Nested Dynamical Modeling of Neural Systems: A strategy and some consequences
Luis H. Favela (2023). Nested Dynamical Modeling of Neural Systems: A strategy and some consequences. Journal of Multiscale Neuroscience 2(1), 240-250 https://doi.org/10.56280/1567939485
Neuroscience has become a big data enterprise. This is due in large part to the rapidly growing quantity and quality of data and increased appreciation of non-neuronal physiology and environments in explaining behavior, cognition, and consciousness. One way neuroscience is dealing with this embarrassment of riches is by appealing to investigative frameworks that put the multiscale nature of neural systems at the forefront. The current work offers one such approach: Nested dynamical modeling, a strategy for creating models of phenomena comprised of multiple spatial and/or temporal scales for purposes of exploration, explanation, and understanding. Building from dynamical systems theory and synergetics, nested dynamical modeling applies a methodological approach aimed at nesting models at one scale of inquiry within models at other scales without compromising biological realism. This strategy is demonstrated via a proof of concept. Some consequences this approach has for the epistemological and theoretical commitments of neuroscience are discussed.
Keywords: Big data, dynamical systems theory, modeling, multiscale, synergetics
Conflict of Interest
The author declares no conflict of interest
This article belongs to the Special Issue
Prof Michael J Spivey, Author of "The Continuity of Mind".
Department of Cognitive and Information Sciences,
University of California, Merced, USA
Copyright: © 2023 The Author(s). Published by Neural Press.
This is an open access article distributed under the terms and conditions of the CC BY 4.0 license.