Computational models simulating electrophysiological activity in the basal ganglia

2007 ◽  
pp. 505-511 ◽  
Author(s):  
Konstantina S. Nikita ◽  
G. L. Tsirogiannis
Keyword(s):  
Basal Ganglia ◽  
Computational Models ◽  
Electrophysiological Activity

scholarly journals Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia

2006 ◽  
Vol 18 (2) ◽  
pp. 283-328 ◽  
Author(s):  
Randall C. O'Reilly ◽  
Michael J. Frank
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Basal Ganglia ◽  
Computational Model ◽  
Computational Models ◽  
Memory Task ◽  
Learning Mechanisms ◽  
Subcortical Structures ◽  
Credit Assignment ◽  
Gating Mechanism

The prefrontal cortex has long been thought to subserve both working memory (the holding of information online for processing) and executive functions (deciding how to manipulate working memory and perform processing). Although many computational models of working memory have been developed, the mechanistic basis of executive function remains elusive, often amounting to a homunculus. This article presents an attempt to deconstruct this homunculus through powerful learning mechanisms that allow a computational model of the prefrontal cortex to control both itself and other brain areas in a strategic, task-appropriate manner. These learning mechanisms are based on subcortical structures in the midbrain, basal ganglia, and amygdala, which together form an actor-critic architecture. The critic system learns which prefrontal representations are task relevant and trains the actor, which in turn provides a dynamic gating mechanism for controlling working memory updating. Computationally, the learning mechanism is designed to simultaneously solve the temporal and structural credit assignment problems. The model's performance compares favorably with standard backpropagation-based temporal learning mechanisms on the challenging 1-2-AX working memory task and other benchmark working memory tasks.

scholarly journals Intrinsic Timescales Across the Basal Ganglia

2021 ◽  
Author(s):  
Simon Nougaret ◽  
Valeria Fascianelli ◽  
Sabrina Ravel ◽  
Aldo Genovesio
Keyword(s):  
Basal Ganglia ◽  
Computational Models ◽  
Complex Dynamics ◽  
Baseline Period ◽  
Autocorrelation Analysis ◽  
Subcortical Structures ◽  
Neuronal Populations ◽  
Visuomotor Task ◽  
Cortical Hierarchy ◽  
To Receive

Recent studies have shown that neuronal stability over time can be estimated by the structure of the spike-count autocorrelation of neuronal populations. This estimation, called the intrinsic timescale, has been computed for several cortical areas and can be used to propose a cortical hierarchy reflecting a scale of temporal receptive windows between areas. In this study, we performed an autocorrelation analysis on neuronal populations of three basal ganglia (BG) nuclei, including the striatum and the subthalamic nucleus (STN), the input structures of the BG, and the external globus pallidus (GPe). The analysis was performed during the baseline period of a motivational visuomotor task in which monkeys had to apply different amounts of force to receive a different amount of reward. We found that the striatum and the STN have longer intrinsic timescales than the GPe. Moreover, our results allow for the placement of these subcortical structures within the already-defined scale of cortical temporal receptive windows. Estimates of intrinsic timescales are important in adding further constraints in the development of computational models of the complex dynamics among these nuclei and throughout cortico-BG-thalamo-cortical loops.

scholarly journals Intrinsic timescales across the basal ganglia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Nougaret ◽  
Valeria Fascianelli ◽  
Sabrina Ravel ◽  
Aldo Genovesio
Keyword(s):  
Basal Ganglia ◽  
Computational Models ◽  
Complex Dynamics ◽  
Temporal Stability ◽  
Baseline Period ◽  
Subcortical Structures ◽  
Neuronal Populations ◽  
Visuomotor Task ◽  
Cortical Hierarchy ◽  
To Receive

AbstractRecent studies have shown that temporal stability of the neuronal activity over time can be estimated by the structure of the spike-count autocorrelation of neuronal populations. This estimation, called the intrinsic timescale, has been computed for several cortical areas and can be used to propose a cortical hierarchy reflecting a scale of temporal receptive windows between areas. In this study, we performed an autocorrelation analysis on neuronal populations of three basal ganglia (BG) nuclei, including the striatum and the subthalamic nucleus (STN), the input structures of the BG, and the external globus pallidus (GPe). The analysis was performed during the baseline period of a motivational visuomotor task in which monkeys had to apply different amounts of force to receive different amounts of reward. We found that the striatum and the STN have longer intrinsic timescales than the GPe. Moreover, our results allow for the placement of these subcortical structures within the already-defined scale of cortical temporal receptive windows. Estimates of intrinsic timescales are important in adding further constraints in the development of computational models of the complex dynamics among these nuclei and throughout cortico-BG-thalamo-cortical loops.

Intact Proactive Motor Inhibition after Unilateral Prefrontal Cortex or Basal Ganglia Lesions

2021 ◽  
pp. 1-18
Author(s):  
Matthias Liebrand ◽  
Anne-Kristin Solbakk ◽  
Ingrid Funderud ◽  
Macià Buades-Rotger ◽  
Robert T. Knight ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Basal Ganglia ◽  
Contingent Negative Variation ◽  
Motor Preparation ◽  
Motor Inhibition ◽  
Critical Importance ◽  
Electrophysiological Activity ◽  
Preparatory Attention ◽  
External Signals

Previous research provided evidence for the critical importance of the prefrontal cortex (pFC) and basal ganglia (BG) for reactive motor inhibition, that is, when actions are cancelled in response to external signals. Less is known about the role of the pFC and BG in proactive motor inhibition, referring to preparation for an upcoming stop signal. In this study, patients with unilateral lesions to the BG or lateral pFC performed in a cued go/no-go task, whereas their EEG was recorded. The paradigm called for cue-based preparation for upcoming, lateralized no-go signals. Based on previous findings, we focused on EEG indices of cognitive control (prefrontal beta), motor preparation (sensorimotor mu/beta, contingent negative variation [CNV]), and preparatory attention (occipital alpha, CNV). On a behavioral level, no differences between patients and controls were found, suggesting an intact ability to proactively prepare for motor inhibition. Patients showed an altered preparatory CNV effect, but no other differences in electrophysiological activity related to proactive and reactive motor inhibition. Our results suggest a context-dependent role of BG and pFC structures in motor inhibition, being critical in reactive, unpredictable contexts, but less so in situations where one can prepare for stopping on a short timescale.

Computational models of the basal ganglia

2000 ◽  
Vol 15 (5) ◽  
pp. 762-770 ◽  
Author(s):  
Andrew Gillies ◽  
Gordon Arbuthnott
Keyword(s):  
Basal Ganglia ◽  
Computational Models

scholarly journals Insights into Parkinson’s disease from computational models of the basal ganglia

2018 ◽  
Author(s):  
Mark D. Humphries ◽  
Jose Obeso ◽  
Jakob Kisbye Dreyer
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Computational Models ◽  
Neural Circuits ◽  
Computational Modelling ◽  
Beta Band ◽  
Complex Interplay ◽  
Deep Brain

AbstractMovement disorders arise from the complex interplay of multiple changes to neural circuits. Successful treatments for these disorders could interact with these complex changes in myriad ways, and as a consequence their mechanisms of action and their amelioration of symptoms are incompletely understood. Using Parkinson’s disease as a case-study, we review here how computational models are a crucial tool for taming this complexity, across causative mechanisms, consequent neural dynamics, and treatments. For mechanisms, we review models that capture the effects of losing dopamine on basal ganglia function; for dynamics, we discuss models that have transformed our understanding of how beta-band (15-30 Hz) oscillations arise in the parkinsonian basal ganglia. For treatments, we touch on the breadth of computational modelling work trying to understand the therapeutic actions of deep brain stimulation. Collectively, models from across all levels of description are providing a compelling account of the causes, symptoms, and treatments for Parkinson’s disease.

scholarly journals Insights into Parkinson’s disease from computational models of the basal ganglia

2018 ◽  
Vol 89 (11) ◽  
pp. 1181-1188 ◽  
Author(s):  
Mark D Humphries ◽  
Jose Angel Obeso ◽  
Jakob Kisbye Dreyer
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Computational Models ◽  
Neural Circuits ◽  
Computational Modelling ◽  
Beta Band ◽  
Complex Interplay ◽  
Deep Brain

Movement disorders arise from the complex interplay of multiple changes to neural circuits. Successful treatments for these disorders could interact with these complex changes in myriad ways, and as a consequence their mechanisms of action and their amelioration of symptoms are incompletely understood. Using Parkinson’s disease as a case study, we review here how computational models are a crucial tool for taming this complexity, across causative mechanisms, consequent neural dynamics and treatments. For mechanisms, we review models that capture the effects of losing dopamine on basal ganglia function; for dynamics, we discuss models that have transformed our understanding of how beta-band (15–30 Hz) oscillations arise in the parkinsonian basal ganglia. For treatments, we touch on the breadth of computational modelling work trying to understand the therapeutic actions of deep brain stimulation. Collectively, models from across all levels of description are providing a compelling account of the causes, symptoms and treatments for Parkinson’s disease.

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