Hierarchical novelty-familiarity coding in the early visual cortex

Abstract:
Information processing in the visual system can be viewed as driven by the statistical structure in natural stimuli due to evolutionary adaptation processes. Predictive coding, in which population feedback from higher areas carries expectations of lower-level activity (familiarity signal), whereas the population feedforward (novelty) signals carry discrepancies between the expectations and the stimuli, has been suggested to serve as an organizing principle for the entire cortical hierarchy (Lee and Mumford, 2003; Friston, 2005). Modeling results (Olshausen and Field, 1996, 1997; Rao and Ballard, 1999) have shown that the statistics of natural images can explain some receptive field (RF) properties, but no attempt had been made at coding entire images. Furthermore, the proposed connectivity appeared too slow to be biologically compatible and unrealistic RFs with only 3 small patches of 5 natural images were used. We investigate how good predictive coding actually is at coding entire natural stimuli using a natural topographic connectivity and a hierarchy of processing levels, each effectively performing fast visual recognition. Our neural network model is based on the principle of prediction error minimization, natural to expect of an organism in order for it to survive, and is neurobiologically feasible. The network is trained on 1000 natural images, following which the coding performance is evaluated on a set of 200 different images. Despite a compression factor of 4 for each level, the image reconstruction quality is quite good and strongly exceeds that of local averaging, implying that the learning results in the extraction of features characteristic of the set of natural images as a whole. With our model, we are able to reproduce several classical and extra-classical receptive-field effects in V1. Most importantly, the proposed architecture allows for the simultaneous representation of familiarity and novelty (e.g., a predator suddenly appearing in a familiar scene) at several spatial scales and could be an effective way for the visual system to combine fast hierarchical visual recognition with higher information processing, such as providing a read-out signal for attention or escape behavior, in the brain.

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