A sparse coding model with imperfect feed-forward circuitry

Abstract

Event Abstract Back to Event A sparse coding model with imperfect feed-forward circuitry Although simple receptive fields are approximately outlined by a small set of highly specific geniculocortical afferents (Reid and Alonso, 1995, Alonso et al. 2001), the feed-forward wiring is often only an imperfect match of the receptive field, suggesting that neural response properties are also shaped by intracortical processing. While existing sparse coding network models successfully explain the emergence of simple receptive fields, they predict that receptive fields are shaped almost exclusively by feed-forward connections, e.g., (Olshausen and Field, 1996, Rehn and Sommer, 2007). Here we ask if models principled by efficient coding can reproduce response properties from real neurons even though the feedforward weights in the model match the measured receptive fields only imperfectly. We present a novel neural network model for sparse coding based on compressed sensing, a computational principle recently developed in engineering for data compression (Candès and Romberg, 2006). The model exhibits smooth and more realistic simple cell receptive fields than its "imperfect" feed-forward connections would predict, compare the two panel of Fig. 1 of the supplement. Thus, the model demonstrates that efficient coding schemes exist in which feedback connections are critical for shaping the receptive fields. We use simulation experiments with our model to characterize deviations between feed-forward circuitry and measured receptive fields.