On numerical simulations of integrate-and-fire neural networks
Neural Computation
The nature of mathematical modeling
The nature of mathematical modeling
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Neural Computation
Reliability of spike timing is a general property of spiking model neurons
Neural Computation
Differences in spiking patterns among cortical neurons
Neural Computation
Information Geometry of Interspike Intervals in Spiking Neurons
Neural Computation
Methods for finding and validating neural spike patterns
Neurocomputing
Information geometry of interspike intervals in spiking neurons with refractories
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Finding the event structure of neuronal spike trains
Neural Computation
An information geometrical analysis of neural spike sequences
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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When periodic current is injected into an integrate-and-fire model neuron, the voltage as a function of time converges from different initial conditions to an attractor that produces reproducible sequences of spikes. The attractor reliability is a measure of the stability of spike trains against intrinsic noise and is quantified here as the inverse of the number of distinct spike trains obtained in response to repeated presentations of the same stimulus. High reliability characterizes neurons that can support a spike-time code, unlike neurons with discharges forming a renewal process (such as a Poisson process). These two classes of responses cannot be distinguished using measures based on the spike-time histogram, but they can be identified by the attractor dynamics of spike trains, as shown here using a new method for calculating the attractor reliability.We applied these methods to spike trains obtained from current injection into cortical neurons recorded in vitro. These spike trains did not form a renewal process and had a higher reliability compared to renewal-like processes with the same spike-time histogram.