Optimal input reverberation and homeostatic self-organization towards the edge of synchronization
arXiv:2402.05032 · doi:10.1063/5.0202743
Abstract
Transient or partial synchronization can be used to do computations, although a fully synchronized network is frequently related to epileptic seizures. Here, we propose a homeostatic mechanism that is capable of maintaining a neuronal network at the edge of a synchronization transition, thereby avoiding the harmful consequences of a fully synchronized network. We model neurons by maps since they are dynamically richer than integrate-and-fire models and more computationally efficient than conductance-based approaches. We first describe the synchronization phase transition of a dense network of neurons with different tonic spiking frequencies coupled by gap junctions. We show that at the transition critical point, inputs optimally reverberate through the network activity through transient synchronization. Then, we introduce a local homeostatic dynamic in the synaptic coupling and show that it produces a robust self-organization toward the edge of this phase transition. We discuss the potential biological consequences of this self-organization process, such as its relation to the Brain Criticality hypothesis, its input processing capacity, and how its malfunction could lead to pathological synchronization.
17 pages, 12 figures, 1 table
References in corpus (11)
- Dynamical synapses causing self-organized criticality in neural networks
- Criticality in the brain: A synthesis of neurobiology, models and cognition
- Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization
- Feedback mechanisms for self-organization to the edge of a phase transition
- Hybrid-type synchronization transitions: where marginal coherence, scale-free avalanches, and bistability live together
- Subsampled directed-percolation models explain scaling relations experimentally observed in the brain
- Synaptic balance due to homeostatically self-organized quasicritical dynamics
- A bifurcation integrates information from many noisy ion channels
- Stability diagrams for bursting neurons modeled by three-variable maps
- Building a model of the brain: from detailed connectivity maps to network organization
- Less is different: why sparse networks with inhibition differ from complete graphs