Integrating Early Results on Ventral Striatal Gamma Oscillations in the Rat

A vast literature implicates the ventral striatum in the processing of reward-related information and in mediating the impact of such information on behavior. It is characterized by heterogeneity at the local circuit, connectivity, and functional levels. A tool for dissecting this complex structure that has received relatively little attention until recently is the analysis of ventral striatal local field potential oscillations, which are more prominent in the gamma band compared to the dorsal striatum. Here we review recent results on gamma oscillations recorded from freely moving rats. Ventral striatal gamma separates into distinct frequency bands (gamma-50 and gamma-80) with distinct behavioral correlates, relationships to different inputs, and separate populations of phase-locked putative fast-spiking interneurons. Fast switching between gamma-50 and gamma-80 occurs spontaneously but is influenced by reward delivery as well as the application of dopaminergic drugs. These results provide novel insights into ventral striatal processing and highlight the importance of considering fast-timescale dynamics of ventral striatal activity

Hippocampus Leads Ventral Striatum in Replay of Place-Reward Information

While the brain is asleep, the hippocampus plays first fiddle in the orchestra of memory; spatial information is reactivated in the hippocampus shortly in advance of emotional memory traces in the vental striatum

Population-Level Neural Codes Are Robust to Single-Neuron Variability from a Multidimensional Coding Perspective

Sensory neurons are often tuned to particular stimulus features, but their responses to repeated presentation of the same stimulus can vary over subsequent trials. This presents a problem for understanding the functioning of the brain, because downstream neuronal populations ought to construct accurate stimulus representations, even upon singular exposure. To study how trial-by-trial fluctuations (i.e., noise) in activity influence cortical representations of sensory input, we performed chronic calcium imaging of GCaMP6-expressing populations in mouse V1. We observed that high-dimensional response correlations, i.e., dependencies in activation strength among multiple neurons, can be used to predict single-trial, single-neuron noise. These multidimensional correlations are structured such that variability in the response of single neurons is relatively harmless to population representations of visual stimuli. We propose that multidimensional coding may represent a canonical principle of cortical circuits, explaining why the apparent noisiness of neuronal responses is compatible with accurate neural representations of stimulus features

Model-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis.

While the neurobiology of simple and habitual choices is relatively well known, our current understanding of goal-directed choices and planning in the brain is still limited. Theoretical work suggests that goal-directed computations can be productively associated to model-based (reinforcement learning) computations, yet a detailed mapping between computational processes and neuronal circuits remains to be fully established. Here we report a computational analysis that aligns Bayesian nonparametrics and model-based reinforcement learning (MB-RL) to the functioning of the hippocampus (HC) and the ventral striatum (vStr)-a neuronal circuit that increasingly recognized to be an appropriate model system to understand goal-directed (spatial) decisions and planning mechanisms in the brain. We test the MB-RL agent in a contextual conditioning task that depends on intact hippocampus and ventral striatal (shell) function and show that it solves the task while showing key behavioral and neuronal signatures of the HC-vStr circuit. Our simulations also explore the benefits of biological forms of look-ahead prediction (forward sweeps) during both learning and control. This article thus contributes to fill the gap between our current understanding of computational algorithms and biological realizations of (model-based) reinforcement learning

Corticostriatal Interactions during Learning, Memory Processing, and Decision Making

This mini-symposium aims to integrate recent insights from anatomy, behavior, and neurophysiology, highlighting the anatomical organization, behavioral significance, and information-processing mechanisms of corticostriatal interactions. In this summary of topics, which is not meant to provide a comprehensive survey, we will first review the anatomy of corticostriatal circuits, comparing different ways by which "loops" of cortical–basal ganglia circuits communicate. Next, we will address the causal importance and systems-neurophysiological mechanisms of corticostriatal interactions for memory, emphasizing the communication between hippocampus and ventral striatum during contextual conditioning. Furthermore, ensemble recording techniques have been applied to compare information processing in the dorsal and ventral striatum to predictions from reinforcement learning theory. We will next discuss how neural activity develops in corticostriatal areas when habits are learned. Finally, we will evaluate the role of GABAergic interneurons in dynamically transforming cortical inputs into striatal output during learning and decision making

Role for mTOR Signaling and Neuronal Activity in Morphine-Induced Adaptations in Ventral Tegmental Area Dopamine Neurons

SummaryWhile the abuse of opiate drugs continues to rise, the neuroadaptations that occur with long-term drug exposure remain poorly understood. We describe here a series of chronic morphine-induced adaptations in ventral tegmental area (VTA) dopamine neurons, which are mediated via downregulation of AKT-mTORC2 (mammalian target of rapamycin complex-2). Chronic opiates decrease the size of VTA dopamine neurons in rodents, an effect seen in humans as well, and concomitantly increase the excitability of the cells but decrease dopamine output to target regions. Chronic morphine decreases mTORC2 activity, and overexpression of Rictor, a component of mTORC2, prevents morphine-induced changes in cell morphology and activity. Further, local knockout of Rictor in VTA decreases DA soma size and reduces rewarding responses to morphine, consistent with the hypothesis that these adaptations represent a mechanism of reward tolerance. Together, these findings demonstrate a novel role for AKT-mTORC2 signaling in mediating neuroadaptations to opiate drugs of abuse