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Centre for Neurotechnology seminar from Devika Narain

Assistant Professor, Erasmus Medical Centre

Neural dynamics underlying temporal inference

Abstract: Animals possess the ability to effortlessly and precisely time their actions even though information received from the world is often ambiguous and is inadvertently transformed as it passes through the nervous system. 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. We, however, 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. Thereafter, 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 behaviors.

Bio: Since 2020, Dr. Narain is a principal investigator and assistant professor at the Erasmus University Medical Center, Rotterdam, The Netherlands. Her group combines mathematics, machine learning, and systems neuroscience methods to investigate how neural circuits generate sophisticated timing behaviors. Before returning to the Netherlands, she was a postdoctoral fellow at MIT’s McGovern Institute for Brain Research. She was also trained at the NASA Ames Research Center in Mountain View, California, and the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. She holds degrees in engineering, cognitive science, and neuroscience and studied in Bangalore, Munich, and Amsterdam.

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