The primary visual cortex (V1) processes complex mixtures oforientations to build neural representations of our visual en-vironment. It remains unclear how V1 adapts to the highlyvolatile distributions of orientations found in natural images.We used naturalistic stimuli and measured the response of V1neurons to orientation distributions of varying bandwidth. Al-though broad distributions decreased single neuron tuning, aneurally plausible decoder could robustly retrieve the orienta-tions of stimuli from the population activity. Furthermore, weshowed that V1 co-encodes orientation and its precision, whichenhances population discriminatory performances. This inter-nal representation is mediated by temporally distinct neuralcodes, supporting a precision-based description of the neuronalmessage passing in the visual cortex.
A study was conducted to determine stable cortical contrast response functions (CRFs) accurately and repeatedly in the shortest possible experimentation time. The method consisted of searching for experimental temporal aspects (number and duration of trials and number and distribution of contrasts used) with a model based on inhomogeneous Poisson spike trains to varying contrast levels. The set of values providing both short experimental duration and maximizing fit of the CRFs were saved, and then tested on cats’ visual cortical neurons. Our analysis revealed that 4 sets of parameters with less or equal to 6 experimental visual contrasts satisfied our premise of obtaining good CRFs’ performance in a short recording period, in which the number of trials seems to be the experimental condition that stabilizes the fit.
Our daily endeavors occur in a complex visual environment, whose intrinsic variability challenges the way we integrate information to make decisions. By processing myriads of parallel sensory inputs, our brain is theoretically able to compute the variance of its environment, a cue known to guide our behavior. Yet, the neurobiological and computational basis of such variance computations are still poorly understood. Here, we quantify the dynamics of sensory variance modulations of cat primary visual cortex neurons. We report two archetypal neuronal responses, one of which is resilient to changes in variance and co-encodes the sensory feature and its variance, improving the population encoding of orientation. The existence of these variance-specific responses can be accounted for by a model of intracortical recurrent connectivity. We thus propose that local recurrent circuits process uncertainty as a generic computation, advancing our understanding of how the brain handles naturalistic inputs.
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