Operator control of interneural computing machines.

Abstract

A dynamic representation of neural population responses asserts that motor cortex is a flexible pattern generator sending rhythmic, oscillatory signals to generate multiphasic patterns of movement. This raises a question concerning the design and control of new computing machines that mimic the oscillatory patterns and multiphasic patterns seen in neural systems. To address this issue, we design an interneural computing machine (INCM) made of plastic random interneural connections. We develop a mechanical way to measure collective ensemble firing of neurons in INCM. Two sorts of plasticity operators are derived from the measure of synchronous neural activity and the measure of self-sustaining neural activity, respectively. Such plasticity operators conduct activity-dependent operation to modify the network structure of INCM. The activity-dependent operation meets the neurobiological perspective of Hebbian synaptic plasticity and displays the tendency toward circulation breaking aiming to control neural population dynamics. We call such operation operator control of INCM and develop a population analysis of operator control for measuring how well single neurons of INCM can produce rhythmic, oscillatory activity, but at the level of neural ensembles, generate multiphasic patterns of population responses.