Sparse, consistent correlation structure in the retinal population code across natural movies

Neural populations are known to adapt their coding scheme in response to different scenes, but exactly how remains a mystery, especially at the population rather than single cell level. We analyze data from the larval salamander retina responding to five different natural movies, and use maximum entropy models to characterize the population in terms of activations and couplings between neurons. We find evidence that while individual cells are adapting their response to the stimulus, the couplings, i.e. the population structure, is consistent across natural movies. In particular, we find a sparse, strongly connected backbone of couplings, which constrains the vocabulary of the neural population. We also show that this consistent coupling could provide a stable structure to allow for consistent decoding despite adaptation. Finally, we show that we can make use of this consistent structure to build models of large groups of neurons in a new, scalable way by taking advantage of the consistency of the learned couplings across small groups.