Fast convergence of spike sequences to periodic patterns in recurrent networks.
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.
Jin
Dezhe Z
DZ
Howard Hughes Medical Institute and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
D000200:Action Potentials / Q000502:physiology; D001921:Brain / Q000502:physiology; D008954:Models, Biological; D009415:Nerve Net / Q000502:physiology*; D009431:Neural Conduction / Q000502:physiology; D009474:Neurons / Q000502:physiology