Stable propagation of activity pulses in populations of spiking neurons

Authors:
Werner M. Kistler;Wulfram Gerstner
Affiliations:
Swiss Federal Institute of Technology Lausanne, 1015 Lausanne EPFL, Switzerland, and Neuroscience Institute, Department of Anatomy, FGG, Erasmus University Rotterdam, 3000DR Rotterdam, The Netherl ...;Swiss Federal Institute of Technology Lausanne, 1015 Lausanne EPFL, Switzerland
Venue:
Neural Computation
Year:
2002

Citing 4
Cited 4

Reduction of the Hodgkin-Huxley equations to a single-variable threshold model

Neural Computation
Modeling collective excitations in cortical tissue

Physica D
Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates

Neural Computation
Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking

Neural Computation

A pulsed neural network model of spectro-temporal receptivefields and population coding in auditory cortex

Natural Computing: an international journal
Correlated Firing in a Feedforward Network with Mexican-Hat-Type Connectivity

Neural Computation
Variable Timescales of Repeated Spike Patterns in Synfire Chain with Mexican-Hat Connectivity

Neural Computation
Stochasticity in localized synfire chain

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We investigate the propagation of pulses of spike activity in a neuronal network with feedforward couplings. The neurons are of the spike-response type with a firing probability that depends linearly on the membrane potential. After firing, neurons enter a phase of refractoriness. Spike packets are described in terms of the moments of the firing-time distribution so as to allow for an analytical treatment of the evolution of the spike packet as it propagates from one layer to the next. Analytical results and simulations show that depending on the synaptic coupling strength, a stable propagation of the packet with constant waveform is possible. Crucial for this observation is neither the existence of a firing threshold nor a sigmoidal gain function--both are absent in our model-but the refractory behavior of the neurons.