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. 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.National Institutes of Health (U.S.) (MH60379)United States. Office of Naval Research (N000140410208)Stanley H. and Sheila G. Sydney FundEuropean Union (Grant 201716)McGovern Institute for Brain Research at MIT (Fellowship

A Natural History of Skills

International audienceThe dorsal pallium (a.k.a. the cortex in the mammals) makes a large loop circuit with the basal ganglia and the thalamus known to control and adapt behavior but the who's who of the functional roles of these structures is still debated. Influenced by the Triune brain theory that was proposed in the early sixties, many current theories propose a hierarchical organization on the top of which stands the cortex to which the subcortical structures are subordinated. In particular, habits formation has been proposed to reflect a switch from conscious on-line control of behavior by the cortex, to a fully automated subcortical control. In this review, we propose to revalue the function of the network in light of the current experimental evidence concerning the anatomy and physiology of the basal ganglia-cortical circuits in vertebrates. We briefly review the current theories and show that they could be encompassed in a broader framework of skill learning and performance. Then, after reminding the state of the art concerning the anatomical architecture of the network and the underlying dynamic processes, we summarize the evolution of the anatomical and physiological substrate of skill learning and performance among vertebrates. We then lay out our hypothesis that the development of automatized skills relies on the BG teaching cortical circuits and is actually a late feature linked with the development of a specialized cortex or pallium that evolved in parallel in different taxa. We finally propose a minimal computational framework where this hypothesis can be explicitly implemented and tested

The well-worn route revisited: Striatal and hippocampal system contributions to route learning in human navigation

Parallel spatial memory systems theory posits that there are two types of memory system. One is a flexible, cognitive mapping system subserved by the hippocampal formation, and the other is a system centred on the striatum based on reinforcement learning principles where specific stimuli are associated with rewarded actions (O’Keefe & Nadel, 1978; White & McDonald, 2002). More recently, Khamassi & Humphries (2012) have argued that the division between model-based and model-free spatial learning is a better predictor of whether hippocampal or striatal systems will be recruited, with hippocampal systems associated with model-based responding and striatal systems with model-free responding. Model-free decision-making occurs when responding is based on average reward history associated with a particular cue-action pairing, whereas model-based decision-making allows knowledge of outcomes from previous learning history to be represented. We sought to test these theories by asking participants (N = 24) to navigate within a virtual environment through a previously learned, 9-junction route with distinctive landmarks at each junction, while undergoing functional magnetic resonance imaging. In critical conflict probe trials, a landmark was presented out of sequence such that following the usual sequence of actions would generate an opposite response to following the learned individual landmark-action association, now out of sequence. Participants that made sequence-based responses had higher parahippocampal activations relative to participants that made responses based on the individual landmark-action association, a result that would be predicted by the need to recruit model-based systems to make a sequence-based response. Parallel spatial memory systems theory would not predict hippocampal formation recruitment for either response in the conflict probe, because no cognitive mapping is required when following a prescribed route. In longer probe trials where participants were able to plan a sequence of responses, striatal systems were recruited (caudate and putamen) suggesting a role for striatum in action chunking

Striatal and hippocampal contributions to flexible navigation in rats and humans.

The hippocampus has been firmly established as playing a crucial role in flexible navigation. Recent evidence suggests that dorsal striatum may also play an important role in such goal-directed behaviour in both rodents and humans. Across recent studies, activity in the caudate nucleus has been linked to forward planning and adaptation to changes in the environment. In particular, several human neuroimaging studies have found the caudate nucleus tracks information traditionally associated with that by the hippocampus. In this brief review, we examine this evidence and argue the dorsal striatum encodes the transition structure of the environment during flexible, goal-directed behaviour. We highlight that future research should explore the following: (1) Investigate neural responses during spatial navigation via a biophysically plausible framework explained by reinforcement learning models and (2) Observe the interaction between cortical areas and both the dorsal striatum and hippocampus during flexible navigation