Optimal decoding of correlated neural population responses in the primate visual cortex

Abstract

Even the simplest environmental stimuli elicit responses in large populations of neurons in early sensory cortical areas. How these distributed responses are read out by subsequent processing stages to mediate behavior remains unknown. Here we used voltage-sensitive dye imaging to measure directly population responses in the primary visual cortex (V1) of monkeys performing a demanding visual detection task. We then evaluated the ability of different decoding rules to detect the target from the measured neural responses. We found that small visual targets elicit widespread responses in V1, and that response variability at distant sites is highly correlated. These correlations render most previously proposed decoding rules inefficient relative to one that uses spatially antagonistic center-surround summation. This optimal decoder consistently outperformed the monkey in the detection task, demonstrating the sensitivity of our techniques. Overall, our results suggest an unexpected role for inhibitory mechanisms in efficient decoding of neural population responses.A fundamental feature of mammalian cerebral cortex is its use of orderly topographic maps to represent sensory and motor information 1-3 . Because cortical neurons tend to respond to a broad range of stimuli 4 or movements 5 , and because there are generally multiple neurons tuned to the same range of parameters within one cortical column 6,7 , even the simplest sensory stimulus or motor response elicits activity that is distributed over a substantial population of neurons 5,8,9 . Electrophysiological studies in behaving primates suggest that perceptual and motor responses are indeed mediated by populations of neurons rather than by single neurons 10-13 . These observations raise several fundamental questions: how are stimuli and movements encoded by neural population responses, what are the optimal strategies for decoding (pooling) the population responses, and how efficient are different non-optimal pooling strategies?Several models of neural pooling in the brain have been proposed 11,14-19 . These include monitoring only the most sensitive neurons (at the extreme, a single neuron) 16 , simple averaging over the active neural population 11 and weighted summation, where the contribution of each neuron in the pool is proportional to its sensitivity 17 or proportional to the parameter value at the peak of its tuning function 14,15,18,19 .

Spatiotemporal precision and hemodynamic mechanism of optical point spreads in alert primates

“…By contrast, the spatial spread at the center of the oximetric response broadened significantly during the rebound, taking on a distinctly rounded profile [75% half-width grew from 0.48 (0.08) to 1.15 (0.08) mm, an increase of a factor of 2.4; P ϭ 9e-6, n ϭ 11]. It is noteworthy that the cross-sectional shape and spatial extent of the volumetric point spread closely matches those observed with voltage sensitive dyes (28,29). This finding suggests that the volumetric response, is not only distinct from the oximetric rebound, but is likely a better neuroimaging signal tightly coupled to underlying neural activity.…”

Asymmetry of Anticipatory Activity in Visual Cortex Predicts the Locus of Attention and Perception

“…First, this relative activity measure improved prediction of the locus of attention compared with measurement of activity for only a single location, mostly by removing a signal that was highly correlated across regions representing different locations (Gold and Shadlen, 2001;Chen et al, 2006;Fox et al, 2006). Second, only the relative activity between attended and unattended locations predicted whether subjects would correctly perceive objects that subsequently appeared at the attended location.…”

“…Activity should be compared between portions of cortex with opposite (attend left vs attend right) preferences, because this will constructively combine their preferences. Theoretical work indicates that when taking the difference in activity between portions of cortex with opposite preferences, information content increases as the degree of positive correlation between those portions increases (Chen et al, 2006). We examined the correlation structure, therefore, across six subdivisions of visual cortex representing different portions of the visual field.…”

Excitatory effects of human immunodeficiency virus 1 Tat on cultured rat cerebral cortical neurons

“…Optical imaging using voltage sensor probes has proven to be a reliable approach in monitoring membrane potential changes in neurons (Ebner and Chen, 1995;Kunkler et al, 2005;Chen et al, 2006). Briefly, the cells were incubated for 30 min in HBSS containing 0.5 mM DiSBAC 4 (3).…”