Spikes in the Auditory Forebrain: Surprise, Not Intensity Patrick Gill1, and Frederic Theunissen1 1University of California at Berkeley High-level sensory neurons encoding natural stimuli are not well modelled by linear filters operating on the time-varying stimulus intensity1. Nonlinear neural models predict marginally better, but they often fail to advance our understanding of the neural code, since they can be difficult to interpret2. We modelled auditory neurons in Caudal Mesopallium (CM), a secondary forebrain area of the male zebra finch, with linear filters convolved not with stimulus intensity, but with stimulus surprise. Surprise was quantified by taking the logarithm of how probable the stimulus was given its recent history, and by separating louder- and quieter-than-expected stimulus features, as follows:

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Surprise ="log(P(S |D)) + log(P(SML |D)) if S < SML , 0 otherwise

"log(P(S |D)) + log(P(SML |D)) if S > SML , 0 otherwise

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( ( (1)

S is the stimulus now, D is the stimulus’ recent history, and SML is the most likely stimulus given the recent history. Representing the stimulus using Equation 1 instead of a spectrogram, predictions of neural responses to conspecific song improved by an astounding 57%. This improvement could be attributed neither to the gain control mechanisms we know to be present in the cochlea, nor to the separation of louder- and quieter-than-expected events. We found the coding scheme CM uses for a type of synthetic auditory noise can be better understood if we assume CM is tuned to represent song even when noise is being played. Our results are easily interpretable: a spike in CM indicates a specific degree of surprise given the expectation of hearing song. Acknowledgments We thank S. M. N. Woolley and T. Fremouw for data acquisition. This work was supported by NIH grants MH59189 and DC07293. References [1] Linearity of cortical receptive fields measured with natural sounds. C. Machens, M. Wehr and A. Zador, Journal of Neuroscience 24(5): 1089-1100, February 2004. [2] Nonlinear V1 responses to natural scenes revealed by neural network analysis. R. Prenger, M. Wu, S. David and J. Gallant, Neural Networks 17: 663–679, March 2004.

Cosyne 2007 Sunday AM, Talk

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