Abstract
Sparse redundancy reducing codes have been proposed as efficient strategies for representing sensory stimuli. A prevailing hypothesis suggests that sensory representations shift from dense redundant codes in the periphery to selective sparse codes in cortex. We propose an alternative framework where sparseness and redundancy depend on sensory integration time scales and demonstrate that the central nucleus of the inferior colliculus (ICC) of cats encodes sound features by precise sparse spike trains. Direct comparisons with auditory cortical neurons demonstrate that ICC responses were sparse and uncorrelated as long as the spike train time scales were matched to the sensory integration time scales relevant to ICC neurons. Intriguingly, correlated spiking in the ICC was substantially lower than predicted by linear or nonlinear models and strictly observed for neurons with best frequencies within a "critical band," the hallmark of perceptual frequency resolution in mammals. This is consistent with a sparse asynchronous code throughout much of the ICC and a complementary correlation code within a critical band that may allow grouping of perceptually relevant cues.
Highlights
Mammals face a daunting task of identifying behaviorally relevant sound cues that they rely on for communication and survival
The analysis reported above suggests that sparse coding in the ICC should occur on much shorter time scales, approximately an order of magnitude faster (IT and ET of ϳ10 and 2 ms, respectively; Fig. 3c)
At the feature integration time scales relevant for the ICC neural activity was or possibly sparser than auditory cortex. These results demonstrate sparse coding on a time scale comparable to the sound integration time and suggest that sparse coding can be conserved across neural structures
Summary
Mammals face a daunting task of identifying behaviorally relevant sound cues that they rely on for communication and survival. Sparse coding and redundancy reduction are two candidate strategies that may allow for an efficient usage of neural resources (Attneave, 1954; Barlow, 1961, 2001; Levy and Baxter, 1996; Olshausen and Field, 2004). Sparse redundancy reducing codes are those in which action potentials occur infrequently and independently from neuron to neuron leading to low levels of metabolic activity and high computational efficiency. Single neurons can exhibit lifetime sparse responses in which a neuron is silent for most stimuli and produces strong activity for only a small subset of stimuli. Population sparseness places constraints on the activity pattern of a neural population by requiring that neural responses are uncorrelated from neuron to neuron and few neurons are active (Willmore et al, 2011)
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