Timing information in transient behavior of neuropopulations

A computational model of neuronal populations based on neurophysiological data is presented. The neuronal population consists of model neurons that simulate electrochemical processes in real neurons. Input and output of the population are defined in terms of the stochastic behavior of the single neurons. Analysis of the populational behavior shows that neuronal populations can approximately function as delay elements that transform step input to step output, and the delay intervals can specify timing information in the several-millisecond range, which is essential to high speed parallel computation. An example network composed of neuronal populations is simulated to show how the timing information can serve to achieve parallel processing in neural networks