Cortical Potential Distributions and Cognitive Information Processing

TL;DR

This paper proposes using cortical field potentials instead of spike trains for cognitive processing, creating a natural metric space for cognitive elements and linking spike train similarity to potential distribution proximity.

Contribution

It introduces a framework that models cognitive information processing through cortical potentials, connecting spike train similarity with potential distribution metrics.

Findings

Cortical potentials form a natural metric space for cognition.

Spike train similarity correlates with potential distribution proximity.

Framework links neural activity patterns to cognitive element spaces.

Abstract

The use of cortical field potentials rather than the details of spike trains as the basis for cognitive information processing is proposed. This results in a space of cognitive elements with natural metrics. Sets of spike trains may also be considered to be points in a multidimensional metric space. The closeness of sets of spike trains in such a space implies the closeness of points in the resulting function space of potential distributions.