Hebbian theory - Citizendia

Hebbian theory describes a basic mechanism for synaptic plasticity wherein an increase in synaptic efficacy arises from the presynaptic cell's repeated and persistent stimulation of the postsynaptic cell. In Neuroscience, synaptic plasticity is the ability of the connection or Synapse, between two Neurons to change in strength. Chemical synapses are specialized junctions through which Neurons signal to each other and to non-neuronal cells such as those in Muscles or Glands Chemical synapses are specialized junctions through which Neurons signal to each other and to non-neuronal cells such as those in Muscles or Glands Chemical synapses are specialized junctions through which Neurons signal to each other and to non-neuronal cells such as those in Muscles or Glands Introduced by Donald Hebb in 1949, it is also called Hebb's rule, Hebb's postulate, and cell assembly theory, and states:

Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability. Donald Olding Hebb ( July 22, 1904 &ndash August 20, 1985) was a Canadian Psychologist who was influential in the area of Neuropsychology Year 1949 ( MCMXLIX) was a Common year starting on Saturday (link will display the full calendar of the Gregorian calendar. … When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased.

The theory is often summarized as "cells that fire together, wire together", although this is an oversimplification of the nervous system not to be taken literally, as well as not accurately representing Hebb's original statement on cell connectivity strength changes. The nervous system is a Network of specialized cells that communicate information about an animal's surroundings and itself The theory is commonly evoked to explain some types of associative learning in which simultaneous activation of cells leads to pronounced increases in synaptic strength. Chemical synapses are specialized junctions through which Neurons signal to each other and to non-neuronal cells such as those in Muscles or Glands Such learning is known as Hebbian learning.

1 Hebbian engrams and cell assembly theory
2 Principles
3 Generalization and stability
4 See also
5 References
6 Further reading
7 External links

Hebbian engrams and cell assembly theory

Hebbian theory concerns how neurons might connect themselves to become engrams. Engrams are a Hypothetical means by which Memory traces are stored as biophysical or biochemical change in the Brain (and other Hebb's theories on the form and function of cell assemblies can be understood from the following:

"The general idea is an old one, that any two cells or systems of cells that are repeatedly active at the same time will tend to become 'associated', so that activity in one facilitates activity in the other. " (Hebb 1949, p. 70)

"When one cell repeatedly assists in firing another, the axon of the first cell develops synaptic knobs (or enlarges them if they already exist) in contact with the soma of the second cell. " (Hebb 1949, p. 63)

Gordon Allport posits additional ideas regarding cell assembly theory and its role in forming engrams, along the lines of the concept of auto-association, described as follows:

"If the inputs to a system cause the same pattern of activity to occur repeatedly, the set of active elements constituting that pattern will become increasingly strongly interassociated. Gordon Willard Allport ( November 11 1897 - October 9 1967) was an American Psychologist. That is, each element will tend to turn on every other element and (with negative weights) to turn off the elements that do not form part of the pattern. To put it another way, the pattern as a whole will become 'auto-associated'. We may call a learned (auto-associated) pattern an engram. " (Hebb 1949, p. 44)

Hebbian theory has been the primary basis for the conventional view that when analyzed from a holistic level, engrams are neuronal nets or neural networks. Traditionally the term neural network had been used to refer to a network or circuit of biological neurons.

Work in the laboratory of Eric Kandel has provided evidence for the involvement of Hebbian learning mechanisms at synapses in the marine gastropod Aplysia californica. Eric Richard Kandel (born November 7, 1929) is a Psychiatrist, a neuroscientist and Professor of Biochemistry The class Gastropoda or the gastropods, also previously known as gasteropods, or univalves, and more commonly known as Snails The California sea slug ( Aplysia californica) is also commonly called the California sea hare, and this is because the shape of all Aplysia species

Experiments on Hebbian synapse modification mechanisms at the central nervous system synapses of vertebrates are much more difficult to control than are experiments with the relatively simple peripheral nervous system synapses studied in marine invertebrates. In Vertebrates the central nervous system ( CNS) is the part of the Nervous system which is enclosed in the Meninges. Chemical synapses are specialized junctions through which Neurons signal to each other and to non-neuronal cells such as those in Muscles or Glands Vertebrates are members of the Subphylum Vertebrata, Chordates with backbones or spinal columns The grouping sometimes includes The peripheral nervous system ( PNS) resides or extends outside the Central nervous system (CNS which consists of the Brain and Spinal cord. Much of the work on long-lasting synaptic changes between vertebrate neurons (such as long-term potentiation) involves the use of non-physiological experimental stimulation of brain cells. In Neuroscience, long-term potentiation ( LTP) is the long-lasting improvement in communication between two Neurons that results from stimulating them However, some of the physiologically relevant synapse modification mechanisms that have been studied in vertebrate brains do seem to be examples of Hebbian processes. One such study reviews results from experiments that indicate that long-lasting changes in synaptic strengths can be induced by physiologically relevant synaptic activity working through both Hebbian and non-Hebbian mechanisms

Principles

From the point of view of artificial neurons and artificial neural networks, Hebb's principle can be described as a method of determining how to alter the weights between model neurons. The weight between two neurons will increase if the two neurons activate simultaneously; it is reduced if they activate separately. Nodes which tend to be either both positive or both negative at the same time will have strong positive weights while those which tend to be opposite will have strong negative weights. It is sometimes stated more simply as "neurons that fire together, wire together. "

This original principle is perhaps the simplest form of weight selection. While this means it can be relatively easily coded into a computer program and used to update the weights for a network, it also prohibits the number of applications of Hebbian learning. Computer programs (also software programs, or just programs) are instructions for a Computer. Today, the term Hebbian learning generally refers to some form of mathematical abstraction of the original principle proposed by Hebb. In this sense, Hebbian learning involves weights between learning nodes being adjusted so that each weight better represents the relationship between the nodes. As such, many learning methods can be considered to be somewhat Hebbian in nature. In the fields of Neuropsychology, Personal development and Education, Learning is one of the most important Mental function of humans

The following is a formulaic description of Hebbian learning: (note that many other descriptions are possible)

$\,w_{ij}=x_ix_j$

where $w i j$ is the weight of the connection from neuron $j$ to neuron $i$ and $x i$ the input for neuron $i$ . An artificial neuron is a mathematical function conceived as a crude model or abstraction of biological Neurons Artificial neurons are the constitutive units in an Artificial Note that this is pattern learning (weights updated after every training example). In a Hopfield network, connections $w i j$ are set to zero if $i = j$ (no reflexive connections allowed). A Hopfield net is a form of recurrent artificial neural network invented by John Hopfield. With binary neurons (activations either 0 or 1), connections would be set to 1 if the connected neurons have the same activation for a pattern.

Another formulaic description is:

$w_{ij} = \frac{1}{n} \sum_{k=1}^p x_i^k x_j^k\,$ ,

where $w i j$ is the weight of the connection from neuron $j$ to neuron $i$ , $n$ is the dimension of the input vector, $p$ the number of training patterns, and $x_{i}^k$ the $k$ th input for neuron $i$ . This is learning by epoch (weights updated after all the training examples are presented). Again, in a Hopfield network, connections $w i j$ are set to zero if $i = j$ (no reflexive connections). A Hopfield net is a form of recurrent artificial neural network invented by John Hopfield.

A variation of Hebbian learning that takes into account phenomena such as blocking and many other neural learning phenomena is the mathematical model of Harry Klopf. Klopf's model reproduces a great many biological phenomena, and is also simple to implement.

Generalization and stability

Hebb's Rule is often generalized as

$\,\Delta w_i = \eta x_i y$ ,

or the change in the $i$ th synaptic weight $w i$ is equal to a learning rate $η$ times the $i$ th input $x i$ times the postsynaptic response $y$ . Often cited is the case of a linear neuron,

$\,y = \sum_j w_j x_j$ ,

and the previous section's simplification takes both the learning rate and the input weights to be 1. This version of the rule is clearly unstable, as in any network with a dominant signal the synaptic weights will increase or decrease exponentially. However, it can be shown that for any neuron model, Hebb's rule is unstable. Therefore, network models of neurons usually employ other learning theories such as BCM theory, Oja's rule^[1], or the Generalized Hebbian Algorithm. Oja's Learning Rule, or simply Oja's rule, is a model of how neurons in the brain or in Artificial neural networks change connection strength or learn over time The Generalized Hebbian Algorithm ( GHA) also known in the literature as Sanger's rule, is a linear Feedforward Neural network model for unsupervised

References

^ Shouval, Harel (2005-01-03). Traditionally the term neural network had been used to refer to a network or circuit of biological neurons. In Neuroscience, long-term potentiation ( LTP) is the long-lasting improvement in communication between two Neurons that results from stimulating them In Psychology, memory is an organism's ability to store retain and subsequently retrieve information In neurobiology a tetanic stimulation consists of a high-frequency sequence of individual stimulations of a Neuron. Coincidence detection in the context of neurobiology is a process by which a Neuron or a neural circuit can encode information by detecting the occurrence of simultaneous yet Metaplasticity is a term originally coined by WC Abraham and M Year 2005 ( MMV) was a Common year starting on Saturday (link displays full calendar of the Gregorian calendar. Events 1431 - Joan of Arc is handed over to the Bishop Pierre Cauchon. The Physics of the Brain. The Synaptic basis for Learning and Memory: A theoretical approach. The University of Texas Health Science Center at Houston. Retrieved on 2007-11-14. Year 2007 ( MMVII) was a Common year starting on Monday of the Gregorian calendar in the 21st century. Events 1533 - Conquistadors from Spain under the leadership of Francisco Pizarro arrive in Cajamarca, Inca

External links

Overview
Hebbian Learning tutorial (Part 1: Novelty Filtering, Part 2: PCA)

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Contents

Hebbian engrams and cell assembly theory

Principles

Generalization and stability

See also

References

Further reading

External links