A package of simple reservoir computing tools

Introducing "Causal Influence Decomposition" and trying it on an Echo State Network.

EvoESN: a neuroevolutionary framework for the Echo State Network model

Codes for ''Machine-learning parameter tracking with partial state observation'', which published in Physical Review Research.

Slides for the 26th International Conference on Computational Statistics (COMPSTAT 2024)

Repo for Bees outperform vertebrates in a delayed conditional learning task

Kaggle Toxic Comment Classification Challenge models (https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)

Criticality analysis on complex networks using Power-law scaling functions, exponential relations, and phase transitions

Reservoir Networks or Echo State Networks jupyter notes

Computational Neuroscience models

Stochastic Processes Comparison of Accuracy: Data Assimilation, Echo State Machines and Nonlinear Vector Autoregressive Learning Methods. ### Comparing discrete and variational data assimilation methods to reservoir computing - machine learning for synthetic chaotic nonlinear dynamical systems

Human Activity Recognition using Echo State Networks.

A simple Python implementation of EchoStateNetwork

Music generation with machine learning: Completing Bach's unfinished Contrapunctus XIV. A variety of models including linear regression, multilayer perceptrons and echo state networks are used as autoregressive models to take on the challenging time-series task.

Federated Echo State Networks for Stress Prediction in the Automotive Use Case. Master Thesis in Artificial Intelligence @ University of Pisa

Reservoir computing for short-and long-term prediction of chaotic systems, with tasks Lorenz and Mackey-Glass systems. Bayesian optimization (hyperparameter optimization algorithm) is used to tune the hyperparameters and improve the performance.

Notebooks, learning materials

ESN implementation of reservoir computing

Code for Computational Neuroscience course 2020/2021 @ UniPi