Using natural stimuli to estimate receptive fields in neurons that employ sparse coding

Abstract

Listen

Translate article

Event Abstract Back to Event Using natural stimuli to estimate receptive fields in neurons that employ sparse coding Guy Isely1*, Christopher Hillar2 and Friedrich Sommer2 1 University of California at Berkeley, Redwood Center for Theoretical Neuroscience, United States 2 University of California, United States Estimating receptive fields with natural stimuli has become an important approach for analyzing response properties of sensory neurons. It has been noted that natural stimuli contain correlations that have to be accounted for in analyzing receptive field properties. The appropriate correction has been worked out for the linear coding model [4] and there are methods to estimate receptive fields if the assumed coding model contains a pointwise nonlinearity [3]. Here we ask the question how one can estimate receptive fields if the underlying coding process is sparse coding, a model for sensory neurons that has been very successful in explaining the emergence of experimentally observed response properties [1, 2]. To explore this question we perform recording experiments on the sparse coding model, probing its activity with stimulus samples and determining the receptive fields of its neurons from stimulus response pairs. Naive reverse correlation with natural stimulus probes finds receptive fields that are similar in structure to the feed forward connections in the model. However, correcting for the autocorrelations in these probes corrupts that structure with high frequency noise. The linear generative mathematics inherent in the sparse coding model can explain why naive reverse correlation without the correction for stimulus autocorrelations provides a better estimate of the receptive field structure of sparse coding neurons. In particular, a mathematical analysis of sparse coding networks suggests a correction for the autocorrelations in the responses of their neurons rather than a correction for stimulus autocorrelations. Since these response autocorrelations are relatively small for a sparse coding network sampled with a sufficiently large natural stimulus ensemble, uncorrected naive reverse correlation with natural stimulus probes is a good approximation. More generally, an implication of our analysis is that the best linear predictor of a neuron’s firing rate is potentially not the best estimate of the spatial structure of connections that drive its stimulus response. Thus, surprisingly, under the assumption that the neurons one records from are performing sparse coding or similar computations involving later inhibition, naive reverse correlation may provide a better estimate of the structure their receptive fields than seemingly more rigorous techniques that correct for stimulus autocorrelations.