A novel statistical algorithm for mining high-dimensional spike train (count) data for significant spatio-temporal patterns reveals new insights into task and brain area dependent functional organization of neural activity.
The synaptic structure in mouse V1 is explained by a synergy of homeostatic plasticity in incoming and outgoing synapses of inhibitory interneurons, establishing a stimulus-specific balance of excitation and inhibition.
Ten popular spike sorting codes are reproducibly benchmarked for accuracy on electrophysiology datasets from eleven laboratories with interactive web-based exploration of thousands of ground-truth units.
A novel method and software provides researchers with the capability to rapidly, flexibly, and robustly perform Bayesian parameter estimation of theoretically meaningful models in cognitive neuroscience that were heretofore intractable.
CaImAn is an open-software package that equips the neuroscience community with a set of turnkey, fast and scalable solutions to pre-processing problems arising in single cell calcium imaging data analysis.
The human brain is capable of implementing inverse reinforcement learning, where an observer infers the hidden reward structure of a decision problem solely through observing another individual take actions.
Hybrid brain network models predict neurophysiological processes that link structural and functional empirical data across scales and modalities in order to better understand neural information processing and its relation to brain function.