scholarly journals Computational Modelling of Cerebellar Magnetic Stimulation: The Effect of Washout

2021 ◽  
pp. 35-46
Author(s):  
Alberto Antonietti ◽  
Claudia Casellato ◽  
Egidio D’Angelo ◽  
Alessandra Pedrocchi
Keyword(s):  
Washout Period ◽  
Magnetic Stimulation ◽  
Computational Models ◽  
Computational Modelling ◽  
Mechanistic Explanation ◽  
Neural Responses ◽  
External Stimulation ◽  
Associative Conditioning ◽  
Underlying Mechanisms ◽  
The Brain

AbstractNowadays, clinicians have multiple tools that they can use to stimulate the brain, by means of electric or magnetic fields that can interfere with the bio-electrical behaviour of neurons. However, it is still unclear which are the neural mechanisms that are involved and how the external stimulation changes the neural responses at network-level. In this paper, we have exploited the simulations carried out using a spiking neural network model, which reconstructed the cerebellar system, to shed light on the underlying mechanisms of cerebellar Transcranial Magnetic Stimulation affecting specific task behaviour. Namely, two computational studies have been merged and compared. The two studies employed a very similar experimental protocol: a first session of Pavlovian associative conditioning, the administration of the TMS (effective or sham), a washout period, and a second session of Pavlovian associative conditioning. In one study, the washout period between the two sessions was long (1 week), while the other study foresaw a very short washout (15 min). Computational models suggested a mechanistic explanation for the TMS effect on the cerebellum. In this work, we have found that the duration of the washout strongly changes the modification of plasticity mechanisms in the cerebellar network, then reflected in the learning behaviour.

scholarly journals Transient brain networks underlying interpersonal strategies during synchronized action

2020 ◽  
Author(s):  
Ole Adrian Heggli ◽  
Ivana Konvalinka ◽  
Joana Cabral ◽  
Elvira Brattico ◽  
Morten L Kringelbach ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Human Interaction ◽  
Action Perception ◽  
Network Information ◽  
Mutual Adaptation ◽  
Underlying Mechanisms ◽  
The Brain

Abstract Interpersonal coordination is a core part of human interaction, and its underlying mechanisms have been extensively studied using social paradigms such as joint finger-tapping. Here, individual and dyadic differences have been found to yield a range of dyadic synchronization strategies, such as mutual adaptation, leading–leading, and leading–following behaviour, but the brain mechanisms that underlie these strategies remain poorly understood. To identify individual brain mechanisms underlying emergence of these minimal social interaction strategies, we contrasted EEG-recorded brain activity in two groups of musicians exhibiting the mutual adaptation and leading–leading strategies. We found that the individuals coordinating via mutual adaptation exhibited a more frequent occurrence of phase-locked activity within a transient action–perception-related brain network in the alpha range, as compared to the leading–leading group. Furthermore, we identified parietal and temporal brain regions that changed significantly in the directionality of their within-network information flow. Our results suggest that the stronger weight on extrinsic coupling observed in computational models of mutual adaptation as compared to leading–leading might be facilitated by a higher degree of action–perception network coupling in the brain.

scholarly journals Developmental Computational Psychiatry

2018 ◽  
Author(s):  
Tobias U. Hauser ◽  
Geert-Jan Will ◽  
Magda Dubois ◽  
Raymond J Dolan
Keyword(s):  
Cognitive Development ◽  
Psychiatric Disorders ◽  
Cognitive Processes ◽  
Computational Modelling ◽  
Brain Maturation ◽  
Childhood And Adolescence ◽  
Social Evaluation ◽  
Computational Psychiatry ◽  
Underlying Mechanisms ◽  
The Brain

Most psychiatric disorders emerge during childhood and adolescence. This is also a period when the brain undergoes substantial growth and reorganisation. However, it remains unclear how a heightened vulnerability to psychiatric disorder relates to brain maturation, and what the underlying mechanisms might be. Here, we propose ‘developmental computational psychiatry’ as a framework for linking brain maturation to cognitive development. We propose that through modelling some of the brain’s fundamental cognitive computations and relating them to brain development, we can bridge the gap between brain and cognitive development. This in turn can lead to a richer understanding of the ontogeny of psychiatric disorders. We illustrate this perspective by taking examples from reinforcement learning (RL) and dopamine function, showing how computational modelling deepens an understanding of how cognitive processes, such as reward learning, effort learning, and social evaluation might go awry in psychiatric disorders. Finally, we formulate testable hypotheses and sketch the potential and limitations of developmental computational psychiatry.

scholarly journals Computational modelling of social cognition and behaviour—a reinforcement learning primer

2020 ◽  
Author(s):  
Patricia L Lockwood ◽  
Miriam C Klein-Flügge
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Observational Learning ◽  
Impression Formation ◽  
Computational Models ◽  
Computational Modelling ◽  
Neural Systems ◽  
Neural Basis ◽  
The Past ◽  
The Brain

Abstract Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

scholarly journals The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing

2020 ◽  
Author(s):  
Martin Schrimpf ◽  
Idan Blank ◽  
Greta Tuckute ◽  
Carina Kauf ◽  
Eghbal A. Hosseini ◽  
...  
Keyword(s):  
Reverse Engineering ◽  
Language Processing ◽  
Brain Function ◽  
Computational Models ◽  
Predictive Processing ◽  
Neural Responses ◽  
Data Types ◽  
Neural Architecture ◽  
And Behavior ◽  
The Brain

AbstractThe neuroscience of perception has recently been revolutionized with an integrative reverse-engineering approach in which computation, brain function, and behavior are linked across many different datasets and many computational models. We here present a first systematic study taking this approach into higher-level cognition: human language processing, our species’ signature cognitive skill. We find that the most powerful ‘transformer’ networks predict neural responses at nearly 100% and generalize across different datasets and data types (fMRI, ECoG). Across models, significant correlations are observed among all three metrics of performance: neural fit, fit to behavioral responses, and accuracy on the next-word prediction task (but not other language tasks), consistent with the long-standing hypothesis that the brain’s language system is optimized for predictive processing. Model architectures with initial weights further perform surprisingly similar to final trained models, suggesting that inherent structure – and not just experience with language – crucially contributes to a model’s match to the brain.

Computational neurostimulation

2021 ◽  
Author(s):  
Ainslie Johnstone ◽  
James J. Bonaiuto ◽  
Sven Bestmann
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Motor Cortex ◽  
Magnetic Stimulation ◽  
Computational Models ◽  
Primary Motor Cortex ◽  
Health And Disease ◽  
Small Footprint ◽  
The Impact ◽  
The Brain ◽  
Insight Into

Computational neurostimulation is the use of biologically grounded computational models to investigate the mechanism of action of brain stimulation and predict the impact of transcranial magnetic stimulation (TMS) on behavior in health and disease. Computational models are now widespread, and their success is incontrovertible, yet they have left a rather small footprint on the field of TMS. We highlight and discuss recent advances in models of primary motor cortex TMS, the brain region for which most models have been developed. These models provide insight into the putative, but unobservable, mechanisms through which TMS influences physiology, and help predicting the effects of different TMS applications. We discuss how these advances in computational neurostimulation provide opportunities for mechanistically understanding and predicting the impact of TMS on behavior.

Using Computational Modelling to Understand Cognition in the Ventral Visual-Perirhinal Pathway

2011 ◽  
pp. 15-45 ◽  
Author(s):  
Rosemary A. Cowell ◽  
Timothy J. Bussey ◽  
Lisa M. Saksida
Keyword(s):  
Visual Memory ◽  
Computational Models ◽  
Computational Modelling ◽  
Modular Organization ◽  
Object Representations ◽  
Object Processing ◽  
Representational System ◽  
Object Recognition Memory ◽  
And Function ◽  
The Brain

The authors present a series of studies in which computational models are used as a tool to examine the organization and function of the ventral visual-perirhinal stream in the brain. The prevailing theoretical view in this area of cognitive neuroscience holds that the object-processing pathway has a modular organization, in which visual perception and visual memory are carried out independently. They use computational simulations to demonstrate that the effects of brain damage on both visual discrimination and object recognition memory may not be due to an impairment in a specific function such as memory or perception, but are more likely due to compromised object representations in a hierarchical and continuous representational system. The authors argue that examining the nature of stimulus representations and their processing in cortex is a more fruitful approach than attempting to map cognition onto functional modules.

scholarly journals Computational modelling of social cognition and behaviour – a reinforcement learning primer

2019 ◽  
Author(s):  
Patricia Lockwood ◽  
Miriam Klein-Flugge
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Observational Learning ◽  
Impression Formation ◽  
Computational Models ◽  
Computational Modelling ◽  
Neural Systems ◽  
Neural Basis ◽  
The Past ◽  
The Brain

Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalising and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

scholarly journals Invertebrate central pattern generator circuits

2010 ◽  
Vol 365 (1551) ◽  
pp. 2329-2345 ◽  
Author(s):  
Allen I. Selverston
Keyword(s):  
Central Pattern Generator ◽  
Computational Models ◽  
Mechanistic Explanation ◽  
Pattern Generator ◽  
Individual Organism ◽  
Multiple Phase ◽  
Brain Circuits ◽  
Sufficient Detail ◽  
Cellular Components ◽  
The Brain

There are now a reasonable number of invertebrate central pattern generator (CPG) circuits described in sufficient detail that a mechanistic explanation of how they work is possible. These small circuits represent the best-understood neural circuits with which to investigate how cell-to-cell synaptic connections and individual channel conductances combine to generate rhythmic and patterned output. In this review, some of the main lessons that have appeared from this analysis are discussed and concrete examples of circuits ranging from single phase to multiple phase patterns are described. While it is clear that the cellular components of any CPG are basically the same, the topology of the circuits have evolved independently to meet the particular motor requirements of each individual organism and only a few general principles of circuit operation have emerged. The principal usefulness of small systems in relation to the brain is to demonstrate in detail how cellular infrastructure can be used to generate rhythmicity and form specialized patterns in a way that may suggest how similar processes might occur in more complex systems. But some of the problems and challenges associated with applying data from invertebrate preparations to the brain are also discussed. Finally, I discuss why it is useful to have well-defined circuits with which to examine various computational models that can be validated experimentally and possibly applied to brain circuits when the details of such circuits become available.

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