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.
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).
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.
Computational modeling and analysis of mouse neural population data finds that the excitation/inhibition imbalance theory of brain disorders is too limited to account for key changes in neural activity statistics.
Everyday soundscapes dynamically engage attention towards target sounds or salient ambient events, with both attentional forms engaging the same fronto-parietal network but in a push-pull competition for limited neural resources.