Fast, scalable, Bayesian spike identification for multi-electrode arrays

TL;DR

This paper introduces a fast, scalable Bayesian algorithm for identifying neural spikes in high-density multi-electrode array data, capable of distinguishing many units even with overlapping signals and variability.

Contribution

The paper presents a novel spike identification algorithm that scales efficiently with the number of units and leverages spatial locality and biophysical principles for improved accuracy.

Findings

Highly accurate spike detection on real and simulated data

Scales well with increasing number of units

Minimizes human intervention with a graphical interface

Abstract

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate.