Biology subjects

Athalye, V. R.

Publications and source records attributed to Athalye, V. R..

3 recordsLinked to original sources

bioRxiv2023

Dissociable roles of thalamic nuclei in the refinement of reaches to spatial targets

Reaches are complex movements that are critical for survival, and encompass the control of different aspects such as direction, speed, and endpoint precision. Complex movements have been postulated to be learned and controlled through distributed motor networks, of which the thalamus is a highly connected node. Still, the role of different thalamic circuits in learning and controlling specific aspects of reaches has not been investigated. We report dissociable roles of two distinct thalamic nuclei - the parafascicular (Pf) and ventroanterior/ventrolateral (VAL) nuclei - in the refinement of spatial target reaches in mice. Using 2-photon calcium imaging in a head-fixed joystick task where mice learned to reach to a target in space, we found that glutamatergic neurons in both areas were most active during reaches early in learning. Reach-related activity in both areas decreased late in learning, as movement direction was refined and reaches increased in accuracy. Furthermore, the population dynamics of Pf, but not VAL, covaried in different subspaces in early and late learning, but eventually stabilized in late learning. The neural activity in Pf, but not VAL, encoded the direction of reaches in early but not late learning. Accordingly, bilateral lesions of Pf before, but not after learning, strongly and specifically impaired the refinement of reach direction. VAL lesions did not impact direction refinement, but instead resulted in increased speed and target overshoot. Our findings provide new evidence that the thalamus is a critical motor node in the learning and control of reaching movements, with specific subnuclei controlling distinct aspects of the reach early in learning.

neuroscience↗

bioRxiv2023

Exploration biases how forelimb reaches to a spatial target are learned

The brain can learn to generate actions, such as reaching to a target, using different movement strategies. Understanding how different variables bias which strategies are learned to produce such a reach is important for our understanding of the neural bases of movement. Here we introduce a novel spatial forelimb target task in which perched head-fixed mice learn to reach to a circular target area from a set start position using a joystick. These reaches can be achieved by learning to move into a specific direction or to a specific endpoint location. We find that mice gradually learn to successfully reach the covert target. With time, they refine their initially exploratory complex joystick trajectories into controlled targeted reaches. The execution of these controlled reaches depends on the sensorimotor cortex. Using a probe test with shifting start positions, we show that individual mice learned to use strategies biased to either direction or endpoint-based movements. The degree of endpoint learning bias was correlated with the spatial directional variability with which the workspace was explored early in training. Furthermore, we demonstrate that reinforcement learning model agents exhibit a similar correlation between directional variability during training and learned strategy. These results provide evidence that individual exploratory behavior during training biases the control strategies that mice use to perform forelimb covert target reaches.

neuroscience↗

bioRxiv2021

The brain uses invariant dynamics to generalize outputs across movements

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it re-uses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface that transformed rhesus macaques motor cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low-dimensional, and critically, they align with the brain-machine interface, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements, and show how feedback can be integrated with invariant dynamics to issue generalizable commands.

neuroscience↗