Motor adaptation

Adaptation is essential for biological motor control, enabling rapid modifications of control strategies as the environment changes. However, we still lack a unified network-level theory of coordinated motor adaptation. In previous work, we have proposed a multi-area RNN capable of rapid motor adaptation. The network comprises an internal forward model and a planning/control policy network, both of which are continuously modulated by a network that performs contextual inference. The network is meta-trained to maintain proficient control of hand reaches in the face of changing environmental dyn

…Sensorimotor learning provides a particularly useful testbed for theories of continual learning, as previous experiments have revealed a rich phenomenology of the complex and often intriguing dependence of motor adaptation on both rec

…[1,2] strategy, where memorization and adaptation are performed by two separate modules. This structure a

Motor imagery and overt movement have been hypothesized to be functionally equivalent. At the same time there is evidence that hints at distinct mechanisms of both processes. To explore this disparity, we investigated whether prior imagined movements had the same effect as prior overt movements on motor adaptation performance during a subsequent reach. Additionally, we aimed to identify neuronal correlates of motor imagery predicting such motor adaptation.Movement kinematics (exoskeleton robot, Kinarm Lab) and EEG of 60 participants were recorded to investigate direction-specific adaptation du

Sensorimotor adaptation, crucial for refining movements in response to unexpected errors, fundamentally depends on the cerebellum. The olivary climbing fiber (CF) input to cerebellar Purkinje cells (PC) acts as a “teacher” signal during motor learning and drives cerebellar plasticity. Although the CF input reliably evokes PC dendritic spikes, it is not the only signal that modulates this post-synaptic response. To disentangle the modulation of the pre-synaptic CF burst from that of its post-synaptic PC dendritic response, we simultaneously imaged PC dendrites and CF terminals with 2-photon mic

The cerebellum is involved in motor coordination and is divided into anatomo-functional modules processing sensorimotor information from a given body part. This information is conveyed by mossy fibers to granule cells (GC) contacting Purkinje cells (PC) belonging to different modules via their long axons, the parallel fibers (PFs). PFs underlie inter-modular communication, which may be at the core of motor coordination. Therefore, the main goal of our research is to understand how intermodular communication in the cerebellar cortex underlies motor adaptation.Using patterned light photostimulat

Learning induces changes in neural activity over slow timescales. These changes can be summarized by restructuring neural population data into a three-dimensional array or tensor, of size neurons by time points by trials. Classic dimensionality reduction methods often assume that neural representations are constrained to a fixed low-dimensional latent subspace. Consequently, this view does not capture how the latent subspace could evolve over learning, nor how high-dimensional neural activity could emerge over learning. Furthermore, the link between these empirically-observed changes in neural

Optimal motor performance requires flexible adjustments to behavior depending on different contexts. This idea has gained wide support from behavioral and computational studies, but remains poorly understood at the neural level. Bengalese finch (Lonchura striata) song is a learned motor sequence composed of discrete elements, or syllables. Adult finches can learn to modify the pitch of individual syllables in a gradual way, supported by a well-understood cortical-basal ganglia circuit, the AFP (anterior forebrain pathway). We here tested whether adult finches can use arbitrary contextual cues

Motor adaptation is a widely-used paradigm for understanding short-term learning. However, it is unknown how existing skillsets acquired through the long-term learning process that begins in utero can affect motor adaptation. Long-term learning likely causes changes in neural connectivity, which may shape the neural dynamics that can be produced. To understand the interaction between circuit connectivity constraints and a neural population’s ability to change its activity patterns, we modeled the neural dynamics of the motor cortex during skill learning and subsequent adaptation using a recurr