Stimulus complexity shapes response correlations in primary visual cortex
| Autor: | Wolf Singer, Johanna Klon-Lipok, Andreea Lazar, Liane Klein, Mihály Bányai, Marcell Stippinger, Gergő Orbán |
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| Rok vydání: | 2019 |
| Předmět: |
Male
Visual perception genetic structures Computer science Stimulus specificity Action Potentials Sensory system Stimulus (physiology) Spike count hierarchical perception Stimulus Complexity medicine Animals visual cortex spike count correlations Neurons Multidisciplinary Biological Sciences Probabilistic inference Macaca mulatta stomatognathic diseases Visual cortex medicine.anatomical_structure nervous system PNAS Plus Visual Perception Female Neuroscience Photic Stimulation |
| Zdroj: | Proceedings of the National Academy of Sciences of the United States of America |
| Popis: | Significance Whether the population activity in neuronal networks can be understood as the sum of individual activities or neurons jointly determine the state of populations is a fundamental question of neuroscience. Spike count correlations reflect coordination between pairs of neurons and therefore can be regarded as a signature of joint computations. So far, a majority of experimental and theoretical analyses considered these correlations noise and ignored stimulus-dependent aspects. Based on theoretical considerations, we argue that spike count correlations are stimulus dependent and variations in their structure can be predicted by stimulus content. Recording the activity of neurons from primary visual cortex in task-engaged monkeys, we confirm these predictions. These results provide insight into the computations performed by populations of cortical neurons. Spike count correlations (SCCs) are ubiquitous in sensory cortices, are characterized by rich structure, and arise from structured internal dynamics. However, most theories of visual perception treat contributions of neurons to the representation of stimuli independently and focus on mean responses. Here, we argue that, in a functional model of visual perception, featuring probabilistic inference over a hierarchy of features, inferences about high-level features modulate inferences about low-level features ultimately introducing structured internal dynamics and patterns in SCCs. Specifically, high-level inferences for complex stimuli establish the local context in which neurons in the primary visual cortex (V1) interpret stimuli. Since the local context differentially affects multiple neurons, this conjecture predicts specific modulations in the fine structure of SCCs as stimulus identity and, more importantly, stimulus complexity varies. We designed experiments with natural and synthetic stimuli to measure the fine structure of SCCs in V1 of awake behaving macaques and assessed their dependence on stimulus identity and stimulus statistics. We show that the fine structure of SCCs is specific to the identity of natural stimuli and changes in SCCs are independent of changes in response mean. Critically, we demonstrate that stimulus specificity of SCCs in V1 can be directly manipulated by altering the amount of high-order structure in synthetic stimuli. Finally, we show that simple phenomenological models of V1 activity cannot account for the observed SCC patterns and conclude that the stimulus dependence of SCCs is a natural consequence of structured internal dynamics in a hierarchical probabilistic model of natural images. |
| Databáze: | OpenAIRE |
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