Protocol for Neuro-MINE: A machine learning pipeline for time-series neural response data analysis

Here, we present a protocol for the machine-learning-based Python package Neuro-MINE. We provide instructions for user interfaces that control model training and model prediction. Model training links experimental predictors to neural activity using a convolutional neural network that captures both linear and non-linear relationships. Taylor decomposition characterizes key aspects of these relationships, and we provide instructions for tweaking relevant parameters. Model prediction then computes the most likely neural response to hypothetical inputs, making it a powerful hypothesis generator. For complete details on the use and execution of this protocol, please refer to Costabile et al. 1 1. Costabile, J.D. ∙ Balakrishnan, K.A. ∙ Schwinn, S. ... Model discovery to link neural activity to behavioral tasks eLife. 2023; 12 , e83289 Crossref Scopus (6) PubMed Google Scholar

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Publication Details

Journal
STAR Protocols
Published
2026-10-27
DOI
https://doi.org/10.1016/j.xpro.2026.104913
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
0.00

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article

Protocol for Neuro-MINE: A machine learning pipeline for time-series neural response data analysis

Martin Haesemeyer, Danica Matovic
STAR Protocols
Neural dynamics and brain function
article

Protocol for Neuro-MINE: A machine learning pipeline for time-series neural response data analysis

Martin Haesemeyer, Danica Matovic
article en

Abstract

Here, we present a protocol for the machine-learning-based Python package Neuro-MINE. We provide instructions for user interfaces that control model training and model prediction. Model training links experimental predictors to neural activity using a convolutional neural network that captures both linear and non-linear relationships. Taylor decomposition characterizes key aspects of these relationships, and we provide instructions for tweaking relevant parameters. Model prediction then computes the most likely neural response to hypothetical inputs, making it a powerful hypothesis generator. For complete details on the use and execution of this protocol, please refer to Costabile et al. 1 1. Costabile, J.D. ∙ Balakrishnan, K.A. ∙ Schwinn, S. ... Model discovery to link neural activity to behavioral tasks eLife. 2023; 12 , e83289 Crossref Scopus (6) PubMed Google Scholar

STAR ProtocolsVol. 7(4)
The Ohio State University (US)
National Institutes of Health
Openalex Percentile: Top 15%
Neural dynamics and brain function
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