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 …

A central principle in neuroscience is that neurons within the brain act in concert to produce
perception, cognition, and adaptive behavior. Neurons are organized into specialized brain …

Primates can richly parse sensory inputs to infer latent information. This ability is
hypothesized to rely on establishing mental models of the external world and running mental …

Modelling the spatiotemporal dynamics in the activity of neural populations while also
enabling their flexible inference is hindered by the complexity and noisiness of neural …

Others' observed actions cause continuously changing retinal images, making it challenging
to build neural representations of action identity. The monkey anterior intraparietal area …

Stroke is a leading cause of disability. While neurotechnology has shown promise for
improving upper limb recovery after stroke, efficacy in clinical trials has been variable. Our …

The utility of machine learning in understanding the motor system is promising a revolution
in how to collect, measure, and analyze data. The field of movement science already …

Recurrent neural networks (RNNs) trained with machine learning techniques on cognitive
tasks have become a widely accepted tool for neuroscientists. In this short opinion piece, we …

One of the essential functions of biological neural networks is the processing of information.
This includes everything from processing sensory information to perceive the environment …

Animals move in three dimensions (3D). Thus, 3D measurement is necessary to report the
true kinematics of animal movement. Existing 3D measurement techniques draw on …