neuro-mine 0.8.0

Identifies the contribution of behavioural and stimulus parameters to neural activity

Neuro-MINE (Model Identification of Neural Encoding) 🧠💻

Welcome to Neuro-MINE: your handy companion for processing neuronal response data! This app allows users to use MINE as a GUI or in the command line to train a flexible, convolutional neural network (CNN) to analyze experimental datasets containing neural activity and corresponding predictors (e.g., behavioral responses).

Quick Start

[1] Create an environment using Python v3.9

conda create -n mine python=3.9

[2] Activate new environment

conda activate mine

[3] Install MINE from PyPi

pip install neuro_mine

Use Cases and Requirements

Use Cases:

• Any model organism
• Any type of predictor data (stimuli and/or behavior)
• Any type of response data (imaging or spikes)
• Episodic or non-episodic data
• Generate response predictions from new predictors use an existing model

Data Requirements:

• File type: .csv with any delimiter
• Predictor data **must** have time as the first column and it must be named 'time'; for optimal outputs, predictor columns should be meaningfully labelled (e.g., 'temperature' or 'left_paw') in the header
• Reponse data **must** have time as the first column and the responses must be in adjacent columns; column titles (a header) are supported but are not mandatory
• Within episodes, data must be continuous in time, and time must be monotonically increasing

Neuro-MINE for Fitting

To launch GUI for model fitting:

Mine-gui

Possible commmand line arguments for fitting with Neuro-MINE:

Mine -p <predictor directory or filepath(s)> -r <respose directory or filepath(s)> -ut <use time> -sh <run shuffle> -ct <test score threshold> -ts <Taylor significance> -la <linear fit variance fraction> -lsq <square fit variance fraction> -n <name of model> -mh <model history (seconds)> -tl <Taylor lookahead> -j <Store Jacobians> -o <JSON filepath with existing parameters>  -e <epoch number> -mv <verbose in terminal> -mtf <fraction of data for training vs testing> -eps <data is eposidic>

Mine --help # see possible command line prompts to customize the model

Neuro-MINE for Predictions

To launch GUI for response prediction:

Mine-predict # Launches prediction GUI from existing models and new prediction data

Possible commmand line arguments for predicting with Neuro-MINE:

Mine-predict -p <predictor directory or filepath(s)> -o <JSON filepath with model parameters> -w <hdf5 filepath with weights> -a <hdf5 filepath with analysis of fit> -ct <test score threshold>

Mine-predict --help # see possible command line prompts to parameterize the prediction

Authors:
Danica Matovic
Martin Haesemeyer
Jamie Costabile
Kaarthik Balakrishnan
Sina Schwinn

Publication: Costabile JD, Balakrishnan KA, Schwinn S, Haesemeyer M. Model discovery to link neural activity to behavioral tasks. Elife. 2023 Jun 6;12:e83289. doi: 10.7554/eLife.83289. PMID: 37278516; PMCID: PMC10310322. https://elifesciences.org/articles/83289

GitHub Repository of Original Publication: https://github.com/haesemeyer/mine_pub
Lab Website: https://www.thermofish.org/

All code is licensed under the MIT license. See LICENSE for details.
© Martin Haesemeyer, Jamie D Costabile, Kaarthik A Balakrishnan, and Danica Matovic 2020-2025
Questions may be directed to [e-mail]