A model of dopamine modulated cortical activation

Ashby, F. Gregory; Casale, Michael

doi:10.1016/s0893-6080(03)00051-0

Cited by 22 publications

(18 citation statements)

References 41 publications

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“…The variance

σ_{E}^{2}

increases with trial-by-trial variability in the participant’s perception of the stimulus and memory of the decision criterion (i.e., perceptual and criterial noise). As argued in Ashby and Casale (2003),

σ_{E}^{2}

is inversely related to cortical DA levels (for similar arguments, see also Durstewitz & Seamans, 2008; Frank, 2005; Moustafa & Gluck, 2010). …”

Section: A Computational Implementation Of Covissupporting

confidence: 57%

“…When a <1, the selection becomes more deterministic—the rule with the greatest weight is almost always chosen. Hence, a can be interpreted as a gain parameter and, as such, COVIS assumes that a increases with cortical DA (Ashby & Casale, 2003; for similar arguments, see Durstewitz & Seamans, 2008; Frank, 2005; Moustafa & Gluck, 2010). …”

Section: A Computational Implementation Of Covismentioning

confidence: 99%

“…Thus, the selection parameter λ should increase with DA levels in frontal cortex, and the switching parameter γ is assumed to decrease with increased DA levels in the caudate nucleus. In addition, it has been argued that DA in the prefrontal cortex increases signal-to-noise ratio (Ashby & Casale, 2003; Frank, 2005; Durstewitz & Seamans, 2008; Moustafa & Gluck, 2010). Hence, a in Eq.…”

Section: Modeling Parkinson’s Disease With Covismentioning

confidence: 99%

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A neurocomputational account of cognitive deficits in Parkinson's disease

2012

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Parkinson’s disease (PD) is caused by the accelerated death of dopamine (DA) producing neurons. Numerous studies documenting cognitive deficits of PD patients have revealed impairments in a variety of tasks related to memory, learning, visuospatial skills, and attention. While there have been several studies documenting cognitive deficits of PD patients, very few computational models have been proposed. In this article, we use the COVIS model of category learning to simulate DA depletion and show that the model suffers from cognitive symptoms similar to those of human participants affected by PD. Specifically, DA depletion in COVIS produced deficits in rule-based categorization, non-linear information-integration categorization, probabilistic classification, rule maintenance, and rule switching. These were observed by simulating results from younger controls, older controls, PD patients, and severe PD patients in five well-known tasks. Differential performance among the different age groups and clinical populations was modeled simply by changing the amount of DA available in the model. This suggests that COVIS may not only be an adequate model of the simulated tasks and phenomena but also more generally of the role of DA in these tasks and phenomena.

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“…The variance

σ_{E}^{2}

σ_{E}^{2}

is inversely related to cortical DA levels (for similar arguments, see also Durstewitz & Seamans, 2008; Frank, 2005; Moustafa & Gluck, 2010). …”

Section: A Computational Implementation Of Covissupporting

confidence: 57%

Section: A Computational Implementation Of Covismentioning

confidence: 99%

Section: Modeling Parkinson’s Disease With Covismentioning

confidence: 99%

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A neurocomputational account of cognitive deficits in Parkinson's disease

2012

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“…More specifically, DA acts to increase the signal-to-noise ratio in neurons that are targets of glutamate neurons. In particular, increasing DA levels potentiate the response of strong glutamate signals and dampen the response of weak glutamate signals (Ashby & Casale, 2003; Cohen & Servan-Schreiber, 1992). Visual cortex sends prominent projections to the striatum and to many areas of frontal cortex, all of which are targets of DA neurons.…”

Section: Discussionmentioning

confidence: 99%

Dopamine dependence in aggregate feedback learning: A computational cognitive neuroscience approach

Valentin

Maddox

Ashby

2016

Brain and Cognition

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Procedural learning of skills depends on dopamine-mediated striatal plasticity. Most prior work investigated single stimulus-response procedural learning followed by feedback. However, many skills include several actions that must be performed before feedback is available. A new procedural-learning task is developed in which three independent and successive unsupervised categorization responses receive aggregate feedback indicating either that all three responses were correct, or at least one response was incorrect. Experiment 1 showed superior learning of stimuli in position 3, and that learning in the first two positions was initially compromised, and then recovered. An extensive theoretical analysis that used parameter space partitioning found that a large class of procedural-learning models, which predict propagation of dopamine release from feedback to stimuli, and/or an eligibility trace, fail to fully account for these data. The analysis also suggested that any dopamine released to the second or third stimulus impaired categorization learning in the first and second positions. A second experiment tested and confirmed a novel prediction of this large class of procedural-learning models that if the to-be-learned actions are introduced one-by-one in succession then learning is much better if training begins with the first action (and works forwards) than if it begins with the last action (and works backwards).

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“…Finally, the notion that dopamine modulates synaptic gain plays a key role in several proposals. It has been argued for in [50] and used in several Parkinson's disease modelling papers [51], [52].…”

Section: Introductionmentioning

confidence: 99%

Dopamine, Affordance and Active Inference

et al. 2012

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The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.

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A model of dopamine modulated cortical activation

Cited by 22 publications

References 41 publications

A neurocomputational account of cognitive deficits in Parkinson's disease

A neurocomputational account of cognitive deficits in Parkinson's disease

Dopamine dependence in aggregate feedback learning: A computational cognitive neuroscience approach

Dopamine, Affordance and Active Inference

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