How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?
From MaRDI portal
Publication:1704797
DOI10.1007/S10827-013-0481-5zbMath1382.92058OpenAlexW2134055773WikidataQ47757711 ScholiaQ47757711MaRDI QIDQ1704797
Agnieszka Grabska-Barwińska, Peter E. Latham
Publication date: 13 March 2018
Published in: Journal of Computational Neuroscience (Search for Journal in Brave)
Full work available at URL: http://discovery.ucl.ac.uk/1424874/1/qif_accepted.pdf
synchronizationrandom networksmean field theoryrecurrent networkquadratic integrate and fire neurontheta neuron
Related Items (1)
Cites Work
- Unnamed Item
- Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.
- Parabolic Bursting in an Excitable System Coupled with a Slow Oscillation
- A stochastic model of neural response
- Self-consistent signal-to-noise analysis and its application to analogue neural networks with asymmetric connections
- Introduction to Theoretical Neurobiology
- Onset of 'super retrieval phase' and enhancement of the storage capacity in neural networks of nonmonotonic neurons
- Dynamics of a recurrent network of spiking neurons before and following learning
- Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron
- Rate Models for Conductance-Based Cortical Neuronal Networks
- Background Synaptic Activity as a Switch Between Dynamical States in a Network
- Computing and Stability in Cortical Networks
- Cross-Correlations in High-Conductance States of a Model Cortical Network
- Response Variability in Balanced Cortical Networks
This page was built for publication: How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?
