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How animals are able to rapidly adapt their behaviour to changing environmental demands remains poorly understood. Here, the authors use a modelling approach to show that synaptic plasticity in motor cortex may underlie rapid motor learning, demonstrating that small, correlated connectivity changes that preserve neural covariance are highly effective in driving behavioural adaptation.

Recordings of neural populations from motor cortex and striatum spanning monkeys and mice demonstrate that neural dynamics in individuals from the same species are preserved when they perform similar behaviour.

Gallego, Perich et al. report that latent dynamics in the neural manifold across three cortical areas are stable throughout years of consistent behavior. The authors posit that these dynamics are fundamental building blocks of learned behavior.

A spinal cord neuroprosthesis targeting leg motor neurons in real time improves walking and reduces freezing of gait in non-human primate models and in one individual with advanced Parkinson’s disease.

How the cortex generates movement to achieve different tasks remains poorly understood. Here the authors show that the cortex serializes motor control by first performing task-specific computations in dorsal premotor cortex in order to then generate task-independent commands in primary motor cortex.

How the brain adapts our movements to new conditions remains unclear. Here, the authors show that a recurrent neural network that controls its output using error-based feedback can learn to counteract a persistent perturbation using a biologically plausible plasticity rule, recapitulating key neural and behavioural features of motor adaptation.

Recent advances in neuroscience have revealed how neural population activity underlying behavior can be well described by topological objects called neural manifolds. Understanding how nature, nurture and other factors shape neural manifolds could illuminate new avenues for defining mechanisms and interventions.

Using recurrent neural networks, here the authors show that learning the same task through different experiences can lead to important differences in how neural activity is structured. These differences can play a crucial role for subsequent adaptation, with networks that are equally good at the initial task showing opposing trends in adaptation.

This paper describes a neurotechnology that interacts with neural circuits in the spinal cord to restore arm and hand control after injury. With this implant, monkeys with paralysis recovered the ability to reach and grasp just a few days after injury.

Surface two-photon imaging of the brain cannot access somatic calcium signals of neurons from deep layers of the macaque cortex. Here, the authors present an implant and imaging system for chronic motion-stabilized two-photon imaging of dendritic calcium signals to drive an optical brain-computer interface in macaques.

Movements are continually constrained by the current body position and its relation to the surroundings. Here the authors report that the population activity of monkey dorsal premotor cortex neurons dynamically represents the probability distribution of possible reach directions.

Motor cortical neurons enable performance of a wide range of movements. Here, the authors report that dominant population activity patterns, the neural modes, are largely preserved across various tasks, with many displaying consistent temporal dynamics and reliably mapping onto muscle activity.

A computational approach that uses the statistics of movement to find a mapping between neural activity and motor variables decodes the intended movements of monkeys with performance comparable to that of supervised methods.