Core Concepts
The author explores the significance of pseudo-equilibria in understanding the behavior of the NNLIF model with a large delay, showcasing convergence to equilibrium and periodic solutions in inhibitory networks.
Abstract
The content delves into mathematical models for neuronal populations, focusing on nonlinear noisy leaky integrate and fire (NNLIF) models. It introduces pseudo-equilibria as key states determined by system parameters, aiding in understanding system behavior. The study shows how a sequence of pseudo-equilibria leads to convergence to equilibrium, offering insights into periodic solutions in inhibitory networks. The analysis involves firing rates, equilibria stability, and numerical studies supporting theoretical findings.
Key points include:
- Introduction to mathematical models for neuronal populations.
- Focus on NNLIF models describing neuronal activity at the membrane potential level.
- Introduction of pseudo-equilibria as states solely determined by system parameters.
- Proposal of a new strategy showing convergence to equilibrium based on pseudo-equilibria sequences.
- Exploration of periodic solutions in strongly inhibitory networks through numerical studies.
The content provides detailed insights into the long-term behavior of NNLIF systems with significant synaptic delays, shedding light on their dynamics and stability properties.
Stats
The number of steady states depends on the connectivity parameter b [7]: for b ≤ 0 (inhibitory case) there is only one steady state.
For the nonlinear Fokker-Planck Equation (1.1), there is a global in-time solution if d > 0 [11,42].
If Equation (2.3) has two solutions: N∗1 and N∗2 (N∗1 < N∗2), then...
For excitatory networks with 0 < b, {Nk,∞} tends to an equilibrium or a 2-cycle depending on b values [7].