Our Research Goal

We are interested in how motor skills are learned and generated by the brain.  We study this in two different model systems - songbird and rodent. We use the songbird because it is a well-studied and experimentally tractable model system that allow us to ask detailed questions about how neural circuits implement complex motor sequence learning. More recently, we have shifted our focus to study rodents, generalist motor learners with the capacity to master a variety of different motor tasks. We have developed a unique experimental infrastructure to address these questions, including a fully automated high-throughput training system for rodents that can be combined with continuous long-term neural and behavioral recordings. We use this together with sophisticated circuit dissection tools (optogenetics, pharmacogenetics etc), as well as functional imaging of targeted neuronal populations, to arrive at a mechanistic description of how the mammalian brain learns and executes motor skills.   

Rodents Songbirds

 

Recent Publications

Acute off-target effects of neural circuit manipulations

Otchy TM, Wolff SBE, Rhee JY, Pehlevan C, Kawai R, Kempf A, Gobes SMH, Ölveczky BP. Acute off-target effects of neural circuit manipulations. Nature [Internet]. 2015 :doi:10.1038/nature16442. Publisher's VersionAbstract

Rapid and reversible manipulations of neural activity in behaving animals are transforming our understanding of brain function. An important assumption underlying much of this work is that evoked behavioural changes reflect the function of the manipulated circuits. We show that this assumption is problematic because it disregards indirect effects on the independent functions of downstream circuits. Transient inactivations of motor cortex in rats and nucleus interface (Nif) in songbirds severely degraded task-specific movement patterns and courtship songs, respectively, which are learned skills that recover spontaneously after permanent lesions of the same areas. We resolve this discrepancy in songbirds, showing that Nif silencing acutely affects the function of HVC, a downstream song control nucleus. Paralleling song recovery, the off-target effects resolved within days of Nif lesions, a recovery consistent with homeostatic regulation of neural activity in HVC. These results have implications for interpreting transient circuit manipulations and for understanding recovery after brain lesions.

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Automated long-term recording and analysis of neural activity in behaving animals

Dhawale AK, Poddar R, Kopelowitz E, Normand V, Wolff S, Ölveczky BP. Automated long-term recording and analysis of neural activity in behaving animals. Bioarxiv [Internet]. 2015;doi: http://dx.doi.org/10.1101/033266. Publisher's VersionAbstract

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons during experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics outside of task context. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving animals. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.

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Deep neuroethology of a virtual rodent

J M, D A, J M, Y T, G W, BP Ö. Deep neuroethology of a virtual rodent. arXiv [Internet]. In Preparation. Publisher's VersionAbstract

Parallel developments in neuroscience and deep learning have led to mutually productive exchanges, pushing our understanding of real and artificial neural networks in sensory and cognitive systems. However, this interaction between fields is less developed in the study of motor control. In this work, we develop a virtual rodent as a platform for the grounded study of motor activity in artificial models of embodied control. We then use this platform to study motor activity across contexts by training a model to solve four complex tasks. Using methods familiar to neuroscientists, we describe the behavioral representations and algorithms employed by different layers of the network using a neuroethological approach to characterize motor activity relative to the rodent's behavior and goals. We find that the model uses two classes of representations which respectively encode the task-specific behavioral strategies and task-invariant behavioral kinematics. These representations are reflected in the sequential activity and population dynamics of neural subpopulations. Overall, the virtual rodent facilitates grounded collaborations between deep reinforcement learning and motor neuroscience.

 arXiv:1911.09451v1

 

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