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Intestelligence: A pharmacological neural network using intestine data

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Yusuke Watanabe d062d870ec update 6 kuukautta sitten
extended_data e6e3e6f9d1 update 6 kuukautta sitten
scripts 37398ef990 update 6 kuukautta sitten
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README.md d062d870ec update 6 kuukautta sitten
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README.md

Intestelligence: A pharmacological neural network using intestine data

This repository includes the data and source code of our paper (This link will be updated to a DOI URL after being published).

Installation

$ git clone git@gin.g-node.org:/intestelligence/intestelligence.git && cd intestelligence
$ python -m venv env && source env/bin/activate && pip install -r requirements.txt

Scripts

Runs an example code

$ python ./scripts/train_MNIST.py --activation_function intestine_simulated # INN
$ python ./scripts/train_MNIST.py --activation_function sigmoid # Perceptron

BibTeX Citation

@article {Watanabe2023.04.15.537044,
	author = {Yusuke Watanabe and Hiroshi Ban and Nobuhiro Hagura and Yuji Ikegaya},
	title = {Intestelligence: A pharmacological neural network using intestine data},
	elocation-id = {2023.04.15.537044},
	year = {2023},
	doi = {10.1101/2023.04.15.537044},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {A neural network is a machine learning algorithm that can learn and make predictions by adjusting the strength of the connections between nodes. The sigmoid function is commonly used as an activation function in these nodes. This study explores the potential applicability of biological materials in the development of alternative activation functions. Inspired by the fact that acetylcholine induces intestinal contractions that follow a sigmoid function, we used pharmacological data obtained from guinea pig ilea in a layered neural network for image classification tasks. We found that the intestinal data-based neural network with the same structure as a conventional three-layer perceptron achieved an impressive classification accuracy of 85.7\% {\textpm} 0.6\% based on the MNIST handwritten digit dataset (chance = 10\%). Additionally, the neural network was trained to determine whether objects in photographs collected from the internet were digestible, achieving an accuracy of 88.5\% {\textpm} 0.9\% (chance = 50\%). Our approach highlights the potential applicability of intestine data in neural computations based on pharmacological mechanisms.Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2023/04/17/2023.04.15.537044},
	eprint = {https://www.biorxiv.org/content/early/2023/04/17/2023.04.15.537044.full.pdf},
	journal = {bioRxiv}
}
datacite.yml
Title Intestelligence: A pharmacological neural network using intestine data
Authors Watanabe,Yusuke;Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, 3010, Australia;ORCID:0000-0001-9541-6073
Ban,Hiroshi;Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, 565-0871, Japan
Hagura,Nobuhiro;Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, 565-0871, Japan
Ikegaya,Yuji;Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, 565-0871, Japan; Institute for AI and Beyond, The University of Tokyo, Tokyo 113-0033, Japan;ORCID:0000-0003-2260-8191
Description "Background: A neural network is a machine learning algorithm that can learn and make predictions by adjusting the strength of the connections between nodes. The sigmoid function is commonly used as an activation function in these nodes. This study explores the potential applicability of biological materials in the development of alternative activation functions. Methods: Inspired by the fact that acetylcholine induces intestinal contractions that follow a sigmoid function, we used pharmacological data obtained from guinea pig ilea in a layered neural network for image classification tasks. Results: and Conclusions We found that the intestinal data-based neural network with the same structure as a conventional three-layer perceptron achieved an impressive classification accuracy of 85.7% ± 0.6% based on the MNIST handwritten digit dataset (chance = 10%). Additionally, the neural network was trained to determine whether objects in photographs collected from the internet were digestible, achieving an accuracy of 88.5% ± 0.9% (chance = 50%). Our approach highlights the potential applicability of intestine data in neural computations based on pharmacological mechanisms."
License CC0 (http://creativecommons.org/publicdomain/zero/1.0)
References Yusuke Watanabe, Hiroshi Ban, Nobuhiro Hagura, and Yuji Ikegaya (2024) Intestelligence: A pharmacological neural network using intestine data. F1000research, under review. [tba] (IsSupplementTo)
Watanabe Y, Ban H, Haugra N, Ikegaya Y (2023) Intestelligence: A pharmacological neural network using intestine data. bioRxiv 2023.04.15.537044. https://doi.org/10.1101/2023.04.15.537044 [doi:10.1101/2023.04.15.537044] (IsSupplementTo)
Funding Japan Science and Technology Agency (JST); Exploratory Research for Advanced Technology (ERATO) grant JPMJER1801
Keywords Biologically-inspired Neural Network
Activation Function
Intestine
Resource Type Software