Neurons in the ventral striatum exhibit cell-type-specific representations of outcome during learning.

The ventromedial striatum (VMS) is a node in circuits underpinning both affect and reinforcement learning. The cellular bases of these functions and especially their potential linkages have been unclear. VMS cholinergic interneurons, however, have been singled out as being related both to affect and to reinforcement-based conditioning, raising the possibility that unique aspects of their signaling could account for these functions.

Cell type–specific channelrhodopsin-2 transgenic mice for optogenetic dissection of neural circuitry function.

Optogenetic methods have emerged as powerful tools for dissecting neural circuit connectivity, function and dysfunction. We used a bacterial artificial chromosome (BAC) transgenic strategy to express the H134R variant of channelrhodopsin-2, ChR2(H134R), under the control of cell type–specific promoter elements. We performed an extensive functional characterization of the newly established VGAT-ChR2(H134R)-EYFP, ChAT-ChR2(H134R)-EYFP, Tph2-ChR2(H134R)-EYFP and Pvalb(H134R)-ChR2-EYFP BAC transgenic mouse lines and demonstrate the utility of these lines for precisely controlling action-potential firing of GABAergic, cholinergic, serotonergic and parvalbumin-expressing neuron subsets using blue light.

Habit learning is associated with major shifts in frequencies of oscillatory activity and synchronized spike firing in striatum.

Rhythmic brain activity is thought to reflect, and to help organize, spike activity in populations of neurons during on-going behavior. We report that during learning, a major transition occurs in task-related oscillatory activity in the ventromedial striatum, a striatal region related to motivation-dependent learning. Early on as rats learned a T-maze task, bursts of 70- to 90-Hz high-γ activity were prominent during T-maze runs, but these gradually receded as bursts of 15- to 28-Hz β-band activity became pronounced.

Differential dynamics of activity changes in dorsolateral and dorsomedial striatal loops during learning.

The basal ganglia are implicated in a remarkable range of functions influencing emotion and cognition as well as motor behavior. Current models of basal ganglia function hypothesize that parallel limbic, associative, and motor cortico-basal ganglia loops contribute to this diverse set of functions, but little is yet known about how these loops operate and how their activities evolve during learning. To address these issues, we recorded simultaneously in sensorimotor and associative regions of the striatum as rats learned different versions of a conditional T-maze task.

The role of the dorsal striatum and dorsal hippocampus in probabilistic and deterministic odor discrimination tasks.

Three experiments explored the contribution of the cortico-striatal system and the hippocampus system to the acquisition of solutions to simultaneous instrumental odor discriminations. Inactivation of the dorsal striatum after rats had reached criterion on a three problem probabilistic set of discriminations--A (80%) vs. B (20%), C (67%) vs.

Separate neural substrates for skill learning and performance in the ventral and dorsal striatum.

It is widely accepted that the striatum of the basal ganglia is a primary substrate for the learning and performance of skills. We provide evidence that two regions of the rat striatum, ventral and dorsal, play distinct roles in instrumental conditioning (skill learning), with the ventral striatum being critical for learning and the dorsal striatum being important for performance but, notably, not for learning. This implies an actor (dorsal) versus director (ventral) division of labor, which is a new variant of the widely discussed actor-critic architecture.

Hippocampus, cortex, and basal ganglia: insights from computational models of complementary learning systems.

We present a framework for understanding how the hippocampus, neocortex, and basal ganglia work together to support cognitive and behavioral function in the mammalian brain. This framework is based on computational tradeoffs that arise in neural network models, where achieving one type of learning function requires very different parameters from those necessary to achieve another form of learning. For example, we dissociate the hippocampus from cortex with respect to general levels of activity, learning rate, and level of overlap between activation patterns.