Abstract
A key step in many perceptual decision tasks is the integration of sensory inputs over time, but a 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 exceedingly precise. The need for fine tuning can be overcome via a “robust integrator” mechanism in which momentary inputs must be above a preset limit to be registered by the circuit. The degree of this limiting embodies a tradeoff between sensitivity to the input stream and robustness against parameter mistuning. Here, we analyze the consequences of this tradeoff for decision-making performance. For concreteness, we focus on the well-studied random dot motion discrimination task and constrain stimulus parameters by experimental data. We show that mistuning feedback in an integrator circuit decreases decision performance but that the robust integrator mechanism can limit this loss. Intriguingly, even for perfectly tuned circuits with no immediate need for a robustness mechanism, including one often does not impose a substantial penalty for decision-making performance. The implication is that robust integrators may be well suited to subserve the basic function of evidence integration in many cognitive tasks. We develop these ideas using simulations of coupled neural units and the mathematics of sequential analysis.
Highlights
Our most surprising result is not the subtle improvement that robustness can confer in our simulated task but the fact that levels of robustness can be quite high before they begin to degrade decision-making performance
We have demonstrated that robustness serves to protect an integrator against the hazards of runaway excitation and leak and that the cost of doing so is surprisingly small
We have demonstrated that increasing the robustness limit Rcan improve performance for mistuned integrators, in both the reaction time and controlled duration tasks
Summary
Monte Carlo simulations of Eq 5 were performed using the Euler-Maruyama method (Higham 2001), with dt ϭ 0.1 ms. The range and spacing of these values were chosen dependent on the values of Rand  for the simulation; the range was adjusted to capture the relative maximum of reward rate as a function of , while the spacing was adjusted to find the optimal value with a resolution of Ϯ0.1: the values of and ␥ (see table included in Fig. 15) were chosen to best match accuracy and chronometric functions to behavioral data reported in Roitman and Shadlen (2002). This was accomplished by minimizing the sum-squared error in data vs. model accuracy and chronometric curves across a discrete grid of and ␥ values, with a resolution of 0.1
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