- Poster presentation
- Open Access
Revisiting time discretisation of spiking network models
© Cessac and Viéville; licensee BioMed Central Ltd. 2007
- Published: 6 July 2007
- Periodic Orbit
- Neural Network Model
- Neuron Model
- Generic Regime
- Condition Sensitivity
A link is built between a biologically plausible generalized integrate and fire (GIF) neuron model with conductance-based dynamics  and a discrete time neural network model with spiking neurons , for which rigorous results on the spontaneous dynamics has been obtained. More precisely the following has been shown.
i) Occurrence of periodic orbits is the generic regime of activity, with a bounded period in the presence of spike-time dependence plasticity, and arbitrary large periods at the edge of chaos (such regime is indistinguishable from chaos in numerical experiments, explaining what is obtained in ),
ii) the dynamics of membrane potential has a one to one correspondence with sequences of spikes patterns ("raster plots").
This allows a better insight into the possible neural coding in such a network and provides a deep understanding, at the network level, of the system behavior. Moreover, though the dynamics is generically periodic, it has a weak form of initial conditions sensitivity due to the presence of the sharp spiking threshold . A step further, constructive conditions are derived, allowing to properly implement visual functions on such networks .
This work was partially supported by the EC IP project FP6-015879, FACETS.
- Rudolph M, Destexhe A: Analytical integrate and fire neuron models with conductance-based dynamics for event driven simulation strategies. Neural Computation. 2006, 18: 2146-2210. 10.1162/neco.2006.18.9.2146.PubMedView ArticleGoogle Scholar
- Soula H, Beslon G, Mazet O: Spontaneous dynamics of asymmetric random recurrent spiking neural networks. Neural Computation. 2006, 18 (1):Google Scholar
- Cessac B: A discrete time neural network model with spiking neurons. i. rigorous results on the spontaneous dynamics. [A PRECISER].Google Scholar
- Viéville T, Kornprobst P: Modeling cortical maps with feed-backs. Int Joint Conf on Neural Networks. 2006Google Scholar
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