zhaoxiaomian

Adaptive-Concentration-Threshold

GradientPrediction

Code for "Convergent Temperature Representations in Artificial and Biological Neural Networks"

Preferential-Copying-Network

Copying model with first node selected preferentially.

Network-Science-with-Python-and-NetworkX-Quick-Start-Guide

Network Science with Python and NetworkX Quick Start Guide, published by Packt

barabasi-albert

Barabási-Albert scale-free network model

graph-theory

Julia and Python complex system applications in ecology, epidemiology, sociology, economics & finance; network science models including Bianconi-Barabási, Barabási-Albert, Watts-Strogatz, Waxman Model & Erdős-Rényi; graph theory algorithms involving Gillespie, Bron Kerbosch, Ramsey, Bellman Ford, A*, Kruskal, Borůvka, Prim, Dijkstra, DSatur, Randomized Distributed, Vizing, Topological Sort, DFS, BFS

multitask

Code for Task representations in neural networks trained to perform many cognitive tasks

NavigationDQN

This project will train an agent to navigate (and collect bananas!) in a large, square world. A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. Thus, the goal of your agent is to collect as many yellow bananas as possible while avoiding blue bananas. The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. Four discrete actions are available, corresponding to: 0 - move forward. 1 - move backward. 2 - turn left. 3 - turn right. The task is episodic, and in order to solve the environment, the agent must get an average score of +13 over 100 consecutive episodes.

DeepQnetworks

MATLAB implementation of DQN for a navigation environment

Navigation-Project-with-DQN

Using the Deep Q-Learning Network to train an Agent for Navigation

Deep-Q-Learning

Tensorflow implementation of Deepminds dqn with double dueling networks

SaltSensing

Code for Salt Sensing paper

HEXplasticity

Repository of functions and data for the manuscript: Context-dependent inversion of the response in a single sensory neuron type reverses olfactory preference behavior

olfaction_evolution

Flexible-ctrl-persistent-states_Ji-et-al-eLife-2021

drone-flight

cook_et_al_2020_pharynx

Accompanying code for Cook et al 2020 The connectome of the Caenorhabditis elegans pharynx

frcnn-all-in-one

Worm-Motor-Sequence-Generation

Worm-Motor-Sequence-Generation

empathy-synchronous-dyad

Causal-temporal network modelling mirroring/homophily principles underlying empathy in [a]synchronous dyads.

dtw_som

DTW-SOM: Self-organizing map for time-series data

CNN-LSTM-Attention

使用卷积神经网络-长短期记忆网络（bi-LSTM）-注意力机制对股票收盘价进行回归预测。The convolution neural network, short-term memory network and attention mechanism are used to predict the closing price.

Neural-network-based-seizure-detection-Kaggle-dataset-

Seizure detection based on Neural Networks (1D-CNN + LSTM/GRU)

vaccine-degradation-prediction

In this competition,we tried to predict degradation rates at each base of an RNA molecule, trained on a subset of an Eterna dataset comprising over 3000 RNA molecules (which span a panoply of sequences and structures) and their degradation rates at each position. We will then score your models on a second generation of RNA sequences that have just been devised by Eterna players for COVID-19 mRNA vaccines. These final test sequences are currently being synthesized and experimentally characterized at Stanford University in parallel to your modeling efforts.

EEG-motor-imagery

ECE-GY 9123 Project: GCN-Explain-Net: An Explainable Graph Convolutional Neural Network (GCN) for EEG-based Motor Imagery Classification and Demystification

SkeletonGCN

Skeleton Graph Convolution Network is based on the Deep Graph Library and inspired by ST-GCN network designing.

gcn_demo

Mask_RCNN

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

SynapsePhenotyping

Code to accompany San Miguel et al. "Deep Phenotyping Unveils Hidden Traits and Genetic Relations in Subtle Mutants "