# A Fast $\mathcal{L}_p$ Spike Alignment Metric

## TL;DR

This paper introduces a new $ ext{L}_p$ spike alignment metric with a fast algorithm, enabling efficient comparison of neural spike trains and facilitating geometric analysis of neural response spaces.

## Contribution

It presents a novel $ ext{L}_p$ spike metric with a fast bipartite graph matching algorithm, enhancing computational efficiency for neural response analysis.

## Key findings

- The $ ext{L}_2$ version is Euclidean, suitable for embedding in Euclidean spaces.
- The algorithm significantly speeds up spike train comparisons.
- The metric supports geometrical analysis of neural response spaces.

## 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 are the {\em spike metrics}, notions of distance between two spike trains recorded from a neuron. Alignment spike metrics work by identifying "equivalent" spikes in one train and the other. We present an alignment spike metric having $\mathcal{L}_p$ underlying geometrical structure; the $\mathcal{L}_2$ 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.