Stable propagation of synchronous spiking in cortical neural networks (Diesmann et al 1999)


"... Here we show that precisely synchronized action potentials can propagate within a model of cortical network activity that recapitulates many of the features of biological systems. An attractor, yielding a stable spiking precision in the (sub)millisecond range, governs the dynamics of synchronization. Our results indicate that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is possible within a cortical-like network."

Model Type: Realistic Network

Region(s) or Organism(s): Neocortex

Model Concept(s): Attractor Neural Network

Simulation Environment: Brian (web link to method); Python (web link to model)

References:

Diesmann M, Gewaltig MO, Aertsen A. (1999). Stable propagation of synchronous spiking in cortical neural networks. Nature 402 [PubMed]