update

README.md

@@ -1,5 +1,5 @@
 ### Intestelligence: A pharmacological neural network using intestine data
-This repogitory includes the data and source code of [our paper](https://www.biorxiv.org/content/10.1101/2023.04.15.537044v1) (This link will be updated to a DOI URL).
+This repository includes the data and source code of [our paper](https://doi.org/10.1101/2023.04.15.537044) (This link will be updated to a DOI URL after being published).
 
 #### Installation
 ``` bash

datacite.yml

@@ -23,7 +23,7 @@ authors:
     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:0000-0003-2260-819"
+    id: "ORCID:0000-0003-2260-8191"
 
 
 # A title to describe the published resource.
@@ -31,16 +31,16 @@ 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."
+  "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
+  - "Biologically-inspired Neural Network"
   - Activation Function
   - Intestine
 
@@ -53,15 +53,22 @@ license:
 
 references:
   -
-    reftype: "IsDescribedBy"
+    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:
-  - "The Exploratory Research for Advanced Technology by Japan Science and Technology Agency (JST ERATO); JPMJER1801"
+  - "Japan Science and Technology Agency (JST); Exploratory Research for Advanced Technology (ERATO) grant JPMJER1801"
 
 
 # Related publications. reftype might be: IsSupplementTo, IsDescribedBy, IsReferencedBy.