Computational Concepts for Reconstructing and Simulating Brain Tissue

Abstract

It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

“…BBN is actively and continuously being further developed and maintained by Blue Brain as a key technology and complementary approach to classical neuroinformatics in its data-driven approach and long term scientific goal and workflow for organzing brain tissue data and models [44]. BBN technology stack based on open, interoperable and domain agnostic standards supporting domain specific extension, combined with the three production deployments by adopters from large long term organisations where potential developers and contributors community can arise, shows BBN maturity, genericity, ability to support new use cases and evolution of existing ones as well as sustainability moving forward.…”

NeoCoMM: Neocortical Computational Microscale Model

“…These models are capable of depicting the dynamics of the brain across a spectrum of scales, encompassing cellular (microscopic), population (neural mass), and whole-brain (macroscopic) levels [2]. In particular, microscale models can be a valuable tool for understanding underlying physiological and pathological mechanisms [3,4,5,6] into the cellular dynamics of neural networks in various contexts, especially in the case of brain disorders such as epilepsy [5,7,8]. These models need to be neurophysiologically and biophysically relevant which can be achieved thanks to recent advances in computation and increased experimental data gathering [4].…”

Different parameter solutions of a conductance-based model that behave identically are not necessarily degenerate

“…Finally, even though some recent works use the generalization capability as an essential criteria to select acceptable CBM solutions reproducing raw data (Druckmann et al, 2011;Markram et al, 2015;Gouwens et al, 2018;Iavarone et al, 2019;Naudin et al, 2022;Schürmann et al, 2022), it remains that these works seem to represent only a small part of the existing studies. This can be surprising at least in one way, when compared with the methodology used in artificial intelligence (A.I.).…”