Pathways to the 2023 IHP thematic program Random Processes in the Brain/Seminars

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Template:Pathways to the 2023 IHP thematic program Random Processes in the Brain/Seção Disorganization of mental activity in psychosis Speaker: Peter F Liddle, Institute of Mental Health, University of Nottingham Date: Tuesday, April 26, 2022 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. Seminar video recording. Structure-preserving Approximate Bayesian Computation (ABC) for stochastic neuronal models Speaker: Massimiliano Tamborrino, Department of Statistics at University of Warwick 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 stepsizes. 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). Simulation-based inference for neural network structure Speaker: Christophe Pouzat, Université de Strasbourg and NeuroMat Date: Tuesday, March 8, 2022 Abstract: The central issue we would like to discuss with you is the network inference both from a structural and a dynamical viewpoint. What we mean by ”network” here corresponds to a cortical column, not a whole brain. We now have, for many brain regions, a lot of anatomical/ structural data. We would like to use these data when we try to infer the network underlying the observed neuronal activity (e.g., in the form of spike trains) recorded by the experimentalists. We also have many different reduced dynamical models for the neurons: some deterministic like the Hodgkin-Huxley and its reduced versions (e.g. Morris-Lecar, FitzHugh-Nagumo) and variants of integrate-and-fire models (e.g. exponential IF) and Izhikevich model; some stochastic models like stochastic integrate-and-fire models, adaptive threshold models, the Hawkes process or the Galves-Löcherbach model. These reduced models allow us to simulate reasonably large scale networks (like cortical columns). These large scale simulations require the specification of many parameters for the dynamical model, as well as for the random graph models we can propose from the known anatomical data. Several colleagues have by now used a combination of a dynamic model and of a network model with fixed parameters to generate data under the ”null hypothesis” (e.g., no functional coupling between the observed neurons), leading to an empirical distribution of their statistic of interest. We think that we should now go one step further and that’s what we would like to discuss with you. Namely we would like to consider a simulation based approach for the network inference problem as is now done in many fields under various names (e.g. ”Approximate Bayesian Computation” and ”Simulation Based Inference”). In our view a successful implementation of these methods to our problem will require the gathering of experts from many fields: quantitative neuro-anatomy, random graphs, large scale numerical simulation of the various dynamical models, statistics and probability.