Cortical Visual Processing for Navigation

How does cortical circuitry perform the visual scene analysis needed to support navigation through the environment?

Most studies of central visual processing are focused on detection or discrimination of specific features of simple artificial stimuli (e.g., orientation, direction of motion, object identity). However, navigation through the environment involves a very different set of computational goals, such as identifying landmarks and using optic flow to avoid obstacles. Furthermore, these computations occur under a very different stimulus regime, with the animal actively sampling a complex and continually moving sensory scene.

Our goal is to determine how the brain extracts relevant visual features from the rich, dynamic visual input that typifies active exploration, and develop (deep) predictive models of brain activity based on visual input and several behavioral variables. The data includes one-of-a-kind measures of neural activity in mice navigating through real-world and virtual environments, collected using 2-photon imaging and electrophysiology by our collaborators Spencer Smith, Michael Goard, and Cris Niell.

The results of this project will provide knowledge about normal visual function and insights for treating impaired vision via prosthetic or assistive devices.

Project Team

Project Leads:

Amirali Vahid

Postdoctoral Scholar

Yuchen Hou

PhD Student

Project Affiliate:

Aiwen Xu

PhD Candidate

Principal Investigators:

Michael Beyeler

Assistant Professor

Michael Goard

Assistant Professor

Cris Niell

Associate Professor
University of Oregon

Spencer Smith

Associate Professor

Consultant:

Bingni W. Brunton

Associate Professor
University of Washington

Project Funding

R01NS121919: Cortical visual processing for navigation
PI: Spencer Smith (UCSB)

April 2021 - March 2024
National Institute of Neurological Disorders and Stroke (NINDS)
National Institutes of Health (NIH)

Publications

We developed a spiking neural network model that showed MSTd-like response properties can emerge from evolving spike-timing dependent plasticity with homeostatic synaptic scaling (STDP-H) parameters of the connections between area MT and MSTd.

Using a dimensionality reduction technique known as non-negative matrix factorization, we found that a variety of medial superior temporal (MSTd) neural response properties could be derived from MT-like input features. The responses that emerge from this technique, such as 3D translation and rotation selectivity, spiral tuning, and heading …

We present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the …

We present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and …

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