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.
Techniques are presented to facilitate widespread and standardized chronic use of Neuropixels probes for high-yield, long-term neural recording in freely moving animals.
Neurons in the lateral habenula are activated by pain, bitterness and social defeat, and their responses are dynamically shaped by learning, suggesting a role in experience-dependent selection of behavioral actions to stressors.
Dorsomedial and dorsolateral striatal neural activity differ during early learning of action sequences but do not change with performance improvement across sessions, and become similar after extended training.
Auditory cortical columns contain small subsets of neurons with highly synchronous activity (cNEs) that create robust sequences of coordinated activity suitable for enhanced information processing and signal transmission beyond the capability of individual neurons.
Sleep spindles provide a temporal framework to organize the reactivation of behaviorally relevant CA1 cells and sparsely active cells in the limbic thalamus.
Avoiding danger requires inhibitory signaling in the prelimbic prefrontal cortex, as evidenced by optogenetic manipulations based on neuronal firing patterns.
The prefrontal cortex encodes both stable and dynamic representations of expected value, providing mechanisms to support robust as well as flexible access to value information during temporal delays.
A two-part neural network models reward-based training and provides a unified framework in which to study diverse computations that can be compared to electrophysiological recordings from behaving animals.
Conscious visual percepts are encoded by face patches in the absence of report, can be decoded from population recordings, and are multiplexed with the veridical physical stimulus.
