Computer simulation of neurone pattern processing.

Abstract

Physiological simulation techniques can provide a very useful additional tool with which to explore the behaviour of neurones, especially for probing those areas that are not easily accessible to experimental monitoring techniques such as activity in the dendrites. This paper explores the information processing capabilities of a cerebral cortical pyramidal neurone using a computer simulation. The computer model simulated two important aspects of pyramidal cell behaviour: the transient distribution of membrane potential over a proportion of the apical dendritic tree of the cell, and the ability of the receptors in the neurones' dendritic spines to undergo plastic changes. The simulation of receptor behaviour modelled the ability of receptor regions to increase their sensitivity if a prior input had occurred when the spine head membrane was depolarized. The model was used to explore the response of the cell to different inputs. It underlined the need for large amounts of local activity before spinal receptors changed their sensitivity. It also demonstrated the ability of pyramidal cells to both recognize patterns of input and to associate one pattern of inputs with another. It is argued that this pattern association property could be a neuronal basis for association learning.