A Fast ℒp Spike Alignment Metric

Author:

Dubbs Alexander J.,Seiler Brad A.1,Magnasco Marcelo O.2

Affiliation:

1. Center for Studies in Physics and Biology, Rockefeller University, New York, New York 10065, U.S.A., and Harvard University, Faculty of Arts and Sciences, Cambridge, MA 02138, U.S.A.

2. Center for Studies in Physics and Biology, Rockefeller University, New York, New York 10065, U.S.A.

Abstract

The metrization of the space of neural responses is an ongoing research program seeking to find natural ways to describe, in geometrical terms, the sets of possible activities in the brain. One component of this program is spike metrics—notions of distance between two spike trains recorded from a neuron. Alignment spike metrics work by identifying “equivalent” spikes in both trains. We present an alignment spike metric having [Formula: see text] underlying geometrical structure; the [Formula: see text] version is Euclidean and is suitable for further embedding in Euclidean spaces by multidimensional scaling methods or related procedures. We show how to implement a fast algorithm for the computation of this metric based on bipartite graph matching theory.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Spike Train Distance;Encyclopedia of Computational Neuroscience;2022

2. Computational challenges and opportunities for a bi-directional artificial retina;Journal of Neural Engineering;2020-10-22

3. Spike Train Distance;Encyclopedia of Computational Neuroscience;2019-12-11

4. Multineuron spike train analysis with R-convolution linear combination kernel;Neural Networks;2018-06

5. Learning a neural response metric for retinal prosthesis;2017-11-29

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