  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.

Neurons and Cognition