Connectivity inference in visual cortex and characterization of contrast-suppressed cells

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• Simon Renner, Ludwig-Maximilians-Universität München
• Friday 26 November 2021, 14:00-15:00
• Online on Zoom.

If you have a question about this talk, please contact Yul Kang.

Connectivity inference in visual cortex

Model-based efforts to infer cortical connectivity from neuronal activity are challenged by overwhelming parameter spaces, biological constraints, and consideration of external inputs. Here, we develop an inference procedure of connectivity in visual cortex using the stabilized supralinear network (SSN) and apply our method to in-vivo recordings from mouse dorsolateral geniculate nucleus (dLGN) and primary visual cortex (V1). We find a consistent ordering of connectivity strengths and offer predictions for inhibitory-to-inhibitory connectivity in V1.

Characterization of contrast-suppressed cells

Since the beginnings of visual neuroscience, neurons that respond to certain visual stimuli with an increase in firing have been studied intensely. However, there exists a significant fraction of neurons that are suppressed by visual stimuli. These suppressed-by-contrast (SbC) neurons have been found from the retina to visual cortex and have recently been related to specific interneuron types and behavioral state. Here, we investigate intrinsic and stimulus response properties of SbC neurons in mouse dLGN and V1. We find SbC neurons to have unique firing patterns, longer response latencies, and to be tuned to stimulus orientation and spatial phase.

Zoom information: https://us02web.zoom.us/j/84958321096?pwd=dFpsYnpJYWVNeHlJbEFKbW1OTzFiQT09

Meeting ID: 849 5832 1096

Passcode: 506576

This talk is part of the Computational Neuroscience series.

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