Paper Info
Title
A mechanistic model of early sensory processing based on subtracting sparse representations.
Abstract
Early stages of sensory systems face the challenge of compressing information from numerous receptors onto a much smaller number of projection neurons, a so called communication bottleneck. To make more efficient use of limited bandwidth, compression may be achieved using predictive coding, whereby predictable, or redundant, components of the stimulus are removed. In the case of the retina, Srinivasan et al. (1982) suggested that feedforward inhibitory connections subtracting a linear prediction generated from nearby receptors implement such compression, resulting in biphasic center-surround receptive fields. However, feedback inhibitory circuits are common in early sensory circuits and furthermore their dynamics may be nonlinear. Can such circuits implement predictive coding as well? Here, solving the transient dynamics of nonlinear reciprocal feedback circuits through analogy to a signal-processing algorithm called linearized Bregman iteration we show that nonlinear predictive coding can be implemented in an inhibitory feedback circuit. In response to a step stimulus, interneuron activity in time constructs progressively less sparse but more accurate representations of the stimulus, a temporally evolving prediction. This analysis provides a powerful theoretical framework to interpret and understand the dynamics of early sensory processing in a variety of physiological experiments and yields novel predictions regarding the relation between activity and stimulus statistics.
Year
Venue
Field
2012
NIPS
Receptive field,Nonlinear system,Computer science,Linear prediction,Bandwidth (signal processing),Artificial intelligence,Stimulus (physiology),Sensory system,Machine learning,Sensory processing,Feed forward
DocType
Citations 
PageRank 
Conference
5
0.58
References 
Authors
2
3
Name
Order
Citations
PageRank
Druckmann, Shaul1324.51
Hu Tao2709.94
Dmitri B. Chklovskii312725.69