Code accompanying "Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity," accepted to IEEE SSCI ICES 2021

Overview

Evolving-spiking-neuron-cellular-automata-and-networks-to-emulate-in-vitro-neuronal-activity

Code accompanying "Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity" [1], accepted to the International Conference on Evolvable Systems (IEEE SSCI 2021).

ICES page: https://attend.ieee.org/ssci-2021/international-conference-on-evolvable-systems-ices/

STRUCTURE:
There are two folders in the main directory.

Resources contains the neural data used in this study as .txt files. The data were collected by Wagenaar et al. [2], and the full open dataset can be found here: http://neurodatasharing.bme.gatech.edu/development-data/html/index.html

Each file contains the time (column 1) and recording channel (column 2) of each spike detected in the data.

The project code is found in the src-folder. The code to run the models and evolutionary algorithm is found here. Additionally there is a separate folder for plotting results.

RUNNING SINGLE MODEL:
A single model with desired parameters can be run with the Model.py file. Parameters are set at the top of this file.

RUNNING EVOLUTIONARY ALGORITHM:
To run the evolutionary algorithm, the Main.py file is run and parameters are set in the default_parameters dict.

RUNNING SAVED MODEL:
To run a saved model, the RunSavedModel.py files is run from terminal with the first argument being the GraphML file and the second argument being simulation duration in seconds.

RUNNING BATCH FILES:
Multiple simulations can be run by passing batch files as arguments when running Main.py. Batch files must be .csv files. An example can be seen in batch_example.csv. Each row is a separate run.

EXTERNAL LIBRARIES:

  • Pandas
  • Numpy
  • NetworkX
  • Scipy
  • Matplotlib
  • Pylab
  • Seaborn
  • Pandas

[1] J Jensen Farner, H Weydahl, CR Jahren, O Huse Ramstad, S Nichele, and K Heiney. "Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity," International Conference on Evolvable Systems (IEEE Symposium Series on Computational Intelligence 2021), 2021.

[2] DA Wagenaar, J Pine, and SM Potter, "An extremely rich repertoire of bursting patterns during the development of cortical cultures," BMC Neuroscience, 7(1):11, 2006.

Owner
SOCRATES: Self-Organizing Computational substRATES
SOCRATES is a long-term time horizon project seeking radical breakthroughs toward efficient and powerful data analysis available everywhere.
SOCRATES: Self-Organizing Computational substRATES
Official implementation of "Learning Not to Reconstruct" (BMVC 2021)

Official PyTorch implementation of "Learning Not to Reconstruct Anomalies" This is the implementation of the paper "Learning Not to Reconstruct Anomal

Marcella Astrid 13 Dec 04, 2022
Instance-Dependent Partial Label Learning

Instance-Dependent Partial Label Learning Installation pip install -r requirements.txt Run the Demo benchmark-random mnist python -u main.py --gpu 0 -

17 Dec 29, 2022
利用Tensorflow实现基于CNN的中文短文本分类

Text Classification with CNN 使用卷积神经网络进行中文文本分类 CNN做句子分类的论文可以参看: Convolutional Neural Networks for Sentence Classification 还可以去读dennybritz大牛的博客:Implemen

Jeremiah 4 Nov 08, 2022
A PyTorch implementation of Implicit Q-Learning

IQL-PyTorch This repository houses a minimal PyTorch implementation of Implicit Q-Learning (IQL), an offline reinforcement learning algorithm, along w

Garrett Thomas 30 Dec 12, 2022
PyTorch implementation of Weak-shot Fine-grained Classification via Similarity Transfer

SimTrans-Weak-Shot-Classification This repository contains the official PyTorch implementation of the following paper: Weak-shot Fine-grained Classifi

BCMI 60 Dec 02, 2022
Multi-task head pose estimation in-the-wild

Multi-task head pose estimation in-the-wild We provide C++ code in order to replicate the head-pose experiments in our paper https://ieeexplore.ieee.o

Roberto Valle 26 Oct 06, 2022
Supplementary code for TISMIR paper "Sliding-Window Pitch-Class Histograms as a Means of Modeling Musical Form"

Sliding-Window Pitch-Class Histograms as a Means of Modeling Musical Form This is supplementary code for the TISMIR paper Sliding-Window Pitch-Class H

1 Nov 27, 2021
PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

UMS for Multi-turn Response Selection Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utte

Taesun Whang 47 Nov 22, 2022
An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and Machine Learning.

ALgorithmic_Trading_with_ML An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and

1 Mar 14, 2022
Reading Group @mila-iqia on Computational Optimal Transport for Machine Learning Applications

Computational Optimal Transport for Machine Learning Reading Group Over the last few years, optimal transport (OT) has quickly become a central topic

Ali Harakeh 11 Aug 26, 2022
An Open-Source Toolkit for Prompt-Learning.

An Open-Source Framework for Prompt-learning. Overview • Installation • How To Use • Docs • Paper • Citation • What's New? Nov 2021: Now we have relea

THUNLP 2.3k Jan 07, 2023
The Pytorch implementation for "Video-Text Pre-training with Learned Regions"

Region_Learner The Pytorch implementation for "Video-Text Pre-training with Learned Regions" (arxiv) We are still cleaning up the code further and pre

Rui Yan 0 Mar 20, 2022
This repository contains a set of codes to run (i.e., train, perform inference with, evaluate) a diarization method called EEND-vector-clustering.

EEND-vector clustering The EEND-vector clustering (End-to-End-Neural-Diarization-vector clustering) is a speaker diarization framework that integrates

45 Dec 26, 2022
机器学习、深度学习、自然语言处理等人工智能基础知识总结。

说明 机器学习、深度学习、自然语言处理基础知识总结。 目前主要参考李航老师的《统计学习方法》一书,也有一些内容例如XGBoost、聚类、深度学习相关内容、NLP相关内容等是书中未提及的。

Peter 445 Dec 12, 2022
π-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

π-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis Project Page | Paper | Data Eric Ryan Chan*, Marco Monteiro*, Pe

375 Dec 31, 2022
🥈78th place in Riiid Solution🥈

Riiid Answer Correctness Prediction Introduction This repository is the code that placed 78th in Riiid Answer Correctness Prediction competition. Requ

ds wook 14 Apr 26, 2022
Boostcamp AI Tech 3rd / Basic Paper reading w.r.t Embedding

Boostcamp AI Tech 3rd : Basic Paper Reading w.r.t Embedding TL;DR 1992년부터 2018년도까지 이루어진 word/sentence embedding의 중요한 줄기를 이루는 기초 논문 스터디를 진행하고자 합니다. 논

Soyeon Kim 14 Nov 14, 2022
Tensorflow 2 Object Detection API kurulumu, GPU desteği, custom model hazırlama

Tensorflow 2 Object Detection API Bu tutorial, TensorFlow 2.x'in kararlı sürümü olan TensorFlow 2.3'ye yöneliktir. Bu, görüntülerde / videoda nesne a

46 Nov 20, 2022
SCALoss: Side and Corner Aligned Loss for Bounding Box Regression (AAAI2022).

SCALoss PyTorch implementation of the paper "SCALoss: Side and Corner Aligned Loss for Bounding Box Regression" (AAAI 2022). Introduction IoU-based lo

TuZheng 20 Sep 07, 2022
Implementation of our recent paper, WOOD: Wasserstein-based Out-of-Distribution Detection.

WOOD Implementation of our recent paper, WOOD: Wasserstein-based Out-of-Distribution Detection. Abstract The training and test data for deep-neural-ne

8 Dec 24, 2022