Learning in deep neural networks and brains with similarity-weighted interleaved learning.

Understanding how the brain learns throughout a lifetime remains a long-standing challenge. In artificial neural networks (ANNs), incorporating novel information too rapidly results in catastrophic interference, i.e.

Early impairment of thalamocortical circuit activity and coherence in a mouse model of Huntington's disease.

Huntington's disease (HD) is a progressive, fatal neurodegenerative disorder characterized by motor, cognitive, and psychiatric disturbances. There is no known cure for HD, but its progressive nature allows for early therapeutic intervention. Currently, much of the research has focused on the striatum, however, there is evidence suggesting that disruption of thalamocortical circuits could underlie some of the early symptoms of HD.

Selective Modulation of Orbitofrontal Network Activity during Negative Occasion Setting.

Discrete cues can gain powerful control over behavior to help an animal anticipate and cope with upcoming events. This is important in conditions where understanding the relationship between complex stimuli provides a means to resolving situational ambiguity. However, it is unclear how cortical circuits generate and maintain these signals that conditionally regulate behavior.

Parvalbumin Interneurons Modulate Striatal Output and Enhance Performance during Associative Learning.

The prevailing view is that striatal parvalbumin (PV)-positive interneurons primarily function to downregulate medium spiny projection neuron (MSN) activity via monosynaptic inhibitory signaling. Here, by combining in vivo neural recordings and optogenetics, we unexpectedly find that both suppressing and over-activating PV cells attenuates spontaneous MSN activity. To account for this, we find that, in addition to monosynaptic coupling, PV-MSN interactions are mediated by a competing disynaptic inhibitory circuit involving a variety of neuropeptide Y-expressing interneurons.