scholarly journals Believer-Skeptic Meets Actor-Critic: Rethinking the Role of Basal Ganglia Pathways during Decision-Making and Reinforcement Learning

2016 ◽  
Vol 10 ◽  
Author(s):  
Kyle Dunovan ◽  
Timothy Verstynen
2021 ◽  
Author(s):  
James McGregor ◽  
Abigail Grassler ◽  
Paul I. Jaffe ◽  
Amanda Louise Jacob ◽  
Michael Brainard ◽  
...  

Songbirds and humans share the ability to adaptively modify their vocalizations based on sensory feedback. Prior studies have focused primarily on the role that auditory feedback plays in shaping vocal output throughout life. In contrast, it is unclear whether and how non-auditory information drives vocal plasticity. Here, we first used a reinforcement learning paradigm to establish that non-auditory feedback can drive vocal learning in adult songbirds. We then assessed the role of a songbird basal ganglia-thalamocortical pathway critical to auditory vocal learning in this novel form of vocal plasticity. We found that both this circuit and its dopaminergic inputs are necessary for non-auditory vocal learning, demonstrating that this pathway is not specialized exclusively for auditory-driven vocal learning. The ability of this circuit to use both auditory and non-auditory information to guide vocal learning may reflect a general principle for the neural systems that support vocal plasticity across species.


F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 122 ◽  
Author(s):  
Kristina Simonyan

The basal ganglia are a complex subcortical structure that is principally involved in the selection and implementation of purposeful actions in response to external and internal cues. The basal ganglia set the pattern for facilitation of voluntary movements and simultaneous inhibition of competing or interfering movements. In addition, the basal ganglia are involved in the control of a wide variety of non-motor behaviors, spanning emotions, language, decision making, procedural learning, and working memory. This review presents a comparative overview of classic and contemporary models of basal ganglia organization and functional importance, including their increased integration with cortical and cerebellar structures.


2021 ◽  
Vol 14 (1) ◽  
pp. 17
Author(s):  
Shuailong Li ◽  
Wei Zhang ◽  
Yuquan Leng ◽  
Xiaohui Wang

Environmental information plays an important role in deep reinforcement learning (DRL). However, many algorithms do not pay much attention to environmental information. In multi-agent reinforcement learning decision-making, because agents need to make decisions combined with the information of other agents in the environment, this makes the environmental information more important. To prove the importance of environmental information, we added environmental information to the algorithm. We evaluated many algorithms on a challenging set of StarCraft II micromanagement tasks. Compared with the original algorithm, the standard deviation (except for the VDN algorithm) was smaller than that of the original algorithm, which shows that our algorithm has better stability. The average score of our algorithm was higher than that of the original algorithm (except for VDN and COMA), which shows that our work significantly outperforms existing multi-agent RL methods.


2020 ◽  
Author(s):  
Milena Rmus ◽  
Samuel McDougle ◽  
Anne Collins

Reinforcement learning (RL) models have advanced our understanding of how animals learn and make decisions, and how the brain supports some aspects of learning. However, the neural computations that are explained by RL algorithms fall short of explaining many sophisticated aspects of human decision making, including the generalization of learned information, one-shot learning, and the synthesis of task information in complex environments. Instead, these aspects of instrumental behavior are assumed to be supported by the brain’s executive functions (EF). We review recent findings that highlight the importance of EF in learning. Specifically, we advance the theory that EF sets the stage for canonical RL computations in the brain, providing inputs that broaden their flexibility and applicability. Our theory has important implications for how to interpret RL computations in the brain and behavior.


2006 ◽  
Vol 16 (02) ◽  
pp. 111-124 ◽  
Author(s):  
D. SRIDHARAN ◽  
P. S. PRASHANTH ◽  
V. S. CHAKRAVARTHY

We present a computational model of basal ganglia as a key player in exploratory behavior. The model describes exploration of a virtual rat in a simulated water pool experiment. The virtual rat is trained using a reward-based or reinforcement learning paradigm which requires units with stochastic behavior for exploration of the system's state space. We model the Subthalamic Nucleus-Globus Pallidus externa (STN-GPe) segment of the basal ganglia as a pair of neuronal layers with oscillatory dynamics, exhibiting a variety of dynamic regimes such as chaos, traveling waves and clustering. Invoking the property of chaotic systems to explore state-space, we suggest that the complex exploratory dynamics of STN-GPe system in conjunction with dopamine-based reward signaling from the Substantia Nigra pars compacta (SNc) present the two key ingredients of a reinforcement learning system.


2017 ◽  
Vol 41 (S1) ◽  
pp. S10-S10
Author(s):  
T. Maia

BackgroundTourette syndrome (TS) has long been thought to involve dopaminergic disturbances, given the effectiveness of antipsychotics in diminishing tics. Molecular-imaging studies have, by and large, confirmed that there are specific alterations in the dopaminergic system in TS. In parallel, multiple lines of evidence have implicated the motor cortico-basal ganglia-thalamo-cortical (CBGTC) loop in TS. Finally, several studies demonstrate that patients with TS exhibit exaggerated habit learning. This talk will present a computational theory of TS that ties together these multiple findings.MethodsThe computational theory builds on computational reinforcement-learning models, and more specifically on a recent model of the role of the direct and indirect basal-ganglia pathways in learning from positive and negative outcomes, respectively.ResultsA model defined by a small set of equations that characterize the role of dopamine in modulating learning and excitability in the direct and indirect pathways explains, in an integrated way: (1) the role of dopamine in the development of tics; (2) the relation between dopaminergic disturbances, involvement of the motor CBGTC loop, and excessive habit learning in TS; (3) the mechanism of action of antipsychotics in TS; and (4) the psychological and neural mechanisms of action of habit-reversal training, the main behavioral therapy for TS.ConclusionsA simple computational model, thoroughly grounded on computational theory and basic-science findings concerning dopamine and the basal ganglia, provides an integrated, rigorous mathematical explanation for a broad range of empirical findings in TS.Disclosure of interestThe author has not supplied his declaration of competing interest.


2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Graham Findlay ◽  
Giulio Tononi ◽  
Chiara Cirelli

Abstract The term hippocampal replay originally referred to the temporally compressed reinstantiation, during rest, of sequential neural activity observed during prior active wake. Since its description in the 1990s, hippocampal replay has often been viewed as the key mechanism by which a memory trace is repeatedly rehearsed at high speeds during sleep and gradually transferred to neocortical circuits. However, the methods used to measure the occurrence of replay remain debated, and it is now clear that the underlying neural events are considerably more complicated than the traditional narratives had suggested. “Replay-like” activity happens during wake, can play out in reverse order, may represent trajectories never taken by the animal, and may have additional functions beyond memory consolidation, from learning values and solving the problem of credit assignment to decision-making and planning. Still, we know little about the role of replay in cognition, and to what extent it differs between wake and sleep. This may soon change, however, because decades-long efforts to explain replay in terms of reinforcement learning (RL) have started to yield testable predictions and possible explanations for a diverse set of observations. Here, we (1) survey the diverse features of replay, focusing especially on the latest findings; (2) discuss recent attempts at unifying disparate experimental results and putatively different cognitive functions under the banner of RL; (3) discuss methodological issues and theoretical biases that impede progress or may warrant a partial revaluation of the current literature, and finally; (4) highlight areas of considerable uncertainty and promising avenues of inquiry.


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