Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

Abstract

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity pattern is hippocampal replay, which is critical for memory consolidation. The switchings between replay events and a low-activity state in neural recordings suggests metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to spike noise and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesosocpic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie ("chemical Langevin equation"), which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down states dynamics) by means of phase-plane analysis. This stochastic neural mass model is the basic component of our mesoscopic model for replay. We show that our model faithfully captures the stochastic nature of individual replayed trajectories. Moreover, compared to the deterministic Romani-Tsodyks model of place cell dynamics, it exhibits a higher level of variability in terms of content, direction and timing of replay events, compatible with biological evidence and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.

arXiv comment43 pages, 8 figures

References in corpus (22)

• Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
• Dynamical synapses causing self-organized criticality in neural networks
• Macroscopic description for networks of spiking neurons
• Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization
• Dynamics of multi-stable states during ongoing and evoked cortical activity
• Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size
• Noise-induced behaviors in neural mean field dynamics
• Metastable dynamics of neural circuits and networks
• Network events on multiple space and time scales in cultured neural networks and in a stochastic rate model
• Irregular dynamics in up and down cortical states
• A reduction methodology for fluctuation driven population dynamics
• Dynamical model of sequential spatial memory: winnerless competition of patterns
• Exact neural mass model for synaptic-based working memory
• An exact firing rate model reveals the differential effects of chemical versus electrical synapses in spiking networks
• Fluctuations and information filtering in coupled populations of spiking neurons with adaptation
• Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks
• Exact finite-dimensional description for networks of globally coupled spiking neurons
• Predicting how and when hidden neurons skew measured synaptic interactions
• Replay in Deep Learning: Current Approaches and Missing Biological Elements
• A system of interacting neurons with short term synaptic facilitation
• Modeling bursting in neuronal networks using facilitation-depression and afterhyperpolarization
• Escape from an attractor generated by recurrent exit

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