With this motif, the feedback loop involves only the inhibitory units and the two synapses that connect them. In contrast, all other feedback architectures involve additional units and synapses in the feedback loop. We studied the consequences of structural complexity of the feedback motif on the ability

of the model to compute steady-state responses to competing stimuli rapidly and reliably. We compared the performance of the reciprocal inhibition of feedforward lateral inhibition motif (Figure 4, circuit 2) with that of the next most structurally simple motif: feedback lateral inhibition by output units (Figure 7A, circuit 3). The parameter values for the circuit 2 model were chosen to be the same as those in Figure 5E. The parameter values for the circuit 3 model were chosen such that the circuit yielded output unit responses find protocol selleck inhibitor at steady state that were nearly identical to those from the circuit 2 model (Figures S5A–S5D). The quality of the match between the responses of the two circuits was particularly sensitive to the values of the parameters for circuit 3, with the best match occurring over a narrow range of values (Figures S5E–S5J). We measured calculation speed as the settling time, defined as the first time step after which responses did not change any further (Experimental Procedures). The time courses of the responses from the two models, calculated

for an RF stimulus of strength second of 9°/s and a competitor strength of 8°/s (relative strength = 1°/s), demonstrated that circuit 2 settled faster than circuit 3 (Figure 7E). This finding held true for all relative stimulus strength values (Figure 7F). Both models exhibited longer settling times as the relative strength between the competing stimuli decreased, consistent with the experimental observation that difficult discriminations take longer to resolve (Gold and Shadlen, 2007).

We assessed the reliability of the calculation as the consistency of the steady-state response. Gaussian noise was introduced into the calculation of the response for each unit at each time step. Consistency was quantified by calculating the Fano factor (Experimental Procedures), a metric that is inversely related to response consistency. The distribution of Fano factors at steady state was estimated using Monte Carlo analyses (Experimental Procedures). Comparison of the Fano factors from the two models for an RF stimulus of strength of 9°/s and a competitor strength of 8°/s (relative strength = 1°/s) showed that circuit 2 produced less variability (smaller Fano factor) than circuit 3 (Figure 7G; average Fano factors were 0.71 ± 0.01 and 0.78 ± 0.01, respectively; p < 10−4, rank-sum test). Circuit 2 exhibited superior reliability for all values of the RF stimulus from 1°/s to 9°/s (competitor = 8°/s), with the reduction in Fano factor being substantial (approximately 75%) when the RF stimulus was weaker or as strong as the competitor (Figure 7H).