Fast Convergence of Spike Sequences to Periodic Patterns in Recurrent Networks
- 25 October 2002
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review Letters
- Vol. 89 (20) , 208102
- https://doi.org/10.1103/physrevlett.89.208102
Abstract
The dynamical attractors are thought to underlie many biological functions of recurrent neural networks. Here we show that stable periodic spike sequences with precise timings are the attractors of the spiking dynamics of recurrent neural networks with global inhibition. Almost all spike sequences converge within a finite number of transient spikes to these attractors. The convergence is fast, especially when the global inhibition is strong. These results support the possibility that precise spatiotemporal sequences of spikes are useful for information encoding and processing in biological neural networks.Keywords
This publication has 21 references indexed in Scilit:
- An ultra-sparse code underliesthe generation of neural sequences in a songbirdNature, 2002
- Short-term memory in olfactory network dynamicsNature, 1999
- How the brain keeps the eyes stillProceedings of the National Academy of Sciences, 1996
- Pulse-Coupled Relaxation Oscillators: From Biological Synchronization to Self-Organized CriticalityPhysical Review Letters, 1995
- Synchrony in Excitatory Neural NetworksNeural Computation, 1995
- Time structure of the activity in neural network modelsPhysical Review E, 1995
- Pattern of synchrony in inhomogeneous networks of oscillators with pulse interactionsPhysical Review Letters, 1993
- Synchronization of Pulse-Coupled Biological OscillatorsSIAM Journal on Applied Mathematics, 1990
- Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of the National Academy of Sciences, 1984
- Absolute stability of global pattern formation and parallel memory storage by competitive neural networksIEEE Transactions on Systems, Man, and Cybernetics, 1983