Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

While neural plasticity has long been studied as the basis of learning, the growth of large-
scale neural recording techniques provides a unique opportunity to study how learning …

Animals of the same species exhibit similar behaviours that are advantageously adapted to
their body and environment. These behaviours are shaped at the species level by selection …

The spiking activity of populations of cortical neurons is well described by the dynamics of a
small number of population-wide covariance patterns, whose activation we refer to as 'latent …

Animals can quickly adapt learned movements to external perturbations, and their existing
motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting …

There is rich variety in the activity of single neurons recorded during behaviour. Yet, these
diverse single neuron responses can be well described by relatively few patterns of neural …

The complex neural activity of prefrontal cortex (PFC) is a hallmark of cognitive processes.
How these rich dynamics emerge and support neural computations is largely unknown …

Electrical signal propagation in multi-layer neural networks plays a crucial physiological
role. This study constructs a three-layer neural network to simulate the orderly propagation …

The brain must maintain a stable world model while rapidly adapting to the environment, but
the underlying mechanisms are not known. Here, we posit that cortico-cerebellar loops play …

Learning relies on coordinated synaptic changes in recurrently connected populations of
neurons. Therefore, understanding the collective evolution of synaptic connectivity over …