RT Journal Article
SR Electronic
T1 Efficient coding of natural scenes improves neural system identification
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.01.10.475663
DO 10.1101/2022.01.10.475663
A1 Yongrong Qiu
A1 David A. Klindt
A1 Klaudia P. Szatko
A1 Dominic Gonschorek
A1 Larissa Hoefling
A1 Timm Schubert
A1 Laura Busse
A1 Matthias Bethge
A1 Thomas Euler
YR 2022
UL http://biorxiv.org/content/early/2022/04/29/2022.01.10.475663.abstract
AB Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage normative principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the “stand-alone” system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.Competing Interest StatementThe authors have declared no competing interest.