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Ralph Peterson
Datta Lab, HMS
Learning structure in mouse behavior using Motion Sequencing (MoSeq)

Abstract: Understanding how the brain governs behavior is a fundamental goal of modern neuroscience; however, our ability measure behavior lags far behind our ability to manipulate genes involved in typical brain function, or measure neural activity. Here, we will learn about the general workflow of a novel machine vision and learning technique — called Motion Sequencing (MoSeq) — designed to objectively quantify 3D mouse behavior on a sub-second timescale. Moreover, we will discuss the technique’s current and future uses in addressing questions in behavioral neuroscience.

Jeffrey Markowitz
Datta Lab, Sabatini Lab, HMS
Using machine learning to understand how the brain implements moment to moment action selection

Abstract: Many naturalistic behaviors are built from stereotyped, modular components that are flexibly arranged to form sequences. Although striatal circuits in the brain have been implicated in action selection and implementation, the neural mechanisms that compose behavior in unrestrained animals are not well understood. I will discuss recently published work on simultaneous recording of neural activity in the direct and indirect pathways of dorsolateral striatum (DLS) and monitoring of 3D pose dynamics as mice spontaneously express action sequences. These experiments demonstrate that DLS neurons systematically encode information about the identity and sequential ordering of stereotyped sub-second 3D behavioral motifs; this encoding is facilitated by fast-timescale decorrelations between the direct and indirect pathways. Furthermore, perturbing the DLS prevents appropriate sequence assembly during both exploratory or odor-evoked behaviors. By characterizing naturalistic behavior at neural timescales, these experiments identify a code for 3D pose dynamics built from complementary pathway dynamics, support a role for DLS in constructing meaningful behavioral sequences, and suggest models for how actions are sculpted over time. I will also discuss unpublished work on using closed-loop recognition of behavioral motifs to study the neural implementation of reinforcement learning.