The encoding of alternatives in multiple-choice decision-making

Abstract

Here, we present a minimal biophysically realistic spiking neuron model for decision-making with multiple alternatives. Our model accounts for all relevant aspects of recent experimental data of a random-dot motion discrimination task [1], on both the cellular and behavioral level. Our specific objective was to construct a network model where all network parameters and inputs are independent of the number of possible alternatives. Thereby, we avoid the use of extra top-down regulation mechanisms to adapt the network to the choice number. Our network is an extension of Wang's [2] binary decision-making model, which is based on attractor dynamics and winner-take-all competition of two selective populations of neurons (pools), each representing one choice alternative. Instead of a continuous representation, as recently suggested by Furman and Wang [3], we increased the number of discrete selective neural populations encoding the alternatives and introduced an enhanced connectivity between neighboring pools.