312 Hits in 13.2 sec

An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning

Pragathi P. Balasubramani, V. Srinivasa Chakravarthy, Balaraman Ravindran, Ahmed A. Moustafa
2014 Frontiers in Computational Neuroscience  
The model depicts the roles of dopamine (DA) and serotonin (5HT) in Basal Ganglia (BG).  ...  In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL)-framework.  ...  DISCUSSION MAIN FINDINGS OF THE MODEL Reinforcement Learning framework has been used extensively to model the function of basal ganglia (Frank et al., 2007; Chakravarthy et al., 2010; Krishnan et al  ... 
doi:10.3389/fncom.2014.00047 pmid:24795614 pmcid:PMC3997037 fatcat:zvh3f3iuljan5askvdljwabsqm

Neuromodulatory Systems and Their Interactions: A Review of Models, Theories, and Experiments

Michael C. Avery, Jeffrey L. Krichmar
2017 Frontiers in Neural Circuits  
The fact that these systems interact with each other either directly or indirectly, however, makes it difficult to understand how a failure in one or more systems can lead to a particular symptom or pathology  ...  Better understanding of neuromodulatory systems may lead to the development of novel treatment strategies for a number of brain disorders.  ...  AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.  ... 
doi:10.3389/fncir.2017.00108 pmid:29311844 pmcid:PMC5744617 fatcat:32fdae4325hqfjqt27z6s2vegq

Reinforcement learning: The Good, The Bad and The Ugly

Peter Dayan, Yael Niv
2008 Current Opinion in Neurobiology  
Reinforcement learning provides both qualitative and quantitative frameworks for understanding and modeling adaptive decision-making in the face of rewards and punishments.  ...  Here we review the latest dispatches from the forefront of this field, and map out some of the territories where lie monsters.  ...  Acknowledgements We are very grateful to Peter Bossaerts, Nathaniel Daw, Michael Frank, Russell Poldrack, Daniel Salzman, Ben Seymour and Wako Yoshida for their helpful comments on a previous version of  ... 
doi:10.1016/j.conb.2008.08.003 pmid:18708140 fatcat:tksb6jf6gjccrhvnibn6p4tbdm

Decision Making: From Neuroscience to Psychiatry

Daeyeol Lee
2013 Neuron  
However, it is often difficult to identify optimal behaviors in real life due to the complexity of the decision maker's environment and social dynamics.  ...  As a result, although many different brain areas and circuits are involved in decision making, evolutionary and learning solutions adopted by individual decision makers sometimes produce suboptimal outcomes  ...  The author's research is supported by the National Institute of Health (DA029330 and DA027844).  ... 
doi:10.1016/j.neuron.2013.04.008 pmid:23622061 pmcid:PMC3670825 fatcat:trbrm24zc5e6pgonyhhfvxl7vm

A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making

Pragathi P. Balasubramani, V. Srinivasa Chakravarthy, Balaraman Ravindran, Ahmed A. Moustafa
2015 Frontiers in Computational Neuroscience  
is shown to capture reward-punishment and risk based decision making impairment in Parkinson's Disease (PD).  ...  The model predicts that optimizing 5HT levels along with DA medications might be essential for improving the patients' reward-punishment learning deficits.  ...  There is significant evidence that in addition to reward-punishment based decision making, the Basal Ganglia (BG) contributes to risk-based decision making .  ... 
doi:10.3389/fncom.2015.00076 pmid:26136679 pmcid:PMC4469836 fatcat:neet5fadbbg4zm22xj4zjjmf3y

Modeling Neuromodulation as a Framework to Integrate Uncertainty in General Cognitive Architectures [chapter]

Frédéric Alexandre, Maxime Carrere
2016 Lecture Notes in Computer Science  
, resulting in appropriate changes in cerebral functioning and learning modes.  ...  The original contribution of this paper is to relate the four major neuromodulators to four fundamental dimensions of uncertainty.  ...  In the framework of decision making, the distinction between stationary and unstationary environments can be presented as follows: when you try to associate an action to a stimulus to get a reward, you  ... 
doi:10.1007/978-3-319-41649-6_33 fatcat:zquuqa5uzzfsdbbuqwybbypmhm

Neural and neurochemical basis of reinforcement-guided decision making

Abbas Khani, Gregor Rainer
2016 Journal of Neurophysiology  
Reinforcement-guided decision making models are based on economic and reinforcement learning theories, and their focus is on the maximization of acquired benefit over a defined period of time.  ...  Neuromodulation of reward-based learning and decision making in human aging. Ann NY Acad Sci 1235: 1-17, 2011. Fellows LK.  ...  An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning.  ... 
doi:10.1152/jn.01113.2015 pmid:27226454 fatcat:wpqjzj2sxnh2dh5lfqw5pszuhq

Reinforcement learning, conditioning, and the brain: Successes and challenges

Tiago V. Maia
2009 Cognitive, Affective, & Behavioral Neuroscience  
This article provides an introduction to reinforcement learning followed by an examination of the successes and challenges using reinforcement learning to understand the neural bases of conditioning.  ...  Successes reviewed include (1) the mapping of positive and negative prediction errors to the firing of dopamine neurons and neurons in the lateral habenula, respectively; (2) the mapping of model-based  ...  Reinforcement learning provides an integrated understanding of how agents can learn to behave so as to maximize rewards and minimize punishments-an ability that is the very pillar of survival and functioning  ... 
doi:10.3758/cabn.9.4.343 pmid:19897789 fatcat:ovlgmuwlljeenhkjvix7lswqzy

Dissecting impulsivity and its relationships to drug addictions

J. David Jentsch, James R. Ashenhurst, M. Catalina Cervantes, Stephanie M. Groman, Alexander S. James, Zachary T. Pennington
2014 Annals of the New York Academy of Sciences  
We conclude that the available data strongly supports the notion that impulsivity is both a risk factor for, and a consequence of, drug and alcohol consumption.  ...  link "distinct" subtypes of impulsivity to low dopamine D2 receptor function and perturbed serotonergic transmission, revealing shared mechanisms between the subtypes.  ...  Dopamine, however, may influence non-affective aspects of decision-making related to learning and evaluating risk and reward levels.  ... 
doi:10.1111/nyas.12388 pmid:24654857 pmcid:PMC4360991 fatcat:a7qeidlg3nh23mef3kp3ayncyu

Neuromodulation of reward-based learning and decision making in human aging

Ben Eppinger, Dorothea Hämmerer, Shu-Chen Li
2011 Annals of the New York Academy of Sciences  
Moreover, emerging evidence points to age-related differences in the sensitivity to rewarding and aversive outcomes during learning and decision making if the acquisition of behavior critically depends  ...  Specifically, we focus on evidence suggesting that deficits in neuromodulation contribute to older adults' behavioral disadvantages in learning and decision making.  ...  Thus, on the one hand, it is necessary to understand how functional changes in these networks contribute to adult age differences in reward-based learning and decision making.  ... 
doi:10.1111/j.1749-6632.2011.06230.x pmid:22023564 pmcid:PMC3779838 fatcat:s3h2hepborbfxd3rlpofcmsnni

Nonhuman gamblers: lessons from rodents, primates, and robots

Fabio Paglieri, Elsa Addessi, Francesca De Petrillo, Giovanni Laviola, Marco Mirolli, Domenico Parisi, Giancarlo Petrosino, Marialba Ventricelli, Francesca Zoratto, Walter Adriani
2014 Frontiers in Behavioral Neuroscience  
, neuropsychiatry, evolutionary robotics), to make the case for a greater degree of methodological integration in future studies on pathological gambling.  ...  The search for neuronal and psychological underpinnings of pathological gambling in humans would benefit from investigating related phenomena also outside of our species.  ...  This work is based on a detailed model of the basal ganglia-thalamo-cortical circuit that is assumed to implement action selection and reinforcement learning (e.g., Frank et al., 2001) .  ... 
doi:10.3389/fnbeh.2014.00033 pmid:24574984 pmcid:PMC3920650 fatcat:wexrcrlonvb5palb6rdqi423rq

What do the basal ganglia do? A modeling perspective

V. S. Chakravarthy, Denny Joseph, Raju S. Bapi
2010 Biological cybernetics  
Basal ganglia (BG) constitute a network of 7 deep brain nuclei involved in a variety of crucial brain functions including: action selection, action gating, reward based learning, motor preparation, timing  ...  In this article, we review the existing modeling literature on BG and hypothesize an integrative picture of the function of these nuclei.  ...  Acknowledgments The authors acknowledge the many useful discussions they had with Gangadhar Garipelli and Sridharan Devarajan on various topics related to basal ganglia modeling.  ... 
doi:10.1007/s00422-010-0401-y pmid:20644953 fatcat:fm363yyghfhpzekmvwerw3a7oq

Identifying the Basal Ganglia Network Model Markers for Medication-Induced Impulsivity in Parkinson's Disease Patients

Pragathi Priyadharsini Balasubramani, V. Srinivasa Chakravarthy, Manal Ali, Balaraman Ravindran, Ahmed A. Moustafa, Osama Ali Abulseoud
2015 PLoS ONE  
In the model, the BG action selection dynamics were mimicked using a utility function based decision making framework, with DA controlling reward prediction and 5HT controlling punishment and risk predictions  ...  A neural network model of the Basal Ganglia (BG) that has the capacity to predict the dysfunction of both the dopaminergic (DA) and the serotonergic (5HT) neuromodulator systems was developed and used  ...  in our model of utility based decision making-where 5HT is modeled as a parameter affecting the risk prediction error [28] .  ... 
doi:10.1371/journal.pone.0127542 pmid:26042675 pmcid:PMC4456385 fatcat:35qden6z3vhsbiaq2cc6uuvnbm

Anatomy of a decision: Striato-orbitofrontal interactions in reinforcement learning, decision making, and reversal

Michael J. Frank, Eric D. Claus
2006 Psychological review  
The authors explore the division of labor between the basal ganglia-dopamine (BG-DA) system and the orbitofrontal cortex (OFC) in decision making.  ...  They show that a primitive neural network model of the BG-DA system slowly learns to make decisions on the basis of the relative probability of rewards but is not as sensitive to (a) recency or (b) the  ...  On the one hand, the basal ganglia (BG) and the neuromodulator dopamine (DA) are thought to participate in both action selection and reinforcement learning (Beiser & Houk, 1998; Brown, Bullock, & Grossberg  ... 
doi:10.1037/0033-295x.113.2.300 pmid:16637763 fatcat:flnp3bqm3fdwbmvqzdcy4jhyaa

Impulse control disorders in Parkinson's disease: seeking a roadmap toward a better understanding

Roberto Cilia, Thilo van Eimeren
2011 Brain Structure and Function  
, an "unnatural" increase of dopamine stimulation and a characteristic pattern of resulting functional changes in remote networks of appetitive drive and impulse control.  ...  The development of an impulse control disorder (ICD) is now recognized as a potential nonmotor adverse effect of dopamine replacement therapy in Parkinson's disease (PD).  ...  Strafella and Angelo Antonini for their generous advice and guidance in the recent years. The authors report no conflict of interests.  ... 
doi:10.1007/s00429-011-0314-0 pmid:21541715 pmcid:PMC3197927 fatcat:x2apsyrb2nd2bafmr4xbydlc44
« Previous Showing results 1 — 15 out of 312 results