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A key step in many perceptual decision tasks is the integration of sensoryinputs over time, but fundamental questions remain about how this isaccomplished in neural circuits. One possibility is to balance decay modes ofmembranes and synapses with recurrent excitation. To allow integration overlong timescales, however, this balance must be precise; this is known as thefine tuning problem. The need for fine tuning can be overcome via aratchet-like mechanism, in which momentary inputs must be above a preset limitto be registered by the circuit. The degree of this ratcheting embodies atradeoff between sensitivity to the input stream and robustness againstparameter mistuning.  The goal of our study is to analyze the consequences of this tradeoff fordecision making performance. For concreteness, we focus on the well-studiedrandom dot motion discrimination task. For stimulus parameters constrained byexperimental data, we find that loss of sensitivity to inputs has surprisinglylittle cost for decision performance. This leads robust integrators toperformance gains when feedback becomes mistuned. Moreover, we find thatsubstantially robust and mistuned integrator models remain consistent withchronometric and accuracy functions found in experiments. We explain ourfindings via sequential analysis of the momentary and integrated signals, anddiscuss their implication: robust integrators may be surprisingly well-suitedto subserve the basic function of evidence integration in many cognitive tasks.

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