Possible Mechanisms for Neural Reconfigurability and their Implications

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.