Cortical circuit-based lossless neural integrator for perceptual decision-making

Abstract

SummaryThe intrinsic uncertainty of sensory information (i.e., evidence) does not necessarily deter an observer from making a reliable decision. It is believed that uncertainty is reduced by integrating (accumulating) incoming sensory evidence. Traditionally, recurrent network models have been thought to underlie the integration of evidence necessary for decision-making, but they have yet to fully explain humans' ability to hold accumulated evidence during temporal gaps in sensory inputs. In our study, using a computational model, we show that cortical circuits can readily explain humans' ability to make robust decisions during temporal gaps. Specifically, our model predicts that the interplay among depressing synapses and the two major interneuron types allow the cortex to switch flexibly between 'retention' and 'integration' modes. The evidence can be read out in two different modes mirroring 'stepping' and 'ramping' activity observed empirically, suggesting that these two activity patterns emerge from the same mechanism.Keywords: perceptual decision-making; neural integrator; computational model of cortex; inhibitory neuron types; depressing synapses; ramping and stepping activity peer-reviewed)