Possible Mechanisms for Neural Reconfigurability and their Implications
This addresses the fundamental question of neural flexibility in neuroscience, offering a biologically plausible model that could unify various computational approaches.
The paper tackles the problem of how neural circuits can dynamically reconfigure to perform multiple computations, showing that this reconfigurability accounts for stochastic and distributed coding in neurons and explains timing phenomena in psychophysical experiments.
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.