Topic: motor adaptation

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

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

Learning is fundamental for interacting with a changing environment. Critical to this process is the ability to rapidly adapt previously acquired skills to external perturbations while avoiding catastrophic forgetting of previous tasks. For the learning system, this corresponds to a trade-off between being able to maintain stable memories of learned tasks, and the ability to swiftly adapt such memories to changes. We hypothesize that such a problem can be addressed by using a two-timescale [1,2] strategy, where memorization and adaptation are performed by two separate modules. This structure a