Possible Mechanisms for Neural Reconfigurability and their Implications

TL;DR

This paper proposes a biologically plausible neural architecture enabling dynamic reconfiguration of neural pathways for diverse computations, explaining observed neural coding and timing phenomena, and linking to various statistical classifiers.

Contribution

It introduces a novel neural reconfigurability mechanism that accounts for neural coding, timing, and links to classifiers, with implications for neurophysiological interpretations.

Findings

Reconfigurable pathways can perform multiple computations.

Reconfigurability explains stochastic neural coding.

Links to decision trees and Bayesian methods.

Abstract

The paper introduces a biologically and evolutionarily plausible neural architecture that allows a single group of neurons, or an entire cortical pathway, to be dynamically reconfigured to perform multiple, potentially very different computations. The paper shows that reconfigurability can account for the observed stochastic and distributed coding behavior of neurons and provides a parsimonious explanation for timing phenomena in psychophysical experiments. It also shows that reconfigurable pathways correspond to classes of statistical classifiers that include decision lists, decision trees, and hierarchical Bayesian methods. Implications for the interpretation of neurophysiological and psychophysical results are discussed, and future experiments for testing the reconfigurability hypothesis are explored.