Flexible actions

Although we often focus on the ability of a skilled movement to be executed rapidly and with precisely replicated kinematics; motor skill is also characterized by the flexibility with which an action can be executed while reliably achieving a goal. For example, the ability to flexibly act with a range of vigor (e.g. amplitude, speed) is an essential aspect of skill. Several lines of evidence suggest that basal ganglia play a critical role in the purposive control of movement vigor in mammals. To study the circuit mechanisms underlying the control of movement vigor we developed behavioral paradigms to study precise movement kinematics in mice performing voluntary movements of a joystick. In a genetic model of Parkinson's disease we found that parkinsonian mice also exhibit a profound enervation of joystick movements. Subsequent work provided a mechanistic account of these deficits in the absence of dopamine: closed-loop optogenetic stimulation we showed that dopamine-dependent plasticity allows specific cell types in basal ganglia to exert a bidirectional, opponent control over movement vigor. Ongoing work in the lab seeks to understand the principles that govern how basal ganglia control movement vigor. For exmaple, how are competing demands for specificity and generalization balanced?

Related work

Motor cortical output is selectively distributed across projection neuron classes
Science Advances 10.1126/sciadv.abj5167

Opponent and bidirectional control of movement velocity in the basal ganglia
Nature 533(7603):402-6

Dopamine is required for representation and control of movement vigor
Cell 162(6): 1418-30

Models of learning

Systems neuroscience has had influential successes in the observation of neural activity that correlates with (represents) key elements (quantities, dynamics, etc.) computations that could implement such algorithms. In recent years it has become increasingly possible to manipulate activity in increasingly precise ways in attempts to demonstrate that these computations are causally related to the observed behavior. Mechanistic models of circuit computations ultimately require detailed understanding of how the biophysics of individual neurons and properties of connectivity implement observed computations. Drawing upon our background in cellular biophysics and synaptic plasticity, we also seek to articulate how biophysical properties of neurons, synaptic plasticity rules, and patterns of anatomical connectivity give rise to circuit computations. In recent work we have described how the reward prediction error correlate results from temporal integration of two dissociable pathways and how recurrent connectivity in substantia nigra implements feedback gain control.

Related work

Mesolimbic dopamine adapts the rate of learning from action
Nature 614: 294-302

The timing of action determines reward expectation signals in dopamine neurons
Nature Neuroscience 21(11): 1563-1573

Dissociable contributions of phasic dopamine activity to reward and prediction
Cell Reports 109684

Tools + Neuroscience

The work in our lab combines large-scale extracellular electrophysiology, optical recording, and intracellular electrophysiology with techniques to identify and perturb the activity of specific cell types and circuits in the mouse brain. This constellation of technical approaches requires significant development of hardware and software for data acquisition and analysis. We are also actively involved in developing and refining molecular tools that allow us to identify, target, and manipulate specific cell-types in concert with physiology. In our recent work we have helped to develop and validate the cell-type specific pharmacology in vivo using DARTs and developed a new class of optogenetic inhibitor exemplified by FLInChR. We draw heavily upon the expertise of many staff scientists and engineers in the jET, Virus Services, Transgenic Mouse Facility, and Scientific Computing. Some of the fruits of our labor can be found on the resources section of the website where there are details about how to recreate the hardware developed for our experiments and software available for download.

Related work

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings
Science 372: 6539

The timing of action determines reward expectation signals in dopamine neurons
Cell 175(4):1131-1140

Deconstructing behavioral neuropharmacology with cellular specificity
Science 356: eaaj2161

Projects for students

Recorded large ensembles of neurons

We use simultaneous, large scale neural recordings from multiple forebrain areas that are critical for descending control of novel motor skills. Optogenetic techniques for identifying and perturbing distinct cell types allows us to detail the logic of how distinct cell types implement distinct aspects of descending control.

Develop brain inspired learning algorithms

In our recent work we have begun to examine how the activity of midbrain dopamine neurons evolve as a naive animal first learns about a reward predictive cue. Combining these new data with computational models provided the unique insight that dopamine neurons implement an adaptive learning rate that can improve learning.

Develop new hardware for experiments

Over the past several summers undergraduates in the lab have piloted diverse tasks for studying how goal-directed motor skills are executed and how flexible actions adapted to changing demands. Often such projects involve developing new hardware in collaboration with Janelia Experimental Technolgy.