Figure 2.

Contextual Input-Specific Gain Shapes Both Cortical and Thalamic Responses

(A) Scatterplot of generalization performance for the CGF and STRF models in cortex and thalamus measured by cross-validation; inset shows histogram of differences in favor of the CGF model (left) or STRF model (right). Black dashed lines indicate equal performance. The CGF model almost always generalizes more accurately than the STRF, showing that contextual input-specific gain plays a substantial role in shaping responses in both brain structures.

(B and C) Predictive power extrapolations for CGF model (bright colors) and STRF model (dull, greyed colors) in cortex (B) and thalamus (C). Filled circles and solid lines indicate generalization performance on test data, assessed by cross-validation; open circles and dashed lines show predictive performance on training data. In the zero-noise limit, extrapolated intercepts (indicated on the left) are all higher for the CGF model. See Supplemental Experimental Procedures for further explanation.

(D) Effective input-specific gains and predictive advantage. Each dot and horizontal bar indicates the median and interquartile range of the distribution of effective input-specific gains across all points in the stimulus for one neuron, obtained by convolving the spectrogram of the DRC stimulus with the neuron’s CGF (see also Figure 3). Median input-specific gains tend to be substantially smaller than 1 and interquartile ranges are often large, indicating that effects of local acoustic context are predominantly suppressive but can vary substantially across spectrotemporal points within the DRC stimulus.