Figure 2.

(A) Prediction of a decision before the evidence is applied. In this integrate-and-fire simulation of decision-making, the decision cues were turned on at t = 2s, with ΔI = 0 (i.e., the difference between the decision variables λ₁ and λ₂ was 0). The firing rate averaged over approximately 650 winning vs. losing trials for the attractor shows that the firing rate when the attractor will win is on average higher than that for when the attractor will lose at a time that starts in this case at 300 ms before the decision cues are applied. (At t = 2s with ΔI = 0 the input firing rate on each of the 800 external input synapses onto every neuron of both of the selective attractor populations is increased from 3.00 to 3.04 spikes/s, as described in the text). The error bars show the standard deviation of the firing rate calculated across trials for the 50 ms bins to indicate the noisy operation of this decision-making system. The large standard deviations in the period after the decision cues are applied at 2 s reflect the different decision times of the network on different trials. (B) As (A), but with an expanded firing rate axis so that the difference in the firing rates of the pool that will win and of the pool that will lose can be illustrated. (C) The prediction accuracy of which pool will win from 100 ms periods of the firing of the two pools at different times before the decision cues are applied at t = 2s. The network size was 500 neurons, with 400 excitatory neurons, 400 excitatory recurrent collateral synaptic connections on each neuron, and 40 neurons in each of the two decision pools. (D) Example from a single trial of the firing rates of the four populations of neurons for a correct decision (for which ΔI = 16). From the top right the plot order is: D1 is the firing rate of the correct and winning attractor D1. Inh is the inhibitory population that uses GABA as a transmitter. NSp is the non-specific population of neurons (see Figure 1). D2 is the firing rate of the correctly losing attractor D2. (E). Rastergrams for the same trial shown in d to illustrate the probabilistic spiking of each neuron. Ten neurons from each of the four pools of neurons are shown. Each vertical line is the spike from a neuron [After Rolls and Deco (2011)].