Abstract
Biological plasticity is ubiquitous. How does the brain navigate this complex plasticity space, where any component can seemingly change, in adapting to an ever-changing environment? We build a systematic case that stable continuous learning is achieved by structured rules that enforce multiple, but not all, components to change together in specific directions. This rule-based low-dimensional plasticity manifold of permitted plasticity combinations emerges from cell type–specific molecular signaling and triggers cascading impacts that span multiple scales. These multiscale plasticity manifolds form the basis for behavioral learning and are dynamic entities that are altered by neuromodulation, metaplasticity, and pathology. We explore the strong links between heterogeneities, degeneracy, and plasticity manifolds and emphasize the need to incorporate plasticity manifolds into learning-theoretical frameworks and experimental designs.
Highlights
Plasticity is ubiquitous in the brain, with lines of evidence suggesting that changes can occur in any component that governs brain physiology [1]
The framework of plasticity manifolds is inspired by the wellestablished neural manifold framework, which is restricted to represent the rules that govern the population dynamics of correlated firing in interconnected neurons [4e7]
How do systems expressing degeneracy switch from one valid solution to another toward maintaining functional homeostasis in the face of perturbations? We argue that plasticity manifolds provide a structured substrate for multiple components to change together, thereby seamlessly traversing the valid solution landscape (Figure 4a)
Summary
Plasticity is ubiquitous in the brain, with lines of evidence suggesting that changes can occur in any component that governs brain physiology [1]. We build a systematic case that this ultimate goal of brain plasticity is achieved through structured rules that govern the ability of multiple, but not all, components to change concomitantly. These rules are enforced by the current state of the components and the nature of stimuli and permit only certain combinations of these components to undergo plasticity. Emergence of multiscale plasticity manifolds Theoretical and computational frameworks that consider neurons as simplified computational units with synaptic plasticity as the substrate for learning (Figure 2a) have a long and cherished history [8,9].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
