How do developing neurons become tuned to fire only on receiving specific inputs?

How do developing neurons become tuned to fire only on receiving specific inputs?

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Research has uncovered neurons that seem to "listen" for specific inputs and fire only when these inputs are received. For example, neurons in the visual cortex may fire at a higher rate when visual stimuli of a particular orientation is presented, and may not fire for stimuli of other orientations.

Are there any good models on how neurons develop this tuning for specific inputs? Could anyone provide links to good papers or other resources?

The only "good model" I know of is hebbian plasticity* or hebbian theory, here's a link:

Usually what we mean by neurons "listen for" or are "tuned for" or even "like" a particular stimulus is that spiking activity in those neurons is significantly increased (or decreased) in the presence of that stimulus as compared to equal amount of time in the absence of it.

*Plasticity refers to the capacity of the brain to change both the strength of connection as well as number and configuration of connections.

Becoming a new neuron in the adult olfactory bulb

New neurons are continually recruited throughout adulthood in certain regions of the adult mammalian brain. How these cells mature and integrate into preexisting functional circuits remains unknown. Here we describe the physiological properties of newborn olfactory bulb interneurons at five different stages of their maturation in adult mice. Patch-clamp recordings were obtained from tangentially and radially migrating young neurons and from neurons in three subsequent maturation stages. Tangentially migrating neurons expressed extrasynaptic GABAA receptors and then AMPA receptors, before NMDA receptors appeared in radially migrating neurons. Spontaneous synaptic activity emerged soon after migration was complete, and spiking activity was the last characteristic to be acquired. This delayed excitability is unique to cells born in the adult and may protect circuits from uncontrolled neurotransmitter release and neural network disruption. Our results show that newly born cells recruited into the olfactory bulb become neurons, and a unique sequence of events leads to their functional integration.


Diverse ion channels and their dynamics endow single neurons with complex biophysical properties. These properties determine the heterogeneity of cell types that make up the brain, as constituents of neural circuits tuned to perform highly specific computations. How do biophysical properties of single neurons impact network function? We study a set of biophysical properties that emerge in cortical neurons during the first week of development, eventually allowing these neurons to adaptively scale the gain of their response to the amplitude of the fluctuations they encounter. During the same time period, these same neurons participate in large-scale waves of spontaneously generated electrical activity. We investigate the potential role of experimentally observed changes in intrinsic neuronal properties in determining the ability of cortical networks to propagate waves of activity. We show that such changes can strongly affect the ability of multi-layered feedforward networks to represent and transmit information on multiple timescales. With properties modeled on those observed at early stages of development, neurons are relatively insensitive to rapid fluctuations and tend to fire synchronously in response to wave-like events of large amplitude. Following developmental changes in voltage-dependent conductances, these same neurons become efficient encoders of fast input fluctuations over few layers, but lose the ability to transmit slower, population-wide input variations across many layers. Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission. The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity. This work underscores the significance of simple changes in conductance parameters in governing how neurons represent and propagate information, and suggests a role for background synaptic noise in switching the mode of information transmission.


At this level of analysis, questions concern the physiological machinery underlying an animal’s behaviour. Behaviour is explained in terms of the firings of the neural circuits between reception of the stimuli (sensory input) and movements of the muscles (motor output). Consider, for example, a worker honeybee (Apis mellifera) flying back to her hive from a field of flowers several kilometres away. The sensory processes the bee employs, the neural computations she performs, and the patterns of muscular activity she uses to make her way home constitute some of the mechanisms underlying the insect’s impressive feat of homing. In the course of exploring these mechanisms and those underlying other forms of animal behaviour, physiologists have learned an important lesson regarding the mechanisms underlying behaviour: they are special-purpose adaptations tailored to the particular problems faced by an animal, but they are not all-purpose solutions to general problems faced by all animals. Linked to this lesson is the realization that the physiology of a species will have limitations and biases that reflect individuals’ need to deal only with certain behavioral problems and only in specific ecological contexts. In behaviour, as in morphology, an animal’s capabilities are matched to its expected environmental requirements, because the process of natural selection shapes organisms as if it were always addressing the question of how much adaptation is enough.

Consider first the sensory abilities of animals. All actions (such as body movements, detection of objects of interest, or learning from others in a social group) begin with the acquisition of information. Thus, an animal’s sense organs are exceedingly important to its behaviour. They constitute a set of monitoring instruments with which the animal gathers information about itself and its environment. Each sense organ is selective, responding only to one particular form of energy an instrument that responds indiscriminately to multiple forms of energy would be rather useless and similar to having none at all. The particular form of energy to which a sense organ responds determines its sensory modality. Three broad categories of sensory modalities are familiar to humans: chemoreception (exemplified by the senses of taste and smell but also including specialized receptors for pheromones and other behaviorally important molecules), mechanoreception (the basis for touch, hearing, balance, and many other senses, such as joint position), and photoreception (light sensitivity, including form and colour vision).

The capabilities of an animal’s sense organs differ depending on the behavioral and ecological constraints of the species. In recognition of this fact and of the equally important fact that animals perceive their environments differently than do humans, ethologists have adopted the word Umwelt, a German word for environment, to denote an organism’s unique sensory world. The umwelt of a male yellow fever mosquito (Aedes aegypti), for example, differs sharply from that of a human. Whereas the human auditory system hears sounds over a wide range of frequencies, from 20 to about 20,000 Hz, the male mosquito’s hearing apparatus has been tuned narrowly to hear only sounds around 380 Hz. Despite its apparent limitations, a male mosquito’s auditory system serves him perfectly well, for the only sound he must detect is the enchanting wing-tone whine of a female mosquito hovering nearby, a sound all too familiar to anyone who lingers outdoors on a midsummer’s evening.

Pit vipers, colubrid snakes from the subfamily Crotalinae, which include the well-known rattlesnakes, provide another example of how the umwelt of a species serves its own ecological needs. Pit vipers possess directionally sensitive infrared detectors with which they can scan their environment while stalking mammalian prey, such as mice (Mus) and kangaroo rats (Dipodomys), in the dark. A forward-facing sensory pit, located on each side of the snake’s head between the eye and the nostril, serves as the animal’s heat-sensing organ. Each pit is about 1 to 5 mm (about 0.04 to 0.2 inch) deep. A thin membrane, which is extensively innervated and exquisitely sensitive to temperature increases, stretches from wall to wall inside the pit organ, where it functions like the film in a pinhole camera, registering any nearby source of infrared energy.

Human umwelt is not without its own limits and biases. Human eyes do not see the flashy advertisements to insects that flowers produce by reflecting ultraviolet light, and human ears do not hear the infrasonic calls of elephants or the ultrasonic sounds of bats. Furthermore, human noses are limited relative to those of many other mammals. Moreover, humans completely lack the sense organs for the detection of electric fields or of Earth’s geomagnetic field. Sense organs for the former occur in various species of electric fishes (such as electric eels and electric catfish), which use their sensitivity to electric fields for orientation, communication, and prey detection in murky jungle streams, while the latter exist in certain birds and insects, including homing pigeons and honeybees, which use them to navigate back to the home loft or hive. At the same time, unlike most animals, humans are endowed with superb visual acuity and colour vision as a result of having evolved large, high-performance, single-lens eyes.

Each species’ nervous system is an assemblage of special-purpose devices with species-specific and sometimes sex-specific capabilities. These capabilities become even more apparent when investigating how animals use their sense organs to acquire information for solving behavioral problems, such as territory defense or prey capture. Although an animal may possess diverse sensory organs that enable it to receive a great deal of information about the environment, in performing a particular behavioral task, it often responds to a rather small portion of the stimuli perceived. Moreover, only a subset of available stimuli reliably provides the information needed to perform a particular task. Ethologists call the crucial stimuli in any particular behavioral context “sign stimuli.”

A classic example of sign stimuli comes from the behaviour of male three-spined sticklebacks (Gasterosteus aculeatus) when these fish defend their mating territories in the springtime against intrusions from rival male sticklebacks. The males differ from all other objects and forms of life in their environment in a special way: they possess an intensely red throat and belly, which serve as signals to females and other males of their health and vigour. Experiments using models of other fish species have shown that the red colour is the paramount stimulus by which a territory-holding male detects an intruder. Models that accurately imitated sticklebacks but lacked the red markings were seldom attacked, whereas models that possessed a red belly but lacked many of the other characteristics of the sticklebacks, or even of fish in general, were vigorously attacked.

Similarly, the brain cells of some toads (Bufo) are tuned to pick out those features of the environment that reliably match the toads’ natural prey items (such as earthworms). Experiments were conducted in which a hungry toad was presented with cardboard models moving horizontally around the individual at a constant distance and angular velocity. The research revealed that just two stimuli, the elongation of the object (that is, making the cardboard model longer to increase resemblance to prey) and movement in the direction of the elongation, were sufficient to initiate the toad’s prey-catching behaviour. Subsequently, the toad jerked its head after the moving model in order to place it in its frontal visual field. Other stimuli, such as the colour of the model and its velocity of movement, did not influence the toad’s ability to distinguish worms from non-worms, even though toads possess good colour and form vision. Even the broadly tuned human sensory system operates in a highly selective, yet adaptive, manner. For instance, a person hunting white-tailed deer seeks the prey almost exclusively by watching closely for deerlike movements amid the stationary trees of a forest, not by straining to sense the deer’s shape, smell, or sound.

As with sensory systems, the neural mechanisms by which animals compute solutions to behavioral problems have not evolved to function as general-purpose computers. Rather, the central nervous system (that is, the brain and spinal cord of a vertebrate or one of the segmental ganglia of an invertebrate) performs specific computations associated with the particular ecological challenges that individuals face in their environment. A helpful illustration of this point is the startle response of goldfish (Carassius auratus). If a hungry predatory fish strikes from the side, the goldfish executes a brisk swivelling movement that propels its body sideways by about one body length to dodge the predator’s attack. How does the goldfish’s central nervous system process information from the sense organs to instantaneously decide the correct direction (right or left) to move? The key neural element in the startle response of the goldfish is a single bilateral pair of neurons, called the Mauthner neurons, located in the goldfish’s hindbrain. Each neuron on the left or right receives input from the lateral line system (a row of small pressure sensors that are triggered by the disturbances caused by nearby moving objects) located on the left or right side of the goldfish’s body. Each neuron sends output to neurons that activate the musculature on the opposite side of the body. There is strong, mutual inhibition between the left and right Mauthner neurons should the left one fire in response to a mechanical stimulus from the left side of the body, for example, the right one is inactivated. Inactivation prevents it from interfering with the crucial, initial contractions of the trunk muscles on the goldfish’s right side. The net effect is that 20 milliseconds after sensing danger the goldfish assumes a C-like shape with the head and tail bent to the same side and away from the attacker. This reaction is followed 20 milliseconds later by muscle contractions on the other side of the body so that the tail straightens and the fish propels itself sideways, away from the danger. Thus, the two Mauthner neurons of the goldfish’s nervous system function exquisitely for processing information regarding predator attacks, and solving this crucial behavioral problem appears to be the only task that they perform.

Small-brained creatures, such as fishes, are not the only species whose nervous systems have evolved to solve tasks in a limited—but ecologically sufficient—way that turns difficult problems of computation into more tractable ones. For example, take the task of a human computing an interception course with a flying object, such as when a baseball player runs to catch a fly ball. In principle, the task could be solved with a set of differential equations based on the observed curvature and acceleration of the ball. What happens instead, evidently, is that the fielder finds a running path that maintains a linear optical trajectory for the ball. In other words, the player adjusts the speed and direction of his movement over the baseball field so that the trajectory of the ball appears to be straight. Unlike the more complicated differential equation approach, the linear trajectory approach does not tell the player when or where the ball will land. Consequently, the player cannot run to the point where the ball will fall and wait for it. If he did, complicating factors such as wind gusts diverting the ball might mean that he would end up in the wrong place. Instead, the player simply keeps his body on a course that will ensure interception.

Once an animal has received information about the world from its sense organs and has computed a solution to whatever behavioral problem it currently faces, it responds with a coordinated set of movements—that is, a behaviour. Any particular movement reflects the patterned activity of a specific set of muscles that work on the skeletal structures to which they are attached. The activity of these muscles is controlled by a specific set of motor neurons that in turn are controlled by sets of interneurons connected to the animal’s brain. Thus, a given behaviour is ultimately the result of a specific pattern of neural activity.

Sometimes neural control takes the form of a simple sensory reflex, in which the activity in the motor neurons is triggered by sensory neurons. This activity can be achieved directly or via one or two interneurons. Other times, as in the case of rhythmic behaviour (such as with birds flying or insects walking), a central pattern generator located in the central nervous system produces rhythms of activity in the motor neurons. Central pattern generators do not depend on sensory feedback. Feedback, however, commonly occurs to modulate and reset the rhythm of the motor output after a disturbance to the animal’s behaviour, as in the case of air turbulence disrupting the wing movements of a flying bird.

Most commonly, the neural control of behaviour takes the form of a motor command in which the initiation and modulation of activity in the motor neurons is produced by interneurons descending from the animal’s brain. The animal’s brain is where inputs from multiple sensory modalities are integrated. In this way, a sophisticated tuning of the animal’s behaviour in relation to its internal condition and its external circumstances can occur. Often the control of an animal’s movements involves an intricate synthesis of all three forms of neural control: patterned neural activity, simple sensory reflex, and motor command. As in all aspects of behavioral physiology, an immense diversity exists among animal species and behaviour patterns in the way the components of behavioral machinery have been linked over time by natural selection.


I am writing a paper for graduate school on peer-mediated strategies for students with autism. I am citing Mr. Jaffe’s article to explain how students with autism have faulty mirror neuron systems however, because I haven’t been able to see Mr. Jaffe’s list of references which should come at the end of his article, I am stuck in trying to quote some of the people or studies listed in this article. Would you please help me find the list of references he used for this article? Thank you so much!

Lisa Shimabukuro

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STDP represents a potential shift in approach when it comes to developing learning procedures in neural networks. Recent research shows that it has predominantly been applied in pattern recognition related tasks. One 2015 study using an exponential STDP learning rule achieved 95% accuracy on the MNIST dataset [3], a large handwritten digit database that is widely used a training dataset for computer vision. Merely a year later, researchers have managed to make significant progress. For example, Kheradpisheh et al. achieved 98.5% accuracy MNIST by combining SNN and features of deep learning [4]. The network they used comprised several convolutional and pooling layers, and STDP learning rules were used in the convolutional layers to learn the features. Another interesting study took its inspiration from Reinforcement Learning and combined it with a hierarchical SNN to perform pattern recognition [5]. Using a network structure that consists of two simple and two complex layers and a novel reward-modulated STDP (R-STDP), their method outperformed classic unsupervised STDP on several image datasets. STDP has also been applied in real-time learning to take advantage of its speedy nature [6]. The SNN and fast unsupervised STDP learning method that was developed achieved an impressive 21.3 fps in training and 17.9 fps in testing. To put things in perspective, human eyes are able to detect around 24 fps.

Apart from object recognition, STDP has also been applied in speech recognition related tasks. One study uses an STDP-trained, nonrecurrent SNN to convert speech signals into a spike train signature for speech recognition [7]. Another study combines a hidden Markov model with SNN and STDP learning to classify segments of sequential data such as individual spoken words [8]. STDP has also proven to be a useful learning method in modelling pitch perception (i.e. recognising tones). Researchers developed a computational model using neural network that learns using STDP rules to identify (and strengthen) the neuronal connections that are most effective for the extraction of pitch [9].

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

2. Population Density Method

2.1. Integrate-and-Fire Neuron

LWgRvp5yfNYPwWlvZxP46I-PLmgy2399m-AE9lXoaWgEaWvmG4IvX2BSAHvNKekjFFq9HZd7PKxid13XiFbsEtArFJJlOk1Ec-SuXtP4-nezp9VdQEzlMKiwy5ZFjsYmdWmTy5ZfTVzvpWn8KStjLs4M4hZ01GkollOyJ-AHD-nJXVMKN2PsRR93nLyYoyKGwFuap1qbcugD7MjBYc0W25sQsAbdeAmxbJrQjBi-UC0s3xtzbELZVU9yChbH-5IO0dulGPUnIIpNg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" />⁠ , and vth=1 (i.e. ⁠ ). The evolution equation for voltage is 2

ebz8QtbQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" />⁠ . The gamma distribution of random conductance jump sizes upon receiving a synaptic input event has two parameters ⁠ :

2.2. Full 2D Density Equations

The solution to equations 2.6 to 2.13 is referred to as the 2D PDE.

If there are many populations, each similar population of neurons would have its own probability density function ⁠ , and the coupling would occur via the population firing rate (i.e., the presynaptic populations firing rate rk(t) would appear in the input rate of the postsynaptic population ⁠ see Nykamp & Tranchina, 2000, for further details).

2.3. Steady-State Input and Output for Full 2D Equations

Figure 1 illustrates the characteristics of the noisy LIF population receiving constant input. Figure 1A shows the V(t) and G(t) evolution in time governed by equations 2.1 and 2.2, with s −1 and a resulting firing rate of 11 s −1 . The system has already settled to a steady state where the probability density of neurons (over the population) does not change in time, and thus the firing rate is constant. Figure 1B shows the marginal densities f(v) and h(g) computed from the full 2D PDE (black line) and from the Monte Carlo simulations (shaded area). The Monte Carlo simulation of the marginal densities is calculated by assuming ergodicity, where the state variables for each neuron in the population are averaged over time, then binned in a histogram (across the population) and normalized. Thus, state values where the density has more probability mass are where the random variable spends more time for a given neuron. Figure 1C shows the steady-state input-output (or F-I) curves computed by both the 2D PDE (black line) and Monte Carlo simulations (gray dashed line). The match is very good overall, except for extremely large firing rates where the deviation is at most a couple of s −1 .

The noisy LIF population with constant input. (A) Monte Carlo simulation of a single neuron with s −1 , with a firing rate of r=11 s −1 . V(t) on top, G(t) below. (B) Comparison of the distribution of V and G values from Monte Carlo (averaged over N=1000 realizations, with population size M=1000) and the marginal distributions (equations 2.12 and 2.13) computed from the full 2D PDE. (C) Comparison of the constant input-output curve (or F-I curve). The 2D PDE (black line) is reasonably close to the Monte Carlo simulation (gray, dashed line) the differences are due to finite-size effects from the population size and number of realizations or numerical discretization of the 2D PDE.

The noisy LIF population with constant input. (A) Monte Carlo simulation of a single neuron with s −1 , with a firing rate of r=11 s −1 . V(t) on top, G(t) below. (B) Comparison of the distribution of V and G values from Monte Carlo (averaged over N=1000 realizations, with population size M=1000) and the marginal distributions (equations 2.12 and 2.13) computed from the full 2D PDE. (C) Comparison of the constant input-output curve (or F-I curve). The 2D PDE (black line) is reasonably close to the Monte Carlo simulation (gray, dashed line) the differences are due to finite-size effects from the population size and number of realizations or numerical discretization of the 2D PDE.

The deviation between the full 2D PDE and Monte Carlo simulations can stem from either the finite-size effects in the Monte Carlo simulations from population size and number of realizations and the numerical discretization to approximate the full 2D equations.

2.4. Mean-Field Equations

The mean-field approximation (noiseless) of the LIF population is crude and normally does not merit attention except to illustrate that any other characterization taking into account noise is superior and more realistic. However, this section thoroughly explains the mean-field approach because the main result of this letter uses an augmented mean-field approach (see section 3.2).

2.4.1. Steady-State Mean-Field Solution

This system is completely noiseless, and the “probability density” fMF(v) when the firing rate is nonzero should not be interpreted as V(t) having random values rather, the density (because of periodicity) represents the average time spent at particular voltage values.

Figure 2 mirrors Figure 1, except that it is for the noiseless mean-field system. Figure 2A shows the V(t) and G(t) evolution in time (starting from (V, G)=(0, 0)), with two input rates: s −1 (labeled as subthreshold) and s −1 (labeled as suprathreshold) that result in firing rates of 0 s −1 and 31 s −1 , respectively. Figure 2B shows the marginal densities f(v) and h(g) computed from equations 2.22 and 2.24, ignoring the transient dynamics. Importantly, Figure 2C shows the steady-state input-output curves computed in three ways: (1) the 2D PDE (black) of the noisy LIF population (same as Figure 1C), (2) Monte Carlo simulations (gray dashed line) of the noisy LIF population (same as Figure 1C), and (3) the mean-field system (black dotted line). The mean-field curve is a poor approximation because it is completely noiseless and fails to capture firing rates when it is solely driven by noise (or the fluctuation-driven regime). The mean-field equations fail to accurately capture firing rates below (approximately) 60 s −1 . Despite this severe limitation, the mean-field solutions are at least fast to compute, analytical, and straightforward to understand.

The noise-less LIF population constant input (i.e., mean field). (A) Simulation of a single neuron with two input rates: s −1 (black dashed line) and 2678 s −1 (black solid line), with firing rates of r=0 s −1 and r=31 s −1 , respectively. V(t) on top and G(t) below. In this figure, these two regimes are referred to as subthreshold and suprathreshold. (B) Comparison of the density of V and G values from equations 2.22 and 2.24. (C) Comparison of the constant input-output curve (or F-I curve). The 2D PDE (black) and Monte Carlo simulation (gray dashed line) are the same curves from Figure 1 and are shown for comparison purposes only. The F-I curve corresponding to the noiseless system mean field (black dotted line) has 0 firing rate for s −1 .

The noise-less LIF population constant input (i.e., mean field). (A) Simulation of a single neuron with two input rates: s −1 (black dashed line) and 2678 s −1 (black solid line), with firing rates of r=0 s −1 and r=31 s −1 , respectively. V(t) on top and G(t) below. In this figure, these two regimes are referred to as subthreshold and suprathreshold. (B) Comparison of the density of V and G values from equations 2.22 and 2.24. (C) Comparison of the constant input-output curve (or F-I curve). The 2D PDE (black) and Monte Carlo simulation (gray dashed line) are the same curves from Figure 1 and are shown for comparison purposes only. The F-I curve corresponding to the noiseless system mean field (black dotted line) has 0 firing rate for s −1 .

In tiny worms, researchers find spiking neurons -- and clues about brain computation

Contrary to popular belief, the brain is not a computer. However brains do, in their own way, compute. They integrate informational inputs to generate outputs, including behaviors, thoughts, and feelings.

To process vast amounts of data, the brain uses a kind of digital code. Its cells produce discrete bursts of electric current, known as action potentials, that function as the zeros and ones of the nervous system. This code is assumed to be a vital aspect of computation in animals -- that is, in most animals. The tiny roundworm C. elegans has long been considered a curious exception until now, action potentials had never been observed in the organism.

But in a recent study, Rockefeller scientist Cori Bargmann and her colleagues, Qiang Liu, Phil Kidd, and May Dobosiewicz, discovered, among other things, a C. elegans olfactory neuron that produces action potentials. The finding, published in Cell, overturns decades of dogma and could help scientists understand fundamental principles of brain computation.

Trial by fire

Neurons communicate with one another by exchanging chemical messages. Each message alters the state of the receiving cell and as a neuron collects more and more chemical input, it approaches a threshold of activation. An action potential occurs when the cell reaches this threshold, at which point the neuron is said to "fire" or "spike" as an electrical impulse ripples through its extremity. In producing this spike, the cell translates analog chemical messages into digital electric code.

Despite the apparent importance of action potentials, for years researchers believed that C. elegans and other nematodes simply didn't use this information processing strategy.

"There's this whole class of animals where the neurons didn't seem to spike," says Bargmann, the Torsten N. Wiesel Professor. "So our question was: Well, what do these neurons do?" Seeking an answer, her team set out to measure the electrical behavior of C. elegans neurons -- every single one of them, if necessary.

"The C. elegans has just 302 neurons, so it's one of the few animals where you can look at each individual neuron," says Liu, a research assistant professor in Bargmann's lab who set out to measure how all of these neurons respond to stimulation.

Almost immediately, Liu was met with a surprise. While stimulating AWA, a neuron that processes smell signals, he observed that the cell's electrical voltage rose very rapidly before dramatically plummeting. Though unexpected, this dynamic was also very familiar: it looked like an action potential.

A neuron with potential

Additional experiments confirmed that AWA neurons indeed spike. The researchers suspect that other C. elegans cells also produce action potentials yet they note that this is not the norm for this animal's neurons. In fact, their experiments revealed that even AWA fires rather infrequently. Typically, the neuron responds to odors in a more subtle, graded manner. Liu observed action potentials only during experiments in which the stimulus grew stronger over time, suggesting that in nature, AWA fires when the animal is approaching the source of an important smell.

"Here we have a neuron that encodes information in two ways: one way that is slower and graded, and one way that's very nonlinear and sharply tuned to particular circumstances," says Bargmann. "And this lets us see what a spike might be uniquely important for."

While this study initiates C. elegans into the ranks of spike-producing animals, the action potentials observed in this organism were not identical to those seen elsewhere. To define the characteristics of worm-specific spikes, postdoctoral associate Phil Kidd created a mathematical model of AWA's electrical dynamics -- a step that, the scientists hope, will allow their research to enter into conversation with other advances in computational neuroscience.

"There's a huge field of people working on the coding and computational principles of nervous systems," says Kidd. "And our work with C. elegans is likely to uncover principles that were unfamiliar to scientists who have been working in these areas for a long time."

This line of research indeed has the potential to both expand scientific understanding of C. elegans, and of nervous systems at large.

"Computation in the brain is a deep and important problem," says Bargmann. "With this study, we've shown that C. elegans can help solve this puzzle -- and in fact, we've already exposed a whole new piece of it."



Complete list of publications of the lab can be found here !


The ZENITH training program is HIRING 3 students in maths or computer science, contact Claire!


We are hiring a computer scientist to push our analysis of behaviour using deep networks!


July 2020: updated CV for Claire Wyart, please download below


Check out our tracking algorithm to monitor kinematics and postural defects in zebrafish!


A calibrated toolbox for in vivo optogenetics in zebrafish !


The Reissner fiber is necessary for neurons contacting the CSF to sense spinal curvature !


Modelling the flow of cerebrospinal fluid in the central canal!


Innate immunity at the CSF interface: how do sensory neurons fight infections in the CNS !


Claire Wyart is elected to the EMBO Membership !


An article on morphogenesis and sensory function : how do microvilli contribute to sensing!


Hindbrain circuits underlying locomotion : focus on the inhibitory Eng1 brainstem neurons!


Check out single channel recordings IN VIVO: PKD2L1 & mechanotransduction !


HFSP research grant for the team: Investigation of signalling in the CSF in fish & mouse !


Postdoc fellowships for Olivier, Martin and Yasmine: Congrats!


Movie on the team : What is happening in the mind of scientists.


Congrats to Adna Dumitrescu for her travel grant : good luck now for your experiments in Chicago!


Congrats to Yasmine Cantaut-Belarif, PL Bardet for the Big Brain Theory grant from ICM .


Congrats to Martin Carbo-Tano for the Prestige fellowship!


July 2017: Laura Desban obtains a fourth PhD year from the FRM: congrats !


Our review is out on CSF-contacting neurons in Journal of Neurogenetics !


Mechanosensory feedback enhances speed of locomotion via a novel circuit !


April 2017: Kevin moves to Columbia University


March 2017: Kristen moves to the Bormuth lab in UPMC!


March 2017: Urs starts in Harvard University


Lydia starts in Harvard university


A study on neurosecretory properties and morphology of neurons contacting the CSF!


Claire is awarded the 2017 prize from the Fondation Scientifique pour l Education et la Recherche


Claire is awarded the 2016 New York Stem Cell Foundation Innovator in Neuroscience Robertson Award


Claire is a 2016 awardee of the EMBO-Young Investigator Program


How do neurons contacting the CSF project on fast locomotor circuits in the spinal cord ?


Check out the botulinum toxin light chain to silence vesicular release in vivo !


3D holographic method for optogenetic applications with the Emiliani lab!


First in vivo demonstration of the mechanosensory function of neurons contacting the CSF !


Identification of the V0-v as targets of neurons contacting the CSF in the spinal cord !


Conservation of neurons contacting the cerebrospinal fluid in mouse, macaque and zebrafish !

Watch the video: The Neuron (February 2023).