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Representing stimulus motion with waves in adaptive neural fields

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Abstract

Traveling waves of neural activity emerge in cortical networks both spontaneously and in response to stimuli. The spatiotemporal structure of waves can indicate the information they encode and the physiological processes that sustain them. Here, we investigate the stimulus-response relationships of traveling waves emerging in adaptive neural fields as a model of visual motion processing. Neural field equations model the activity of cortical tissue as a continuum excitable medium, and adaptive processes provide negative feedback, generating localized activity patterns. Synaptic connectivity in our model is described by an integral kernel that weakens dynamically due to activity-dependent synaptic depression, leading to marginally stable traveling fronts (with attenuated backs) or pulses of a fixed speed. Our analysis quantifies how weak stimuli shift the relative position of these waves over time, characterized by a wave response function we obtain perturbatively. Persistent and continuously visible stimuli model moving visual objects. Intermittent flashes that hop across visual space can produce the experience of smooth apparent visual motion. Entrainment of waves to both kinds of moving stimuli are well characterized by our theory and numerical simulations, providing a mechanistic description of the perception of visual motion.

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Code availability

All software for carrying out model simulations and for generating figures can be found at the following code repository: https://github.com/shawsa/neural-field-synaptic-depression.

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Acknowledgements

This work was funded by NIH BRAIN 1R01EB029847 and NSF DMS-2207700.

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  1. Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA

    Sage Shaw & Zachary P Kilpatrick

  2. Institute for Cognitive Sciences, University of Colorado Boulder, Boulder, CO, USA

    Zachary P Kilpatrick

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Shaw, S., Kilpatrick, Z.P. Representing stimulus motion with waves in adaptive neural fields. J Comput Neurosci 52, 145–164 (2024). https://doi.org/10.1007/s10827-024-00869-z

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