Optimal Sensory Coding Theories for Neural Systems Under Biophysical Constraints

Abstract

The natural stimuli that biological vision must use to understand the world are extremely complex. Recent advances in machine learning have shown that low-dimensional geometric models (e.g., sparsity, manifolds) can capture much of the structure in complex natural images. I will describe our work building efficient neural coding models that optimally exploit this structure. These results incorporate the constraints of biophysical systems and the physical world by drawing on mathematical tools such as dynamical systems, optimization, unsupervised learning, randomized dimensionality reduction, and manifold learning. These results show that incorporating natural constraints can lead to theoretical models that account for a wide range of observed phenomenon, including complex response properties of individual neurons, architectural features of the network (e.g., makeup of different cell types), and reported perceptual results from human psychophysical experiments.