Machine Learning

This page contains all content related to Machine Learning research, including applications in reservoir computing, network analysis, and bio-inspired systems.

Key Research Areas

  • Reservoir Computing: Developing efficient machine learning approaches using dynamical systems
  • Network-based ML: Machine learning on complex networks and graph structures
  • Bio-inspired Learning: Learning algorithms inspired by biological information processing

Browse the content below to explore publications, projects, and collaborations in machine learning.

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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. RCC26 …

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
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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
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The impact of AI on engineering design procedures for dynamical systems

we examine the potential for integrating AI into theengineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as …

Kristin M. De Pyrebrune
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Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity featured image

Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity

Can evolution beat random chances? Our answer: Yes, we have a proof! 😃

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Dr. Manish Yadav
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