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.
Retrograde tracing of the neural circuits that control movement of the jaw and tongue reveals how shared premotor neurons help to ensure coordinated muscle activity.
Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics.
Greed personality trait is associated with behavioral loss aversion via the mediation of the neural loss aversion signal in the medial orbitofrontal cortex.
A bright and stochastic multicolor labeling method, Tetbow, facilitates millimeters-scale reconstructions of neuronal circuits at a large scale using tissue clearing.
Syntactic structure-building processes can be applied to speech that is task-irrelevant and should be ignored, demonstrating that Selective Attention does not fully eliminate linguistic processing of competing speech.
The conductance-based encoding model creates a new bridge between statistical models and biophysical models of neurons, and infers visually-evoked excitatory and inhibitory synaptic conductances from spike trains in macaque retina.
Random fluctuations in neuronal firing may enable a single brain region, the medial entorhinal cortex, to perform distinct roles in cognition (by generating gamma waves) and spatial navigation (by producing a grid cell map).
