Description:

Title: A Network Mechanism for Perceptual Learning

Abstract: Organisms continually tune their perceptual systems to the features they encounter in their environment. We have studied how this experience reorganizes the synaptic connectivity of neurons in the olfactory (piriform) cortex of the mouse. We developed an approach to measure synaptic connectivity in vivo, training a deep convolutional network to reliably identify monosynaptic connections from the spike-time cross-correlograms of 4.4 million single-unit pairs. This revealed that excitatory piriform neurons that respond similarly to each other are more likely to be connected. We asked whether this like-to-like connectivity was modified by experience but found no effect. Instead, we found a pronounced effect of experience on the connectivity of inhibitory interneurons. Following repeated encounters with a set of odorants, inhibitory neurons that responded differentially to these stimuli both received and formed a high degree of synaptic connections with the cortical network. The experience-dependent organization of inhibitory neuron connectivity was independent of the tuning of either their pre- or their postsynaptic partners. These results suggest the existence of a cell-intrinsic, non-Hebbian plasticity mechanism that depends only on the odor tuning of the inhibitory interneuron. A computational model of this plasticity mechanism predicts that it increases the dimensionality of the entire network’s responses to familiar stimuli, thereby enhancing their discriminability. We confirmed that this network-level property is present in physiological measurements, which showed increased dimensionality and separability of the evoked responses to familiar versus novel odorants. Thus a cell-intrinsic plasticity mechanism acting on inhibitory interneurons may implement a key component of perceptual learning: enhancing an organism’s discrimination of the features particular to its environment.

Learn more about Professor Muscinelli’s research here: https://samuelmuscinelli.com/

Sam Muscinelli is an assistant professor of Neurobiology and part of the Grossman Center for Quantitative Biology and Human Behavior at the University of Chicago. He is interested in how the anatomy of brain circuits both governs learning and adapts to it. His current research centers on two main questions: How does neural connectivity give rise to patterns of activity that support learning? What can we say about the plasticity rules that enable the brain to adapt to changes and goals in the environment? To tackle these questions, he leverages recent advances in our ability to measure synaptic connectivity, and combines analytical theory, machine learning, and data analysis, working closely with experimental collaborators.

The NSF-Simons National Institute for Theory and Mathematics in Biology Seminar Series aims to bring together a mix of mathematicians and biologists to foster discussion and collaboration between the two fields. The seminar series will take place on Fridays from 10am - 11am at the NITMB in the John Hancock Center in downtown Chicago. There will be both an in-person and virtual component.
 
More information: https://www.nitmb.org/nitmb-seminar-series