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Excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic males versus females.

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.

Building on simple unsupervised matrix factorization techniques, the seqNMF algorithm successfully recovers neural sequences in a wide range of simulated and real datasets.

Initial confidence and choice in a decision, and their potential revision, arise from a common mechanism that challenges models that claim confidence and decision processes are dissociated.

A high-throughput behavioral paradigm and computational modeling are used to decompose olfactory navigation in walking Drosophila melanogaster into a set of quantitative relationships between sensory input and motor output.

Attenuated anticipatory activity in ventromedial prefrontal cortex is modulated by dopamine D1 receptor density in nucleus accumbens, and accounts for impaired probabilistic reward learning in older adults.

Computational and theoretical models show that rate adaptation of phase responses can regulate Purkinje cell outputs by forming transient oscillations in fast-spiking neurons.

A mathematical model built around the assumption that the desire to maintain internal homeostasis drives the behavior of animals, by affecting their learning processes, can explain many real-world behaviors, including some that might otherwise appear irrational.

The firing rates of neurons in the grasshopper auditory system are surprisingly robust to changes in temperature, and cell-intrinsic mechanisms are sufficient to explain this temperature insensitivity.