  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.
