intestelligence/datacite.yml

# Metadata for DOI registration according to DataCite Metadata Schema 4.1.
# For detailed schema description see https://doi.org/10.5438/0014

## Required fields

# The main researchers involved. Include digital identifier (e.g., ORCID)
# if possible, including the prefix to indicate its type.
authors:
  -
    firstname: "Yusuke"
    lastname: "Watanabe"
    affiliation: "Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, 3010, Australia"
    id: "ORCID:0000-0001-9541-6073"
  -
    firstname: "Hiroshi"
    lastname: "Ban"
    affiliation: "Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, 565-0871, Japan"
  -
    firstname: "Nobuhiro"
    lastname: "Hagura"
    affiliation: "Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, 565-0871, Japan"
  -
    firstname: "Yuji"
    lastname: "Ikegaya"
    affiliation: "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"
    id: "ORCID:0000-0003-2260-8191"


# A title to describe the published resource.
title: "Intestelligence: A pharmacological neural network using intestine data"

# Additional information about the resource, e.g., a brief abstract.
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."



# Lit of keywords the resource should be associated with.
# Give as many keywords as possible, to make the resource findable.
keywords:
  - "Biologically-inspired Neural Network"
  - Activation Function
  - Intestine

# License information for this resource. Please provide the license name and/or a link to the license.
# Please add also a corresponding LICENSE file to the repository.
license:
 name: "CC0"
 url: "http://creativecommons.org/publicdomain/zero/1.0"


references:
  -
    id: tba    
    reftype: "IsSupplementTo"
    citation: "Yusuke Watanabe, Hiroshi Ban, Nobuhiro Hagura, and Yuji Ikegaya (2024) Intestelligence: A pharmacological neural network using intestine data. F1000research, under review. "

  -
    id: "doi:10.1101/2023.04.15.537044"
    reftype: "IsSupplementTo"
    citation: "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"
    

## Optional Fields

# Funding information for this resource.
# Separate funder name and grant number by comma.
funding:
  - "Japan Science and Technology Agency (JST); Exploratory Research for Advanced Technology (ERATO) grant JPMJER1801"


# Related publications. reftype might be: IsSupplementTo, IsDescribedBy, IsReferencedBy.
# Please provide digital identifier (e.g., DOI) if possible.
# Add a prefix to the ID, separated by a colon, to indicate the source.
# Supported sources are: DOI, arXiv, PMID
# In the citation field, please provide the full reference, including title, authors, journal etc.


# Resource type. Default is Dataset, other possible values are Software, DataPaper, Image, Text.
resourcetype: Software

# Do not edit or remove the following line
templateversion: 1.2