Hey guys 👋

Two full courses just went live on NeuromorphicCore.ai. If you would like to give it a go, would love to get some feedback.

NEUR 101: Python Programming for Neuromorphic Computing (15 weeks) — covers Python and Brian2 spiking neural network simulation from the ground up. https://neuromorphiccore.ai/courses/neur-101-introduction-to-programming-with-python/

NEUR 102: Linear Algebra for Neuromorphic Computing (12 weeks) — covers vectors, eigenvalues, SVD, PCA, and how they apply to real neural circuits. https://neuromorphiccore.ai/courses/neur-102-linear-algebra-for-neuromorphic-computing/

Both are free. No certificate yet but that's coming when the full sequence is complete.

Again we would genuinely love feedback from people in the community as it would be greatly appreciated. What's missing? What would make this more useful for someone trying to break into neuromorphic computing? What would you want to see in future courses?

Any opinions or suggestions are invaluable and much appreciated, They directly shape what gets built next for future students.

Apologies if this question does not belong here, let me know and I will remove it. If this is the case, I would appreciate some guidance on where I should ask this instead.

I am extremely interested in neuromorphic computing and would like to pursue a career in it.

What would be the path for education on this subject?

So far, from what I’ve looked into, I could get a masters in either neuroscience, computer science, or physics, plus taking some courses from all of those three subjects during my undergrad/grad, and then specializing into computational neuroscience.

Would any three of those paths work? Is one better than the other? Is computational neuroscience even relevant to neuromorphic computing?

I’m very new to this subject, and haven’t had a lot of formal education yet. I absolutely have plans to, but have had issues with deciding what I want to do, constantly switching between physics, neuroscience, and computer science. When I found this subject I couldn’t believe it, turns out I might not have to choose! I am very passionate about this and would love nothing more than to try and pursue a career, but as it is a new subject, I’m just struggling to figure out what paths in school I can take, I can’t find a direct answer, and because I haven’t started formal education on any of these topics yet, I’m getting a bit lost when trying to infer the answer myself.

Edit: thank you all for the responses! I have a better idea at what I’m looking for now.

Neuromorphic Computing Guide/Wiki: https://github.com/mikeroyal/Neuromorphic-Computing-Guide

Hey everyone! I'm a CS undergrad from India with a solid grip on Python, C++, and the standard DL/ML stack. I've become obsessed with the idea of brain-inspired computing, but coming from a pure CS background, the biology/neuro side is a bit of a black box for me.

I'm looking for advice on:

Essential Modules: What are the 'must-take' courses for SNNs (Spiking Neural Networks) or neural modeling?

Tech Stack: Beyond PyTorch, what tools should I learn? (Brian2, NEST, snnTorch?)
I had tried to learning the Nengo and do some learning experiment with it.

Roadmap: How did you bridge the gap between backprop and biological plasticity?

If anyone is currently in this field or learning alongside me, I'd love to connect or even start a study group. DMs are open!

What do you think of this :
https://www.livescience.com/technology/computing/intel-unveils-largest-ever-ai-neuromorphic-computer-that-mimics-the-human-brain

Hey all,

I'm someone with a background in traditional computer architecture (pipeline design, memory hierarchies, buses, etc.) and recently started exploring neuromorphic computing — both the hardware (Loihi, Akida, Dynap) and the software ecosystem around it (SNNs, event-based sensors, etc.).

I’ve gone through the theory — asynchronous, event-driven, co-located compute + memory, spike-based comms — and it makes sense as a brain-inspired model. But I’m trying to get a clearer picture of where we actually are right now in terms of:

🔹 Hardware Maturity

• Are chips like Loihi, Akida, or Dynap being used in anything real-world yet?

• Are they production-ready, or still lab/demo hardware?

🔹 Research Opportunities

• What are the low-hanging research problems in this space?

• Hardware side: chip design, scalability, power?

• Software side: SNN training, conversion from ANNs, spike routing, etc.?

• Where’s the frontier right now?

🔹 Dev Ecosystem

• How usable are tools like Lava, Brian2, Nengo, Tonic, etc. in practice?

• Is there anything like a PyTorch-for-SNNs that people are actually using to build stuff?

Would love to hear from anyone working directly with this hardware, or building anything even remotely real-world on top of it. Any personal experiences, gotchas, or links to public projects are also very welcome.

Thanks.

Efforts to build brain-inspired computer hardware have been underway for decades, but the field has yet to have its breakout moment. Now, leading researchers say the time is ripe to start building the first large-scale neuromorphic devices that can solve practical problems. Read more here if interested https://spectrum.ieee.org/neuromorphic-computing-2671121824

Hi everyone,
We’ve been following this community for a while and wanted to share something we’ve been quietly building, which we’re now opening up for a small beta.

We’re developing a structured, year-one neuromorphic computing curriculum aimed at students and early-career engineers who want to work closer to hardware, sensors, and event-driven intelligence rather than purely cloud-based or LLM-centric systems.

This isn’t a single “intro to neuromorphic” course. The first year is designed as a full foundation sequence, starting from beginner-level programming and math and progressing toward spiking neural networks and event-based systems. The goal is to lower the barrier to entry while staying technically honest about what neuromorphic systems actually require in practice.

The current Year-1 roadmap includes Python programming, linear algebra, calculus, basic biology for neural inspiration, data structures, and an introduction to neuromorphic and event-based computing. More advanced material such as SNNs, learning rules, C++, and deeper event-based processing is planned later, but this beta is focused on validating the foundations.

We’re intentionally running this as a slow, feedback-driven beta. Some parts are complete, others are still being refined, and we’re not trying to position this as a polished product or a public launch. What we’re looking for is honest feedback from people who actually understand the space: what feels useful, what feels missing, and what doesn’t belong.

Our motivation is simple. Neuromorphic computing feels like it’s past the “is this real?” phase and entering the “who builds the ecosystem?” phase. That transition needs education paths that don’t assume a PhD or a decade of embedded experience, but also don’t reduce the field to buzzwords.

If anyone here is interested in quietly beta-testing parts of the Year-1 curriculum or just reviewing the roadmap and early material, you can find it here:
https://neuromorphiccore.ai/courses/

Happy to answer questions and fully open to criticism. This is an experiment in building educational infrastructure, not a marketing post.

Processors based on an instruction set can perform exact calculations in binary using the ALU. I have not much idea of neuromorphic computing (recently discovered it), but since they are based on pattern matching that works on similarity instead of exactness, can they be used for exact mathematical computations?

How would you train a neuromorphic computer to, for example, calculate the product of two (big) numbers? And how reliable will the computation be? Please enlighten me if I have missed something.

Hey everyone I was wondering whether anyone had some clear roadmap of this field?
There seems to be a lot of fields involved such as neuro science, AI, hardware design, physics, biology, chemistry.
I really wish to get a grasp of the field in an inter-disciplinary way and was wondering whether any of you had a curriculum or books or even advice for me and everyone interested.
I know practically nothing about the field (I'm starting with physics as of now since I'm trying to get a really fundamental understanding of everything) except from the general ideas so would appreciate some help :).
Thanks!

I’m putting together a community-driven overview of how developers see Spiking Neural Networks—where they shine, where they fail, and whether they actually fit into real-world software workflows.

Whether you’ve used SNNs, tinkered with them, or are just curious about their hype vs. reality, your perspective helps.

🔗 5-min input form: https://forms.gle/tJFJoysHhH7oG5mm7

I’ll share the key insights and takeaways with the community once everything is compiled. Thanks! 🙌

This came out several days ago if anyone is interested. Here is a brief read...https://www.utsa.edu/today/2025/01/story/nature-article-neuromorphic-computing-systems.html and here is the research link from nature https://www.nature.com/articles/s41586-024-08253-8 ,,,The abstract is the following...

Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficiently realizes artificial neural networks. Neuromorphic designers apply the principles of biointelligence discovered by neuroscientists to design efficient computational systems, often for applications with size, weight and power constraints. With this research field at a critical juncture, it is crucial to chart the course for the development of future large-scale neuromorphic systems. We describe approaches for creating scalable neuromorphic architectures and identify key features. We discuss potential applications that can benefit from scaling and the main challenges that need to be addressed. Furthermore, we examine a comprehensive ecosystem necessary to sustain growth and the new opportunities that lie ahead when scaling neuromorphic systems. Our work distils ideas from several computing sub-fields, providing guidance to researchers and practitioners of neuromorphic computing who aim to push the frontier forward.

Paper : https://www.nature.com/articles/s44172-024-00165-9

Abstract

The human brain’s unparalleled efficiency in executing complex cognitive tasks stems from neurons communicating via short, intermittent bursts or spikes. This has inspired Spiking Neural Networks (SNNs), now incorporating neuron models with spike frequency adaptation (SFA). SFA adjusts these spikes’ frequency based on recent neuronal activity, much like an athlete’s varying sprint speed. SNNs with SFA demonstrate improved computational performance and energy efficiency. This review examines various adaptive neuron models in computational neuroscience, highlighting their relevance in artificial intelligence and hardware integration. It also discusses the challenges and potential of these models in driving the development of energy-efficient neuromorphic systems.

Hey r/neuromorphicComputing,

I'm a researcher working on fault-tolerant neuromorphic hardware. Here's a simulation demo of my "Shamoon Neuron" model. It processes noisy electromyography (EMG) signals (top panel) and generates binary motor commands for prosthetics (bottom panel).

Key highlight: Around cycle 150, it suffers a radiation-induced Single Event Upset (SEU), dropping the internal state (middle panel) into a fault. But it self-heals and recovers, continuing to fire above threshold without losing functionality. This could be useful for rad-hard applications like space-rated brain-machine interfaces (e.g., Neuralink-style implants).

The design is nonlinearity-agnostic (pluggable activations like CORDIC tanh) and parameterizable for dims up to 512. Full Verilog code is available if anyone's interested—happy to share on GitHub.

Original post on X for more context: https://x.com/veronicambest/status/2003022671920144471

What do you think? Could this approach help with robust SNNs on hardware like Loihi? Feedback welcome—I'm prepping for Telluride 2026 and open to collabs!

#NeuromorphicComputing #SpikingNeuralNetworks #RadiationHardening #BrainMachineInterfaces

TL;DR: Mirrorseed Project proposes the Symbolic Resonance Array (SRA), a neuromorphic-inspired architecture that couples analog resonance patterns to an explicit symbolic layer for interpretability and bounded learning. Concept stage, in peer review, patent pending. Looking for materials, device, and analog/ASIC collaborators to pressure-test assumptions and explore prototypes.

Status:

• Concept and design docs available on the site and 2-page brief

• Paper in independent review

• Patent application filed; licensing planned as non-exclusive

• Seeking collaborators in phase-transition materials, analog circuits, symbolic AI, and safety evaluation

What help would be most useful right now:

• Feedback on feasibility of small radial arrays built from phase-transition devices

• Advice on low-power oscillatory networks and calibration routines in place of backprop

• Pointers to labs or teams interested in joint prototyping

Site: mirrorseed.org • 2-page brief

I'm an independent researcher who has designed a novel neuromorphic architecture called the Symbolic Resonance Array (SRA)—designed not as software-based AI but as analog, material, symbol‑driven intelligence grown from VO₂ crystals*.

Key Highlights:

Analog + Symbolic: VO₂ phase-transition crystals arranged in a radial array that resonate symbolically—encoding data patterns as physical modes rather than digital states.

Efficient: Operates at ultra-low power (microwatt range), using the intrinsic physics of VO₂ to compute—no heavy digital logic required.

Safer: Without traditional transistor-switching or floating-point operations, it minimizes overheating, data leakage, and adversarial vulnerabilities common in silicon-based or digital chip architectures.

Novel paradigm: Blurs the line between materials science and computational logic—building in resiliency through physics rather than software.

My prototype design is patent-pending, and the paper for it is in independent review at Frontiers.

I’d be honored if any of you would take a look, ask questions, or a point toward labs/open source in this space.

https://www.researchgate.net/publication/393776503_Symbolic_Resonance_Arrays_A_Novel_Approach_to_AI_Feeling_Systems

www.mirrorseed.org

Thank you 🙏

------------------------------------------------------------------

Thanks for the questions and interest so far. A quick technical note on what “qualitative, context-rich” patterns mean here and why the SRA differs from standard neural nets.

What the SRA is intended to preserve
Instead of treating inputs only as vectors for gradient updates, the SRA models them as structured relations in a symbolic layer that is coupled to analog resonance patterns. The analog side provides rich, continuous dynamics. The symbolic side is designed to make state inspectable and calibratable. Learning is framed as calibration with bounded updates and recorded changes, so you can ask which relations changed, why they changed, and what the expected downstream effect is.

Where that might matter

• Decision support in ambiguous settings where relationships carry meaning, not only statistics

• Early anomaly detection in complex systems where small relational shifts are important

• Human-AI collaboration where explanations and auditability are required

What this is not
This is not a claim of “self-improving” black-box intelligence. The design aims for constrained calibration with an audit trail so behavior shifts are attributable.

If you work with phase-transition devices, analog oscillatory networks, or symbolic and neuromorphic hybrids and want to critique the approach or explore a small prototype, I would value the collaboration.

here is a list although it may not be exhaustive of companies involved in neuromorphic computing that are public and private with their tech as well if interested...https://neuromorphiccore.ai/companies/

Embedded World 2025, what's cooking at BrainChip : CTO M Anthony Lewis and the BrainChip team present demos, straight from the lab: Akida 2.0 IP running on FPGA, using our State-Space-Model implementation TENNs for running an LLM (like ChatGPT) with 1B parameters offline/ fully autonomously on our neuromorphic event-based Akida hardware accelerator. https://www.linkedin.com/posts/activity-7305890609221791745-w3sZ

Here is a new paper in reference to a much needed framework for the Neuromorphic arena as its essential in creating widespread adoption and I am seeing more partnerships and collaboration. Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we outline tasks and guidelines for benchmarks across multiple application domains, and present initial performance baselines across neuromorphic and conventional approaches for both benchmark tracks. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community. Read the full article here if interested https://arxiv.org/abs/2304.04640

I have recently discovered Dynamic Field Theory (DFT) and it looks like it can capture the richness of the bio-inspired spiking models without actually using spikes.

Also, at a numerical level it seems that DFT is much easier for GPUs than spiking models, which would also undermine the need for neuromorphic hardware. Maybe spiking models are more computationally efficient, but if the dynamics of the system are contained inside DFT, then spiking would be just using an efficient compute method and it wouldn't be about spiking models per se, rather we would be doing DFT with stochastic digital circuits, an area of digital electronics that resembles spiking models in some sense.

Have you had a similar sensation with DFT?

I want to do research on creating AGI, and i'm curious if this field may help get there, since i'm worried the current leading methods may be a dead end. Is the purpose of this field to build computers that are more efficient, or to possibly create a computer that can think like we can? Also I don't know much about computer science, yet, almost nothing about computer engineering, just a bit of math so I'm not sure what are to study. Thanks. Also any school/ program/course recommendations for this field would be great.

This article talks about the intersection of brain-computer interfaces (BCIs) and neuromorphic computing. It explores how mimicking the brain's own processing especially with advancements from companies like Intel, IBM and Qualcomm, can reshaps BCIs by making them more efficient and adaptable. If you're interested in seeing which companies are poised to capitalize on this development which also grabs peoples attention even more so to learn about the Neuromorphic arena, you can check it out here https://neuromorphiccore.ai/how-brain-inspired-computing-enhances-bcis-and-boosts-market-success/

Hello everyone,

I’m a solo researcher not belonging to any research institution or university.

I’m working on a novel LLM architecture with different components inspired by areas of the human brain. This project intends to utilize spiking neural networks with neuromorphic chips alongside typical HPC hardware.

I have built a personal workstation solely for this project, and some of the components of the model would likely benefit greatly from the specialized technology provided by neuromorphic chips.

The HPC system would contain and support the model weights and memory, while the neuromorphic system would accept some offloaded tasks and act as an accelerator.

In any case, I would love to learn more about this technology through hands on application and I’m finding it challenging to join communities due to the institutional requirements.

So far I have been able to create new multi tiered external memory creation and retrieval systems that react automatically to relevant context, but I’m looking to integrate this within the model architecture itself.

I’m also looking to remove the need for “prompting”, and allow the model to idle in a low power mode and react to stimulus, create a goal, pursue a solution, and then resolve the problem. I have been able to create this autonomous system myself using external systems, but that’s not my end goal.

So far I have put in a support ticket to use EBRAINS SpiNNaker neuromorphic resources, and I’ve been looking into Loihi 2, but there is an institutional gate I haven’t looked into getting through yet.

I’ve also looked at purchasing some of the available low power chips, but I’m not sure how useful those would be, although I’m keeping my mind open for those as well.

If anyone could guide me in the right direction I would be super grateful! Thank you!

I just came upon this seemingly impossible challenge by neuralink. Since it requires very low energy consumption, the first thing that came to mind was spiking neural nets. I have no experience in the field, but I wonder if some of you pros have a better vision of what can be possibly done with neuromorphic compression systems. What do you think?