Reservoir Computing

This page contains all content related to Reservoir Computing research, an innovative approach to machine learning using dynamical systems.

Research Focus

  • Performance-driven Evolution: Developing networks that evolve to optimize computational performance
  • Network Pruning: Identifying and removing unnecessary nodes while maintaining or improving performance
  • Bio-inspired Architectures: Creating reservoir computers inspired by biological neural networks
  • Structural Optimization: Understanding the relationship between network structure and computational function

Browse the content below to explore publications, projects, and collaborations in reservoir computing.

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Emergent E-I Structure in Performance-Evolved Reservoir Networks of Neuronal Population Dynamics featured image

Emergent E-I Structure in Performance-Evolved Reservoir Networks of Neuronal Population Dynamics

Performance-dependent network evolution is applied to Wilson-Cowan neuronal dynamics, revealing compact reservoirs that generalize well and recover interpretable …

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Dr. Manish Yadav
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Reservoir Computing for image classification

Study on reservoir computing methods applied to image classification.

Mehdi Ghorbani
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Research Featured in AIP Scilight: Optimizing Reservoir Computing for Noisy Nonlinear Systems featured image

Research Featured in AIP Scilight: Optimizing Reservoir Computing for Noisy Nonlinear Systems

Research on optimizing reservoir computing for studying noisy nonlinear systems has been featured in AIP Scilight, highlighting innovative approaches to signal processing and …

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Reservoir Computing Conference 2026 (RCC26), TU Berlin featured image

Reservoir Computing Conference 2026 (RCC26), TU Berlin

The Reservoir Computing Conference 2026 (RCC26) is an international conference dedicated to the field of reservoir computing and related machine learning approaches. Co-organizing …

DFG SPP 2353 Jahrestreffen 2025, Wiesbaden

Talk on "Reservoir Computing as Design Assistants Under Limited Data" at the DFG SPP 2353 Jahrestreffen 2025.

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Dr. Manish Yadav
New
Preprint
Network structure-function relationship | New Preprint featured image

Network structure-function relationship | New Preprint

This project has been started to understand emergent structure-function relationship of evolving networks. Part-1: Performance-Dependent Network Evolution (PDNE) framework. I …

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AI in Bucket of water with Physical Reservoir Computer featured image

AI in Bucket of water with Physical Reservoir Computer

A Berlin University Alliance (BUA) Course for engaging students with hands on research by building a Physical ML device that run on water and helping young scientists lead research …

PyReCo Library featured image

PyReCo Library

PyReCo is a Python based library built by researchers for researchers: we aim to develop new RC methods that allow for fast and efficient learning for sequential data. The main …

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Dr. Manish Yadav
Invited Keynote Speaker at GACM 2025 featured image

Invited Keynote Speaker at GACM 2025

A Keynote Talk was presented in the Dynamics Driven Dynamics Session on the *Foundations, Theory and Applications of Reservoir Computing* at the 11th GACM conference in TU …

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Dr. Manish Yadav
Task-specific node pruning enhances computational efficiency of reservoir computing networks featured image

Task-specific node pruning enhances computational efficiency of reservoir computing networks

Task-driven network pruning framework for reservoir computing that reveals pruned networks retain or improve accuracy while uncovering functionally critical subnetworks.

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Dr. Manish Yadav