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Related Experiment Video

Updated: Jun 22, 2026

The DREAM Implant: A Lightweight, Modular, and Cost-Effective Implant System for Chronic Electrophysiology in Head-Fixed and Freely Behaving Mice

The DREAM Implant: A Lightweight, Modular, and Cost-Effective Implant System for Chronic Electrophysiology in Head-Fixed and Freely Behaving Mice

Published on: July 26, 2024

Single-unit stability using chronically implanted multielectrode arrays.

Adam S Dickey¹, Aaron Suminski, Yali Amit

¹Committe on Computational Neuroscience, University of Chicago, Chicago, Illinois 60637, USA.

Journal of Neurophysiology

|June 19, 2009

Summary

This summary is machine-generated.

We developed a method to track individual neurons over time using spike waveforms and interspike interval histograms. This improves the reliability of neurophysiological recordings and brain-machine interfaces.

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Published on: February 14, 2014

Area of Science:

Neuroscience
Computational Neuroscience
Electrophysiology

Background:

Chronic intracortical multielectrode arrays are widely used in neurophysiology.
Distinguishing between the same and different neurons across recording sessions is challenging.

Purpose of the Study:

To develop and validate a criterion for assessing single-unit stability over multiple recording sessions.
To improve the reliability of neuronal recordings for long-term studies and brain-machine interfaces.

Main Methods:

Assessed single-unit stability by measuring similarity in average spike waveforms and interspike interval histograms (ISIHs).
Recorded neuronal activity from Utah arrays in macaque monkeys over 15-17 days and 10 sessions.
Defined unit stability based on consistent satisfaction of the criterion across recordings.

Main Results:

57% of units were stable for 7 days, 43% for 10 days, and 39% for 15 days.
Stable units showed high probability (89%) of remaining stable in subsequent sessions.
Combining waveform and ISIH data reduced false positives compared to using waveforms alone.

Conclusions:

The developed criterion reliably assesses single-unit stability across multiple days.
This method enables tracking neurons during tasks like visuomotor adaptation.
Stable neuron identification has implications for long-term learning studies, data pooling, and brain-machine interface efficacy.