97, stimulated 8.28, INCB024360 ic50 p < 0.001). Response magnitude, however, was reduced by stimulus exposure (Figure 7E, two-way ANOVA main effect of stimulation, F[1,1502] = 59.7, p < 0.001; means in bins

1–9 naive 9.87% ± 0.16%, stimulated 8.31% ± 0.14% dF/F). As in Figure 5F, there was a significant main effect of fidelity on response strength—in both the naive and stimulated groups, the neurons that responded with the highest fidelity (ten out of ten trials) had the largest changes in fluorescence (Figure 7E, F[9,1502] = 27.95, p < 0.001). To examine the effect of passive stimulation on total network activity, we plotted the fraction of neurons in the total population as a function of their mean magnitude of fluorescent change (Figure 7F). Exposure to a nonreinforced stimulus increased total activity by 32.5% (failures included) relative to naive controls (Figure 7F, naive dF/F = 4.64% ± 0.13%, stimulated dF/F = 6.15% ± 0.24%; p < 0.0001). Taken together, our data indicate that exposure to a nonreinforced stimulus has no effect on population sparsification, but does enhance response fidelity at the expense of

response strength. The goal of this study was to determine how associative fear learning shapes the local population response to the associated conditional stimulus in primary sensory cortex. To do this, we developed a paradigm in which controlled whisker stimulation in freely exploring mice could be paired with a foot shock. Mice in which foot shock was paired with whisker stimulation learned the association C646 molecular weight for between the two stimuli and retained the memory for weeks, and possibly longer. This learning was reflected in the neural responses in the region of barrel cortex mapping the trained whisker. Fewer neurons responded to stimulation of the trained whisker, yet their responses were stronger than those in control mice in which whisker stimulation was explicitly unpaired with foot shock. The emergence of sparse population coding

and increased response strength after learning likely improves the metabolic efficiency of cortical processing. The increase in response strength improves robustness in terms of signal to noise, but is metabolically expensive. The enhanced sparsification of the population response likely compensates for the increased metabolic demand of the improved robustness, while simultaneously decreasing network crosstalk (Olshausen and Field, 2004). Supporting this view, we found that net activity—the average activity across all neurons, inclusive of failures—was reduced after associative fear learning. Importantly, these changes were unique to associative learning. In mice that were merely exposed to the CS, response fidelity increased, but the strength of a given neuron’s response decreased.