Contributors

People who have written code, contributed to blog posts, designed artwork, refined the website, revamped social media channels or are who have been helpful and active on our Discord.

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Contributors

Justin Riddiough

With over two decades of experience, Justin brings a wealth of knowledge in collaborating with in-house developers, technologists, and strategists. His expertise extends to crafting and deploying robust, scalable web applications and strategically coordinating growth across various Open Source initiatives.

Justin created and nurtured the AI Models project (aimodels.org ), a dedicated resource for exploring and advancing ONM and related initiatives like the Foundation Models Cheatsheet (fmcheatsheet.org ), prior to AI Models acquisition in late 2024. He remains an active supporter of the community and the ongoing development of these initiatives.

Giulia D'Angelo

Giulia D’Angelo is a Researcher with a PhD in visual attention neuromorphic algorithms for online robotic applications at the Italian Institute of Technology. She’s passionate about bioinspired architectures, cats, event-based cameras, cats, spiking neural networks for vision and embodiment, cats, how much the human visual system is fascinating and her cats. She is the Co-founder and Co-creator of the Brains&Machines Podcast sponsored by EETimes.

Alexander Henkes

Alexander Henkes received the B.Sc. (Mechanical Engineering) and M.Sc. (Mechanical Engineering) degrees from the University of Paderborn, Germany, in 2015 and 2018, respectively. In 2022, he received his Ph.D. with honors from the Technical University of Braunschweig (TUBS), Germany, for his thesis ‘Artificial Neural Networks in Continuum Micromechanics’.

In 2022, he was elected as a junior member of the German Association of Applied Mathematics and Mechanics (GAMM) for his outstanding research in the field of artificial intelligence in continuum micromechanics. In 2023, he won the ETH Zürich Postdoctoral Fellowship and joined the Computational Mechanics group at ETH as a postdoc.

His current research focuses on spiking neural networks (SNN). Recently, he published a preprint on nonlinear history-dependent regression using SNN. This enables SNN to be used in the context of applied mathematics and computational engineering.

He is a contributor of snnTorch .

Jens Egholm Pedersen

Jens Egholm Pedersen is a doctoral student at the Royal Institute of Technology (KTH) working to model and construct neuromorphic control systems.

When faced with the complexity and ambiguity in the real world, contemporary algorithms fail spectacularly. There is a strong need for self-correcting, closed-loop systems to help solve our everyday physical problems.

By simulating and carefully scrutinizing and understanding neural circuits, including vision, motor control, and self-sustenance, Jens seeks to build autonomous systems that perform meaningful work, tightly following the Feynman axiom ‘What I cannot create, I do not understand’.

He is the maintainer of norse and AEStream .

Steven Abreu

Steve is doing his PhD on neuromorphic computing theory in the MINDS research group at the new CogniGron center for cognitive systems and materials in Groningen. He is funded by the European Post-Digital research network.

In his PhD, he works with different neuromorphic systems (Loihi 2 , DynapSE2 , and photonic reservoirs ) to develop programming methods for devices that explore a richer set of physical dynamics than the synchronous bi-stable switching that (most of) computer science relies on. Steve’s background is in computer science and machine learning, with a touch of physics.

Alexander Hadjiivanov

Alex is currently a Research Fellow with the Advanced Concepts Team at the European Space Agency. His research focuses on homeostasis, perception and structural plasticity in classical and spiking neural networks. When he has time, he also works on Pyrception, an easy way to interface various types of input data with neural networks.

No single branch of AI can claim the crown of true intelligence on its own. Rather, developing AI worthy of the ‘I’ would require a concerted effort to combine virtually all the branches - from perception through learning and cognition to reasoning and interaction. The most enticing aspect of neuromorphic computing is its potential to bring about this unification.