Neurons in the brain exhibit activity with various relations to locomotion and these signals are best decoded by combining activities from many neurons.

Synaptic modulations alone imbue networks with computational capabilities comparable to recurrent connections on several neuroscience-relevant tasks, which manifest in fundamentally different neuronal dynamics.

The proposed techniques enable researchers to disentangle the statistical features of neural population responses, and rigorously quantify how these features carry information about stimuli and experimental variables.

A new data analysis tool provides a concise way of visualizing neural data that summarizes all the relevant features of the population response in a single figure.

Analysis and modelling of sensorimotor neural activity shows how ongoing plasticity and appropriately tuned weights can cope with substantial ongoing changes in the neural code.

The neuronal activity in primary motor cortex does not exhibit smooth low-dimensional dynamics during grasp as it does during reach.

An accurate and efficient biologically plausible statistical model of the spiking activity of neural populations shows computational benefits of homeostatic synaptic scaling in learning large neural population codes.

A circuit consisting of motor neurons, inhibitory interneurons and mechanosensory neurons controls copulatory behaviour in male fruit flies.

Using a sequential neurofeedback-arm reaching task, a new link is established among population neural activity patterns, generation of beta oscillations, and motor behavior changes.

Developing long bones contain distinct mesenchymal stem-cell populations derived from mesoderm and neural crest, which have specialized functions in skeleton formation and the establishment of the hematopoietic stem-cell niche, respectively.