A Novel Spike Distance

2001/04/01 by M. C. W. van Rossum, Mark C. W. van Rossum · 12 citations
Neuroscience · Physics and Astronomy · Computer Science · #Neural dynamics and brain function #stochastic dynamics and bifurcation #Nonlinear Dynamics and Pattern Formation

paper · doi:10.1162/089976601300014321

Abstract

The discrimination between two spike trains is a fundamental problem for both experimentalists and the nervous system itself. We introduce a measure for the distance between two spike trains. The distance has a time constant as a parameter. Depending on this parameter, the distance interpolates between a coincidence detector and a rate difference counter. The dependence of the distance on noise is studied with an integrate-and-fire model. For an intermediate range of the time constants, the distance depends linearly on the noise. This property can be used to determine the intrinsic noise of a neuron.

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• SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks
• Quantifying concurrency in event-based temporal network and hypergraph data
• Multi-Sample Online Learning for Spiking Neural Networks based on Generalized Expectation Maximization
• A comparison of Euclidean metrics and their application in statistical inferences in the spike train space
• Privacy-Preserving Spiking Neural Networks: A Deep Dive into Encryption Parameter Optimisation
• Quantifying spike train synchrony and directionality: Measures and Applications
• Random Feature Spiking Neural Networks
• Neural Response Reliability as a Marker of the Transition of Neural Codes along Auditory Pathways
• Response and noise correlations to complex natural sounds in the auditory midbrain
• Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks
• BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks
• Categorical Distributions are Effective Neural Network Outputs for Event Prediction

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