Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …

Most of our motor skills are not innately programmed, but are learned by a combination of
motor exploration and performance evaluation, suggesting that they proceed through a …

Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …

Individual differences in motor learning ability are widely acknowledged, yet little is known
about the factors that underlie them. Here we explore whether movement-to-movement …

It is generally thought that skilled behavior in human beings results from a functional
hierarchy of the motor control system, within which reusable motor primitives are flexibly …

Significant trial-by-trial variation persists even in the most practiced skills. One prevalent
view is that such variation is simply 'noise'that the nervous system is unable to control or that …

In songbirds, as in mammals, basal ganglia-forebrain circuits are necessary for the learning
and production of complex motor behaviors; however, the precise role of these circuits …

Trained neural network models, which exhibit features of neural activity recorded from
behaving animals, may provide insights into the circuit mechanisms of cognitive functions …

Efforts to study the neural correlates of learning are hampered by the size of the network in
which learning occurs. To understand the importance of learning-related changes in a …

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-
relevant variables. Chaotic recurrent networks, which spontaneously generate rich …