neuron research continues to grow fast, across disciplines. Already the number of items produced by a PubMed search, for example, increased twenty-fold between 2000 and 2010, although that number only doubled for “Stroop and brain,” another popular topic. The ongoing technical challenge remains: Mirror neurons are not the majority of cells in the brain areas where they are located, so it is still difficult to pinpoint their role when those areas show spiking activity. Iacoboni suggests that mathematical modeling will help make more of this data useful. Such modeling allowed Keysers, for example, to establish the existence of resonance in the charades study. So what can we expect next? Most likely, Iacoboni, says, more work with depth electrodes in neurological patients and studies like Ferrari’s to test whether mirroring is a biomarker of sociality. A promising underexplored subject is the inhibitors that keep us from mimicking (but fail recovering addicts who relapse when they see others consume). Behind all this work will be a growing consensus that mirror neurons evolved in humans so we could learn from observation and communication. œ


Silicon neuron: digital hardware implementation of the quartic model

This paper presents an FPGA implementation of the quartic neuron model. This approach uses digital computation to emulate individual neuron behavior. We implemented the neuron model using fixed-point arithmetic operation. The neuron model’s computations are performed in arithmetic pipelines. It was designed in VHDL language and simulated prior to mapping in the FPGA. We show that the proposed FPGA implementation of the quartic neuron model can emulate the electrophysiological activities in various types of cortical neurons and is capable of producing a variety of different behaviors, with diversity similar to that of neuronal cells. The neuron family of this digital neuron can be modified by appropriately adjusting the neuron model’s parameters.

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Watch the video: Sind wir allein im Universum? Live im Hörsaal. Harald Lesch (May 2022).