Neural integrators for decision making: A favorable tradeoff between robustness and sensitivity

Abstract

A key step in many perceptual decision tasks is the integration of sensory inputs over time, but fundamental questions remain about how this is accomplished in neural circuits. One possibility is to balance decay modes of membranes and synapses with recurrent excitation. To allow integration over long timescales, however, this balance must be precise; this is known as the fine tuning problem. The need for fine tuning can be overcome via a ratchet-like mechanism, in which momentary inputs must be above a preset limit to be registered by the circuit. The degree of this ratcheting embodies a tradeoff between sensitivity to the input stream and robustness against parameter mistuning. The goal of our study is to analyze the consequences of this tradeoff for decision making performance. For concreteness, we focus on the well-studied random dot motion discrimination task. For stimulus parameters constrained by experimental data, we find that loss of sensitivity to inputs has surprisingly little cost for decision performance. This leads robust integrators to performance gains when feedback becomes mistuned. Moreover, we find that substantially robust and mistuned integrator models remain consistent with chronometric and accuracy functions found in experiments. We explain our findings via sequential analysis of the momentary and integrated signals, and discuss their implication: robust integrators may be surprisingly well-suited to subserve the basic function of evidence integration in many cognitive tasks.