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We are interested in understanding how complex motor sequences are learned and generated by the nervous system. What is the identity of the neural circuits involved in motor learning and how do their respective functions change as the animal learns to execute a stereotyped motor sequence? By studying these questions in two different model systems - songbird and rodent - we hope to identify general principles of how neural circuits underlie the learning and execution of complex motor acts.



Recent Publications

Motor Cortex Is Required for Learning but Not for Executing a Motor Skill

Kawai R, Markman T, Poddar R, Ko R, Fantana AL, Dhawale AK, Kampff AR, Ölveczky BP. Motor Cortex Is Required for Learning but Not for Executing a Motor Skill. Neuron. 2015;86:800-812.Abstract
Motor cortex is widely believed to underlie the acquisition and execution of motor skills, but its contributions to these processes are not fully understood. One reason is that studies on motor skills often conflate motor cortex's established role in dexterous control with roles in learning and producing task-specific motor sequences. To dissociate these aspects, we developed a motor task for rats that trains spatiotemporally precise movement patterns without requirements for dexterity. Remarkably, motor cortex lesions had no discernible effect on the acquired skills, which were expressed in their distinct pre-lesion forms on the very first day of post-lesion training. Motor cortex lesions prior to training, however, rendered rats unable to acquire the stereotyped motor sequences required for the task. These results suggest a remarkable capacity of subcortical motor circuits to execute learned skills and a previously unappreciated role for motor cortex in "tutoring" these circuits during learning. PDF Supplementary Videos: Video1Video2Video3Video4Video5.

Learning precisely timed spikes

Memmesheimer RM, Rubin R, Ölveczky BP, Sompolinsky H. Learning precisely timed spikes. Neuron. 2014;82:925-38.Abstract
To signal the onset of salient sensory features or execute well-timed motor sequences, neuronal circuits must transform streams of incoming spike trains into precisely timed firing. To address the efficiency and fidelity with which neurons can perform such computations, we developed a theory to characterize the capacity of feedforward networks to generate desired spike sequences. We find the maximum number of desired output spikes a neuron can implement to be 0.1-0.3 per synapse. We further present a biologically plausible learning rule that allows feedforward and recurrent networks to learn multiple mappings between inputs and desired spike sequences. We apply this framework to reconstruct synaptic weights from spiking activity and study the precision with which the temporal structure of ongoing behavior can be inferred from the spiking of premotor neurons. This work provides a powerful approach for characterizing the computational and learning capacities of single neurons and neuronal circuits. PDF