[Show abstract][Hide abstract] ABSTRACT: 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. Here we show that VMS tonically active neurons (TANs), including putative cholinergic interneurons, generate unique bidirectional outcome responses during reward-based learning, reporting both positive (reward) and negative (reward omission) outcomes when behavioral change is prompted by switches in reinforcement contingencies. VMS output neurons (SPNs), by contrast, are nearly insensitive to switches in reinforcement contingencies, gradually losing outcome signaling while maintaining responses at trial initiation and goal approach. Thus, TANs and SPNs in the VMS provide distinct signals optimized for different aspects of the learning process.
[Show abstract][Hide abstract] ABSTRACT: 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. Populations of simultaneously recorded neurons synchronized their spike firing similarly during both the high-γ-band and β-band bursts. Thus, the structure of spike firing was reorganized during learning in relation to different rhythms. Spiking was concentrated around the troughs of the β-oscillations for fast-spiking interneurons and around the peaks for projection neurons, indicating alternating periods of firing at different frequencies as learning progressed. Spike-field synchrony was primarily local during high-γ-bursts but was widespread during β-bursts. The learning-related shift in the probability of high-γ and β-bursting thus could reflect a transition from a mainly focal rhythmic inhibition during early phases of learning to a more distributed mode of rhythmic inhibition as learning continues and behavior becomes habitual. These dynamics could underlie changing functions of the ventromedial striatum during habit formation. More generally, our findings suggest that coordinated changes in the spatiotemporal relationships of local field potential oscillations and spike activity could be hallmarks of the learning process.
Proceedings of the National Academy of Sciences 09/2011; 108(40):16801-6. DOI:10.1073/pnas.1113158108 · 9.67 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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. This resource of cell type–specific ChR2(H134R) mouse lines will facilitate the precise mapping of neuronal connectivity and the dissection of the neural basis of behavior.
[Show abstract][Hide abstract] ABSTRACT: 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. We found highly contrasting patterns of activity in these regions during task performance and found that these different patterns of structured activity developed concurrently, but with sharply different dynamics. Based on the region-specific dynamics of these patterns across learning, we suggest a working model whereby dorsomedial associative loops can modulate the access of dorsolateral sensorimotor loops to the control of action.
[Show abstract][Hide abstract] ABSTRACT: 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. D (33%), E(67%) vs. F(33%)--impaired test performance and disrupted performance when the rats were tested with novel cue combinations (C vs. F and E vs. D), where control animals chose C and F. In contrast, inactivating the dorsal hippocampus enhanced performance on this task and on a deterministic discrimination A (100%) vs. B (0%). These results are consistent with the complementary learning systems view, which assumes that the cortico-striatal and hippocampal system capture information in parallel. How this information combines to influence task performance depends on the compatibility of the content captured by each system. These results suggest that the trial-specific information captured by the hippocampal system can be incompatible with the across-trial integration of trial outcomes captured by the cortico-striatal system.
[Show abstract][Hide abstract] ABSTRACT: 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. Our results also imply that the successful performance of a skill can ultimately result in its establishment as a habit outside the basal ganglia.
[Show abstract][Hide abstract] ABSTRACT: 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. Similarly, the frontal cortex and associated basal ganglia system have important neural specializations not required of the posterior cortex system. Taken together, this overall cognitive architecture, which has been implemented in functioning computational models, provides a rich and often subtle means of explaining a wide range of behavioral and cognitive neuroscience data. Here, we summarize recent results in the domains of recognition memory, contextual fear conditioning, effects of basal ganglia lesions on stimulus-response and place learning, and flexible responding.
Neurobiology of Learning and Memory 12/2004; 82(3):253-67. DOI:10.1016/j.nlm.2004.06.004 · 3.65 Impact Factor