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Peer-reviewedCC BY-NC-ND 4.0STAR Protocols

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

  • Danica Matovic1,2
  • Martin Haesemeyer2
  1. 1Neuroscience Graduate Program, The Ohio State University, Columbus, OH 43210, USA
  2. 2Department of Neuroscience, The Ohio State University, Columbus, OH 43210, USA
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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

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Cite

BibTeXRIS
BibTeX
@article{matovic2026protocol,
  title = {{Protocol for Neuro-MINE: A machine learning pipeline for time-series neural response data analysis}},
  author = {Danica Matovic and Martin Haesemeyer},
  journal = {STAR Protocols},
  year = {2026},
  month = oct,
  doi = {10.1016/j.xpro.2026.104913},
  url = {https://doi.org/10.1016/j.xpro.2026.104913},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Protocol for Neuro-MINE: A machine learning pipeline for time-series neural response data analysis
AU  - Danica Matovic
AU  - Martin Haesemeyer
PY  - 2026
DA  - 2026/10/27
JO  - STAR Protocols
DO  - 10.1016/j.xpro.2026.104913
UR  - https://doi.org/10.1016/j.xpro.2026.104913
AB  - 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
SN  - 2666-1667
ER  -