Project AutoMIND – Automated Model Inference from Neural Dynamics for a Mechanistic Understanding of Cognition

Basic data

Acronym:
AutoMIND
Title:
Automated Model Inference from Neural Dynamics for a Mechanistic Understanding of Cognition
Duration:
01/05/2021 to 30/04/2023
Abstract / short description:
Human cognition depends on complex coordinated dynamics of neural populations, which is shaped by a rich heterogeneity in cellular properties and network connectivity patterns of neural circuits. While cognitive neuroscience leverages macroscopic brain signals to relate neural activity to behavioral states, we currently cannot dissect them for their physiological contributions, hindering mechanistic interpretations of experimental data. One way to systematically study how physiological parameters shape neural dynamics is through mechanistic modeling of spiking neural networks. However, current modeling approaches are not quantitatively constrained by observed electrophysiological data, and often require painstaking and ad-hoc parameter-tuning by hand. Efficient discovery of mechanistic models that are consistent with experimental data would dramatically accelerate our understanding of how cellular and network properties impact cognition, and why it breaks down in pathological states, representing a radical departure from how neural data is analyzed in cognitive neuroscience. To this end, I propose to develop a machine learning-assisted model inference tool—Automated Model Inference from Neural Dynamics (AutoMIND)—that can identify parameters of candidate spiking neural network models that could capture arbitrary target neural dynamics from human brain recordings. AutoMIND extends on recent advances in simulation-based inference techniques, incorporating simultaneous parameter manifold-learning and gradient-based simulations. AutoMIND has broad utility for tackling neuroscientific questions by enabling expedited in-silico experiments. Here, I apply it to multiscale neural data to study how cellular and network properties shape: 1) the emergence of synchronous network oscillations during early neurodevelopment, and 2) the difference in neural dynamics and computation between sensory and association cortices—two questions of fundamental importance to neuroscience.
Keywords:
machine learning
Maschinelles Lernen
simulation-based inference
likelihood-free inference
mechanistic modeling
spiking neural networks

Involved staff

Managers

Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Bernstein Center for Computational Neuroscience Tübingen (BCCN)
Interfaculty Institutes
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science

Local organizational units

University of Tübingen

Funders

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