Abstract
Visual recognition is a computational challenge that is thought to occur via efficient coding. An important concept is sparseness, a measure of coding efficiency. The prevailing view is that sparseness supports efficiency by minimizing redundancy and correlations in spiking populations. Yet, we recently reported that “choristers”, neurons that behave more similarly (have correlated stimulus preferences and spontaneous coincident spiking), carry more generalizable object information than uncorrelated neurons (“soloists”) in macaque inferior temporal (IT) cortex. The rarity of choristers (as low as 6% of IT neurons) indicates that they were likely missed in previous studies. Here, we report that correlation strength is distinct from sparseness (choristers are not simply broadly tuned neurons), that choristers are located in non-granular output layers, and that correlated activity predicts human visual search efficiency. These counterintuitive results suggest that a redundant correlational structure supports efficient processing and behavior.
Highlights
Visual recognition engages neural mechanisms that are essential to our ability to learn and process complex information (Poggio and Bizzi, 2004)
We report that correlation strength is distinct from sparseness, that choristers are located in non-granular output layers, and that correlated activity predicts human visual search efficiency
Our results suggest that correlated activity contributes to efficient coding and human visual search efficiency
Summary
Visual recognition engages neural mechanisms that are essential to our ability to learn and process complex information (Poggio and Bizzi, 2004). We recently reported (Lin et al, 2014) that in macaque IT, correlated neurons “choristers” (Kenet et al, 2005; Carandini, 2014), neurons that have similar stimulus tuning and coincident spike timing, even during spontaneous activity, carry more generalizable object information than uncorrelated neurons (“soloists”). This surprising result hints that, counterintuitively, correlation supports efficient coding and that current thinking focused on sparsening, decorrelation, and denoising may be flawed
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