Difference between revisions of "Pathways to the 2023 IHP thematic program Random Processes in the Brain/Seminars"
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− | | style="width:65%; padding:-10px" |{{Pathways to the 2023 IHP thematic program Random Processes in the Brain/ | + | | style="width:65%; padding:-10px" |{{Pathways to the 2023 IHP thematic program Random Processes in the Brain/Section|Markus Diesmann|2}} |
;Single-neuron model in cortical context | ;Single-neuron model in cortical context | ||
* Speaker: Markus Diesmann, Jülich Research Centre | * Speaker: Markus Diesmann, Jülich Research Centre | ||
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* Abstract: In the preparation of the 2023 IHP thematic program "Random Processes in the Brain" the question came up how relevant the single-neuron model is for cortical dynamics and function. Given the plethora of single-neuron models available, insight into their differential effects on the network level would give theoreticians guidance on which model to choose for which research question. The purpose of this talk is to outline a small project approaching this question which could be carried out in the framework of the thematic program in a collaboration of several labs. The talk first presents a well-studied full-density network model of the cortical microcircuit as a suitable reference network. The proposal is to replace the original single-neuron model by alternative common single-neuron models and to quantify the impact on the network level. For this purpose the presentation reviews a range of common single-neuron models as candidates and a set of measures like firing rate, irregularity, and the power spectrum. It seems achievable that all relevant neuron models can be formulated in the domain-specific language NESTML and data analysis be carried out in the Elephant framework such that a reproducible digital workflow for the project can be constructed. A minimal scope of the investigation covers a static network in a stationary state. However, there are indications in the literature that the conventional constraints on network activity are weak. Furthermore, hypotheses on the function of the cortical microcircuit depend on the transient interaction between cortical layers, synaptic plasticity, and a separation of dendritic and somatic compartments. Therefore, we need to carefully debate how the scope of an initial exploration can usefully be restricted. | * Abstract: In the preparation of the 2023 IHP thematic program "Random Processes in the Brain" the question came up how relevant the single-neuron model is for cortical dynamics and function. Given the plethora of single-neuron models available, insight into their differential effects on the network level would give theoreticians guidance on which model to choose for which research question. The purpose of this talk is to outline a small project approaching this question which could be carried out in the framework of the thematic program in a collaboration of several labs. The talk first presents a well-studied full-density network model of the cortical microcircuit as a suitable reference network. The proposal is to replace the original single-neuron model by alternative common single-neuron models and to quantify the impact on the network level. For this purpose the presentation reviews a range of common single-neuron models as candidates and a set of measures like firing rate, irregularity, and the power spectrum. It seems achievable that all relevant neuron models can be formulated in the domain-specific language NESTML and data analysis be carried out in the Elephant framework such that a reproducible digital workflow for the project can be constructed. A minimal scope of the investigation covers a static network in a stationary state. However, there are indications in the literature that the conventional constraints on network activity are weak. Furthermore, hypotheses on the function of the cortical microcircuit depend on the transient interaction between cortical layers, synaptic plasticity, and a separation of dendritic and somatic compartments. Therefore, we need to carefully debate how the scope of an initial exploration can usefully be restricted. | ||
− | {{Pathways to the 2023 IHP thematic program Random Processes in the Brain/ | + | {{Pathways to the 2023 IHP thematic program Random Processes in the Brain/Section|Peter F Liddle|2}} |
[[File:Disorganization_of_mental_activity_in_psychosis.jpg|200px|right]] | [[File:Disorganization_of_mental_activity_in_psychosis.jpg|200px|right]] | ||
;Disorganization of mental activity in psychosis | ;Disorganization of mental activity in psychosis | ||
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* Abstract: Many patients with psychotic illnesses including schizophrenia, suffer persisting disability despite treatment of delusions and hallucinations with antipsychotic medication. There is substantial evidence that disorganization of mental activity makes a major contribution to persisting disability, by disrupting thought, emotion and behaviour. Evidence suggests that this disorganization involves impaired recruitment of the relevant brain systems required to make sense of sensory input and achieve our goals. There is diminished engagement of relevant brain circuits, together with failure to suppress task-irrelevant brain activity. We propose that disorganization of mental activity reflects imprecision of the predictive coding that shapes perception and action. The brain generates internal models of the world that are successively updated in light of sensory information. What we perceive is determined by adjusting predictions to minimise discrepancy between prediction and sensory input. Motor actions are controlled by a forward model of the state of brain and body as intended action is executed. Action is continuously adjusted to minimize discrepancy between prediction and sensory input. Disorganization is associated with both imprecise timing and imprecise content of predictions. We need models that incorporate the interactions between excitatory and inhibitory neurons in local circuits with parameters representing long range communication between brain regions to help us understand the pathophysiological mechanism responsible for imprecise predictive coding in psychotic illness. | * Abstract: Many patients with psychotic illnesses including schizophrenia, suffer persisting disability despite treatment of delusions and hallucinations with antipsychotic medication. There is substantial evidence that disorganization of mental activity makes a major contribution to persisting disability, by disrupting thought, emotion and behaviour. Evidence suggests that this disorganization involves impaired recruitment of the relevant brain systems required to make sense of sensory input and achieve our goals. There is diminished engagement of relevant brain circuits, together with failure to suppress task-irrelevant brain activity. We propose that disorganization of mental activity reflects imprecision of the predictive coding that shapes perception and action. The brain generates internal models of the world that are successively updated in light of sensory information. What we perceive is determined by adjusting predictions to minimise discrepancy between prediction and sensory input. Motor actions are controlled by a forward model of the state of brain and body as intended action is executed. Action is continuously adjusted to minimize discrepancy between prediction and sensory input. Disorganization is associated with both imprecise timing and imprecise content of predictions. We need models that incorporate the interactions between excitatory and inhibitory neurons in local circuits with parameters representing long range communication between brain regions to help us understand the pathophysiological mechanism responsible for imprecise predictive coding in psychotic illness. | ||
− | {{Pathways to the 2023 IHP thematic program Random Processes in the Brain/ | + | {{Pathways to the 2023 IHP thematic program Random Processes in the Brain/Section|Massimiliano Tamborrino|2}} |
[[File:Structure-preserving Approximate Bayesian Computation (ABC) for stochastic neuronal models.jpg|200px|right]] | [[File:Structure-preserving Approximate Bayesian Computation (ABC) for stochastic neuronal models.jpg|200px|right]] | ||
[[File:Structure-preserving Approximate Bayesian Computation (ABC) for stochastic neuronal models.mpg|thumb|Seminar video recording.]] | [[File:Structure-preserving Approximate Bayesian Computation (ABC) for stochastic neuronal models.mpg|thumb|Seminar video recording.]] | ||
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* Speaker: Massimiliano Tamborrino, Department of Statistics at University of Warwick | * Speaker: Massimiliano Tamborrino, Department of Statistics at University of Warwick | ||
* Date: Tuesday, March 29, 2022 | * Date: Tuesday, March 29, 2022 | ||
− | * Abstract: ABC has become one of the major tools for parameter inference in complex mathematical models in the last decade. The method is based on the idea of deriving an approximate posterior density aiming to target the true (unavailable) posterior by running massive simulations from the model for different parameters to replace the intractable likelihood, choosing then those parameters whose simulations are good matches to the observed data. When applying ABC to stochastic models, the derivation of effective summary statistics and proper distances is particularly challenging, since simulations from the model under the same parameter configuration result in different output. Moreover, since exact simulation from complex stochastic models is rarely possible, reliable numerical methods need to be applied. In this talk, we show how to use the underlying structural properties of the model to construct specific ABC summaries that are less sensitive to the intrinsic stochasticity of the model, and the importance of adopting reliable property-preserving numerical (splitting) schemes for the synthetic data generation. Indeed, the commonly used Euler-Maruyama scheme may drastically fail even with very small | + | * Abstract: ABC has become one of the major tools for parameter inference in complex mathematical models in the last decade. The method is based on the idea of deriving an approximate posterior density aiming to target the true (unavailable) posterior by running massive simulations from the model for different parameters to replace the intractable likelihood, choosing then those parameters whose simulations are good matches to the observed data. When applying ABC to stochastic models, the derivation of effective summary statistics and proper distances is particularly challenging, since simulations from the model under the same parameter configuration result in different output. Moreover, since exact simulation from complex stochastic models is rarely possible, reliable numerical methods need to be applied. In this talk, we show how to use the underlying structural properties of the model to construct specific ABC summaries that are less sensitive to the intrinsic stochasticity of the model, and the importance of adopting reliable property-preserving numerical (splitting) schemes for the synthetic data generation. Indeed, the commonly used Euler-Maruyama scheme may drastically fail even with very small step sizes. The proposed approach is illustrated first on the stochastic FitzHugh-Nagumo model, and then on the broad class of partially observed Hamiltonian stochastic differential equations, in particular on the stochastic Jensen-and-Rit neural mass model, both with simulated and with real electroencephalography (EEG) data, for both one neural population and a network of neural populations (ongoing work). |
− | {{Pathways to the 2023 IHP thematic program Random Processes in the Brain/ | + | {{Pathways to the 2023 IHP thematic program Random Processes in the Brain/Section|Christophe Pouzat|2}} |
[[File:Simulation-based inference for neural network structure.jpg|200px|right]] | [[File:Simulation-based inference for neural network structure.jpg|200px|right]] | ||
;Simulation-based inference for neural network structure | ;Simulation-based inference for neural network structure |
Revision as of 14:02, 28 April 2022
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