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Tuesday, July 21 • 4:45pm - 5:15pm
W3 S3: Neural Circuits Underlying Bayesian Inference in Time Perception

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Animals possess the ability to effortlessly and precisely time their actions even though information received from the world is often ambiguous, is corrupted by the influence of noise and is inadvertently transformed as it traverses through neural circuitry. With such uncertainty pervading through our nervous systems, we could expect that much of human and animal behavior relies on inference that incorporates an important additional source of information, prior knowledge of the environment. These concepts have long been studied under the framework of Bayesian inference with substantial corroboration over the last decade that human time perception is consistent with such models. However, we know little about the neural mechanisms that enable Bayesian signatures to emerge in temporal perception. I will present our work on three facets of this problem, how Bayesian estimates are encoded in neural populations, how these estimates are used to generate time intervals and how prior knowledge for these tasks is acquired and optimized by neural circuits. We trained monkeys to perform an interval reproduction task and found their behavior to be consistent with Bayesian inference. Using insights from electrophysiology and in silico models, we propose a mechanism by which cortical populations encode Bayesian estimates and utilize them to generate time intervals. In the second part of my talk, I will present a circuit model for how temporal priors can be acquired by cerebellar machinery leading to estimates consistent with Bayesian theory. Based on electrophysiology and anatomy experiments in rodents, I will provide some support for this model. Overall, these findings attempt to bridge insights from normative frameworks of Bayesian inference with potential neural implementations for the acquisition, estimation and production of timing behaviours.

Speakers
DN

Devika Narain

Assistant Professor, Erasmus MC


Tuesday July 21, 2020 4:45pm - 5:15pm CEST
Crowdcast (W03)