The Sparseness of Neuronal Responses in Ferret Primary Visual Cortex

Abstract

Various arguments suggest that neuronal coding of natural sensory stimuli should be sparse (i.e., individual neurons should respond rarely but should respond reliably). We examined sparseness of visual cortical neurons in anesthetized ferret to flashed natural scenes. Response behavior differed widely between neurons. The median firing rate of 4.1 impulses per second was slightly higher than predicted from consideration of metabolic load. Thirteen percent of neurons (12 of 89) responded to Ͻ5% of the images, but one-half responded to Ͼ25% of images. Multivariate analysis of the range of sparseness values showed that 67% of the variance was accounted for by differing response patterns to moving gratings. Repeat presentation of images showed that response variance for natural images exaggerated sparseness measures; variance was scaled with mean response, but with a lower Fano factor than for the responses to moving gratings. This response variability and the "soft" sparse responses (Rehn and Sommer, 2007) raise the question of what constitutes a reliable neuronal response and imply parallel signaling by multiple neurons. We investigated whether the temporal structure of responses might be reliable enough to give additional information about natural scenes. Poststimulus time histogram shape was similar for "strong" and "weak" stimuli, with no systematic change in first-spike latency with stimulus strength. The variance of first-spike latency for repeat presentations of the same image was greater than the latency variance between images. In general, responses to flashed natural scenes do not seem compatible with a sparse encoding in which neurons fire rarely but reliably.

Natural Movies Evoke Spike Trains with Low Spike Time Variability in Cat Primary Visual Cortex

“…We showed that the responses of striate cortical cells to natural scenes exhibited lower spike time variability than responses to simple grating stimuli (Tolhurst et al, 2009), and this finding was consistent after matching mean spike counts between the two types of responses. The fact that spike time variability was lower for movie responses than for grating responses in windows with the same mean spike count, is consistent with the current evidence that natural stimuli are sparsely encoded by cortical cells Gallant, 2000, 2002;Kayser et al, 2004;Yen et al, 2007).…”

“…In fact, the Fano factor (FF), the ratio of the variance of the spike counts across repetitions to the mean spike count, was often found to be higher than that of a random Poisson process, which has a FF of 1. The variability in spike times has also been shown to be quite high (Tomko and Crapper, 1974;Shadlen and Newsome, 1998;McAdams and Maunsell, 1999;Tolhurst et al, 2009) (but see Richmond and Optican, 1990;McClurkin et al, 1991;Bair and Koch, 1996;Buracas et al, 1998;Nawrot et al, 2008;Benedetti et al, 2009;Maimon and Assad, 2009). As a result, neuronal responses have often been modeled using a nonuniform Poisson process, in which the probability of a spike or the firing rate would be constant for a short time window, and then change to a different rate as a function of the stimulus (Victor and Purpura, 1996;Berry and Meister, 1998;Oram et al, 1999).…”

Anatomical Constraints on Lateral Competition in Columnar Cortical Architectures

“…Within a single column, the machinery required for competition-recurrent excitatory and inhibitory connections-is readily available without making unreasonable assumptions about the cortical architecture. Indeed, responses of neighboring neurons in cat visual cortex are highly decorrelated, over and above what is expected from differences in their respective receptive fields (Yen, Baker, & Gray, 2007;Tolhurst, Smyth, & Thompson, 2009;Ecker et al, 2010;Martin & Schröder, 2013). This surprising lack of correlation between neurons with similar orientation preference and similar retinotopic location could occur through local competition between neurons within a cortical column.…”

Singing-Related Neural Activity Distinguishes Four Classes of Putative Striatal Neurons in the Songbird Basal Ganglia

“…We used an "entropy" measure of sparseness (Lehky et al 2005;Tolhurst et al 2009) to quantify how selective neuronal activity was for specific times in the song. For each neuron, we calculated syllable-aligned rate histograms for every syllable (10-ms bin size as described in the preceding text), resulting in a total of N bins.…”