Dataset Information

Projection-Specific Visual Feature Encoding by Layer 5 Cortical Subnetworks.

ABSTRACT: Primary neocortical sensory areas act as central hubs, distributing afferent information to numerous cortical and subcortical structures. However, it remains unclear whether each downstream target receives a distinct version of sensory information. We used in vivo calcium imaging combined with retrograde tracing to monitor visual response properties of three distinct subpopulations of projection neurons in primary visual cortex. Although there is overlap across the groups, on average, corticotectal (CT) cells exhibit lower contrast thresholds and broader tuning for orientation and spatial frequency in comparison to corticostriatal (CS) cells, whereas corticocortical (CC) cells have intermediate properties. Noise correlational analyses support the hypothesis that CT cells integrate information across diverse layer 5 populations, whereas CS and CC cells form more selectively interconnected groups. Overall, our findings demonstrate the existence of functional subnetworks within layer 5 that may differentially route visual information to behaviorally relevant downstream targets.

SUBMITTER: Lur G

PROVIDER: S-EPMC4805451 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Publications

Projection-Specific Visual Feature Encoding by Layer 5 Cortical Subnetworks.

Lur Gyorgy G Vinck Martin A MA Tang Lan L Cardin Jessica A JA Higley Michael J MJ

Cell reports 20160310 11

Primary neocortical sensory areas act as central hubs, distributing afferent information to numerous cortical and subcortical structures. However, it remains unclear whether each downstream target receives a distinct version of sensory information. We used in vivo calcium imaging combined with retrograde tracing to monitor visual response properties of three distinct subpopulations of projection neurons in primary visual cortex. Although there is overlap across the groups, on average, corticotec ...[more]

PMID: 26972011

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Project description:Segmenting objects from each other and their background is critical for vision. The speed at which objects move provides a salient cue for segmentation. However, how the visual system represents and differentiates multiple speeds is largely unknown. Here we investigated the neural encoding of multiple speeds of overlapping stimuli in the primate visual cortex. We first characterized the perceptual capacity of human and monkey subjects to segment spatially overlapping stimuli moving at different speeds. We then determined how neurons in the motion-sensitive, middle-temporal (MT) cortex of macaque monkeys encode multiple speeds. We made a novel finding that the responses of MT neurons to two speeds of overlapping stimuli showed a robust bias toward the faster speed component when both speeds were slow (≤ 20°/s). The faster-speed bias occurred even when a neuron had a slow preferred speed and responded more strongly to the slower component than the faster component when presented alone. The faster-speed bias emerged very early in neuronal response and was robust over time and to manipulations of motion direction and attention. As the stimulus speed increased, the faster-speed bias changed to response averaging. Our finding can be explained by a modified divisive normalization model, in which the weights for the speed components are proportional to the responses of a population of neurons elicited by the individual speeds. Our results suggest that the neuron population, referred to as the weighting pool, includes neurons that have a broad range of speed preferences. As a result, the response weights for the speed components are determined by the stimulus speeds and invariant to the speed preferences of individual neurons. Our findings help to define the neural encoding rule of multiple stimuli and provide new insight into the underlying neural mechanisms. The faster-speed bias would benefit behavioral tasks such as figure-ground segregation if figural objects tend to move faster than the background in the natural environment.

Dataset Information

Projection-Specific Visual Feature Encoding by Layer 5 Cortical Subnetworks.

Publications

Projection-Specific Visual Feature Encoding by Layer 5 Cortical Subnetworks.

Similar Datasets

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