Synaptic weight
Encyclopedia
In neuroscience
and computer science
, synaptic weight refers to the strength or amplitude of a connection between two nodes, corresponding in biology to the amount of influence the firing
of one neuron
has on another. The term is typically used in artificial
and biological
neural network
research.
and outputs 
, or pre- and post-synaptic neurons respectively, are interconnected with synaptic weights represented by the matrix 
, where for a linear neuron
.
The synaptic weight is changed by using a learning rule, the most basic of which is Hebb's rule, which is usually stated in biological terms as
Computationally, this means that if a large signal from one of the input neurons results in a large signal from one of the output neurons, then the synaptic weight between those two neurons will increase. The rule is unstable, however, and is typically modified using such variations as Oja's rule
, radial basis functions or the backpropagation
algorithm.
models such as BCM theory
have seen some success in mathematically describing these networks.
In the mammalian central nervous system
, signal transmission is carried out by interconnected networks of nerve cells, or neurons. For the basic pyramidal neuron, the input signal is carried by the axon
, which releases neurotransmitter chemicals into the synapse
which is picked up by the dendrites of the next neuron, which can then generate an action potential
which is analogous to the output signal in the computational case.
The synaptic weight in this process is determined by several variable factors:
The changes in synaptic weight that occur is known as synaptic plasticity
, and the process behind long-term changes (long-term potentiation
and depression
) is still poorly understood. Hebb's original learning rule was originally applied to biological systems, but has had to undergo many modifications as a number of theoretical and experimental problems came to light.
Neuroscience
Neuroscience is the scientific study of the nervous system. Traditionally, neuroscience has been seen as a branch of biology. However, it is currently an interdisciplinary science that collaborates with other fields such as chemistry, computer science, engineering, linguistics, mathematics,...
and computer science
Computer science
Computer science or computing science is the study of the theoretical foundations of information and computation and of practical techniques for their implementation and application in computer systems...
, synaptic weight refers to the strength or amplitude of a connection between two nodes, corresponding in biology to the amount of influence the firing
Action potential
In physiology, an action potential is a short-lasting event in which the electrical membrane potential of a cell rapidly rises and falls, following a consistent trajectory. Action potentials occur in several types of animal cells, called excitable cells, which include neurons, muscle cells, and...
of one neuron
Neuron
A neuron is an electrically excitable cell that processes and transmits information by electrical and chemical signaling. Chemical signaling occurs via synapses, specialized connections with other cells. Neurons connect to each other to form networks. Neurons are the core components of the nervous...
has on another. The term is typically used in artificial
Artificial neural network
An artificial neural network , usually called neural network , is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes...
and biological
Biological neural network
In neuroscience, a biological neural network describes a population of physically interconnected neurons or a group of disparate neurons whose inputs or signalling targets define a recognizable circuit. Communication between neurons often involves an electrochemical process...
neural network
Neural network
The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes...
research.
Computation
In a computational neural network, a vector or set of inputs and outputs , or pre- and post-synaptic neurons respectively, are interconnected with synaptic weights represented by the matrix , where for a linear neuron.The synaptic weight is changed by using a learning rule, the most basic of which is Hebb's rule, which is usually stated in biological terms as
Neurons that fire together, wire together.
Computationally, this means that if a large signal from one of the input neurons results in a large signal from one of the output neurons, then the synaptic weight between those two neurons will increase. The rule is unstable, however, and is typically modified using such variations as Oja's rule
Oja's rule
Oja's learning rule, or simply Oja's rule, named after a Finnish computer scientist Erkki Oja, is a model of how neurons in the brain or in artificial neural networks change connection strength, or learn, over time...
, radial basis functions or the backpropagation
Backpropagation
Backpropagation is a common method of teaching artificial neural networks how to perform a given task. Arthur E. Bryson and Yu-Chi Ho described it as a multi-stage dynamic system optimization method in 1969 . It wasn't until 1974 and later, when applied in the context of neural networks and...
algorithm.
Biology
For biological networks, the effect of synaptic weights is not as simple as for linear neurons or Hebbian learning. However, biophysicalBiophysics
Biophysics is an interdisciplinary science that uses the methods of physical science to study biological systems. Studies included under the branches of biophysics span all levels of biological organization, from the molecular scale to whole organisms and ecosystems...
models such as BCM theory
BCM theory
BCM theory, BCM synaptic modification, or the BCM rule, named for Elie Bienenstock, Leon Cooper, and Paul Munro, is a physical theory of learning in the visual cortex developed in 1981...
have seen some success in mathematically describing these networks.
In the mammalian central nervous system
Central nervous system
The central nervous system is the part of the nervous system that integrates the information that it receives from, and coordinates the activity of, all parts of the bodies of bilaterian animals—that is, all multicellular animals except sponges and radially symmetric animals such as jellyfish...
, signal transmission is carried out by interconnected networks of nerve cells, or neurons. For the basic pyramidal neuron, the input signal is carried by the axon
Axon
An axon is a long, slender projection of a nerve cell, or neuron, that conducts electrical impulses away from the neuron's cell body or soma....
, which releases neurotransmitter chemicals into the synapse
Synapse
In the nervous system, a synapse is a structure that permits a neuron to pass an electrical or chemical signal to another cell...
which is picked up by the dendrites of the next neuron, which can then generate an action potential
Action potential
In physiology, an action potential is a short-lasting event in which the electrical membrane potential of a cell rapidly rises and falls, following a consistent trajectory. Action potentials occur in several types of animal cells, called excitable cells, which include neurons, muscle cells, and...
which is analogous to the output signal in the computational case.
The synaptic weight in this process is determined by several variable factors:
- How well the input signal propagates through the axon (see myelination),
- The amount of neurotransmitter released into the synapse and the amount that can be absorbed in the following cell (determined by the number of AMPAAMPA receptorThe α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor is a non-NMDA-type ionotropic transmembrane receptor for glutamate that mediates fast synaptic transmission in the central nervous system . Its name is derived from its ability to be activated by the artificial glutamate analog AMPA...
and NMDA receptorNMDA receptorThe NMDA receptor , a glutamate receptor, is the predominant molecular device for controlling synaptic plasticity and memory function....
s on the cell membrane and the amount of intracellular calciumCalciumCalcium is the chemical element with the symbol Ca and atomic number 20. It has an atomic mass of 40.078 amu. Calcium is a soft gray alkaline earth metal, and is the fifth-most-abundant element by mass in the Earth's crust...
and other ions), - The number of such connections made by the axon to the dendrites,
- How well the signal propagates and integrates in the postsynaptic cell.
The changes in synaptic weight that occur is known as synaptic plasticity
Synaptic plasticity
In neuroscience, synaptic plasticity is the ability of the connection, or synapse, between two neurons to change in strength in response to either use or disuse of transmission over synaptic pathways. Plastic change also results from the alteration of the number of receptors located on a synapse...
, and the process behind long-term changes (long-term potentiation
Long-term potentiation
In neuroscience, long-term potentiation is a long-lasting enhancement in signal transmission between two neurons that results from stimulating them synchronously. It is one of several phenomena underlying synaptic plasticity, the ability of chemical synapses to change their strength...
and depression
Long-term depression
Long-term depression , in neurophysiology, is an activity-dependent reduction in the efficacy of neuronal synapses lasting hours or longer. LTD occurs in many areas of the CNS with varying mechanisms depending upon brain region and developmental progress...
) is still poorly understood. Hebb's original learning rule was originally applied to biological systems, but has had to undergo many modifications as a number of theoretical and experimental problems came to light.
See also
- Neural networkNeural networkThe term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes...
- Synaptic plasticitySynaptic plasticityIn neuroscience, synaptic plasticity is the ability of the connection, or synapse, between two neurons to change in strength in response to either use or disuse of transmission over synaptic pathways. Plastic change also results from the alteration of the number of receptors located on a synapse...
- Hebbian theoryHebbian theoryHebbian 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...