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Memory allocation is a process that determines which specific synapses and neurons in a neural network will store a given memory. [1] [2] [3] Although multiple neurons can receive a stimulus, only a subset of the neurons will induce the necessary plasticity for memory encoding. The selection of this subset of neurons is termed neuronal allocation.
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural network.
The neurons are typically organized into multiple layers, especially in deep learning. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers. The layer that receives external data is the input layer. The layer that produces the ultimate result is the output layer.
It is hypothesized that the elementary biological unit is an active cell, called neuron, and the human machine is run by a vast network that connects these neurons, called neural (or neuronal) network. [5] The neural network is integrated with the human organs to form the human machine comprising the nervous system. [citation needed]
Animated confocal micrograph, showing interconnections of medium spiny neurons in mouse striatum. A neural network, also called a neuronal network, is an interconnected population of neurons (typically containing multiple neural circuits). [1] Biological neural networks are studied to understand the organization and functioning of nervous systems.
In one study, single-cell recordings were taken from electrodes implanted in a rat's hippocampus, and it was found that certain neurons responded strongly only when the rat was in certain locations. These cells are called place cells, and collections of these cells can be considered to be mental maps. Individual place cells do not only respond ...
Simple artificial neurons, such as the McCulloch–Pitts model, are sometimes described as "caricature models", since they are intended to reflect one or more neurophysiological observations, but without regard to realism. [3] Artificial neurons can also refer to artificial cells in neuromorphic engineering that are similar to natural physical ...
The transition of a memory from short term to long term is called memory consolidation. Little is known about the physiological processes involved. Two propositions of how the brain achieves this task are backpropagation or backprop and positive feedback from the endocrine system.