alexa A simple neural network generating an interactive memory


Journal of Information Technology & Software Engineering

Author(s): James A Anderson

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A model of a neural system where a group of neurons projects to another group of neurons is discussed. We assume that a trace is the simultaneous pattern of individual activities shown by a group of neurons. We assume synaptic interactions add linearly and that synaptic weights (quantitative measure of degree of coupling between two cells) can be coded in a simple but optimal way where changes in synaptic weight are proportional to the product of pre-and postsynaptic activity at a given time. Then it is shown that this simple system is capable of “memory” in the sense that it can (1) recognize a previously presented trace and (2) if two traces have been associated in the past (that is, if trace f̄ was impressed on the first group of neurons and trace ḡ was impressed on the second group of neurons and synaptic weights coupling the two groups changed according to the above rule) presentation of f̄ to the first group of neurons gives rise to f̄ plus a calculable amount of noise at the second set of neurons. This kind of memory is called an “interactive memory” since distinct stored traces interact in storage. It is shown that this model can effectively perform many functions. Quantitative expressions are derived for the average signal to noise ratio for recognition and one type of association. The selectivity of the system is discussed. References to physiological data are made where appropriate. A sketch of a model of mammalian cerebral cortex which generates an interactive memory is presented and briefly discussed. We identify a trace with the activity of groups of cortical pyramidal cells. Then it is argued that certain plausible assumptions about the properties of the synapses coupling groups of pyramidal cells lead to the generation of an interactive memory.

This article was published in Mathematical Biosciences and referenced in Journal of Information Technology & Software Engineering

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