Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism

By Michael J Frank. Journal of Cognitive Neuroscience, 2005

977 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neurology

View Paper: https://www.semanticscholar.org/paper/8116cdbcc80e8cfd63554a1346eccd49d32666f1

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Citations in the corpus

Computational models of reinforcement learning: the role of dopamine as a reward signal (2010, background): “This enables the network to be more likely to repeat actions that are associated with positive reward prediction errors (Frank 2005), and is consistent with recent studies of synaptic plasticity showing D1-dependent LTP in striatonigral cells and D2-dependent LTD in striatopallidal cells (Shen et al”
Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy (2013, background): “Recent computational models mostly follow Mink’s (1996) suggestion (e.g., Gurney et al., 2001a,b; Suri et al., 2001; Brown et al., 2004; Frank, 2005; Ashby et al., 2007; Schroll et al., 2012) and applied it to cognitive operations as well (e.g., O’Reilly and Frank, 2006; Schroll et al., 2012).”
THE ROLE OF THE BASAL GANGLIA IN CATEGORY LEARNING (2006, background): “According to Frank (2005) , the direct and indirect pathways mediate the learning of GO and NO GO responses, respectively.”
Adolescence as a neurobiological critical period for the development of higher-order cognition (2018, neutral): “Specifically, DA activity supports reward processing and reinforcement by signaling the reward expectancy and value of action outcomes (Clarke et al., 2014; Dreher et al., 2009; Frank, 2005; Hariri, 2009; Luciana et al., 2012; Schultz et al., 1997).”
The Computational Anatomy of Psychosis (2013, methodology): “It is important to note that DA-R subtypes have opposite effects on synaptic gain: D1R activation stimulates cAMP production and increases the excitability of depolarized neurons, whereas D2R activation inhibits cAMP production and reduces gain (reviewed in Frank, 2005).”
Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia (2006, methodology): “This model has direct implications for understanding executive dysfunction in neurological disorders such as ADHD and Parkinson’s disease, which involve the interaction between dopamine, basal ganglia, and prefrontal cortex (e.g., Frank, Seeberger, & O’Reilly, 2004; Frank, 2005 ).”
Cognitive control over learning: creating, clustering, and generalizing task-set structure. (2013, positive): “This combination of mechanisms has been shown to produce adaptive learning in complex probabilistic reinforcement environments using solely RL (e.g., Frank, 2005).”
How Prediction Errors Shape Perception, Attention, and Motivation (2012, background): “These opposite effects are suggested to reflect modulation of distinct striatal pathways that mediate activation and inhibition of responses via frontostriatal coupling changes (Frank, 2005).”
Dopamine, Affordance and Active Inference (2012, methodology): “It has been argued for in [50] and used in several Parkinson’s disease modelling papers [51,52].”
A computational model of inhibitory control in frontal cortex and basal ganglia. (2011, methodology): “The architecture of the core model is similar to Frank (2006). While the original model simulated manual motor responses, our model features a slightly adapted architecture in accordance to the neuroanatomy and physiology underlying rapid eye-movements (i.e., saccades) as reviewed in Hikosaka (2007)”
Dopamine in motivational control: rewarding, aversive, and alerting (2010, background): “…dopamine can also exert immediate control over neural circuits by modulating neural spiking activity and synaptic connections between neurons (Surmeier et al., 2007; Robbins and Arnsten, 2009), in some cases doing so in a manner that would promote immediate reward-seeking actions (Frank, 2005).”
Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. (2014, methodology): “This has been proposed as the mechanism by which DA promotes approach learning in the direct pathway and avoidance learning in the indirect pathway (Frank, 2005), with opposite-coding but apparently redundant representations of action values and learning.”
Goals and Habits in the Brain (2013, neutral): “…as Neuron 80, October 16, 2013 ª2013 The Authors 317 reporting the temporal difference prediction error for reward (Montague et al., 1996; Schultz et al., 1997) that underpins model-free evaluation and control via its influence over activity and plasticity (Reynolds et al., 2001; Frank, 2005).”
Towards an executive without a homunculus: computational models of the prefrontal cortex/basal ganglia system (2007, methodology): “This functionality enables the BG system to learn to discriminate between subtly different reinforcement values of alternative responses (Frank 2005) and is consistent with several lines of biological and behavioural evidence (for review see Frank & O'Reilly 2006).”