Working with neuromorphic and analog computing. Curious about abstractions. Cares about #FOSS
Author of Neuromorphic Intermediate Representation in Nature Communications: https://www.nature.com/articles/s41467-024-52259-9
| [email protected] | |
| Website | jepedersen.dk |
| GitHub | github.com/jegp |
| https://www.linkedin.com/in/jens-egholm-pedersen-69543117/ |
@drgroftehauge Tak Morten!
Godt spørgsmål! Jeg har en lille side med resultatet fra en af vores papers her: https://jepedersen.dk/posts/202510_nrf/
Og så er der nok lidt information om en af mine andre projekter her: https://neuroir.org/
Men faktisk burde introduktionen i mit speciale være nooogenlunde tilgængelig https://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A2001119&dswid=-1044
Ellers vil jeg gerne komme med et par korte toots, hvis det er interessant :)
Neuromorphic computing exploits the laws of physics to perform computations, similar to the human brain. If we can “lower” the computation into physics, we achieve extreme energy gains, up to 27-35 orders of magnitude. So, why aren’t we doing that? Presently, we lack theories to guide efficient implementations. We can build the circuits, but we don’t know how to combine them to achieve what we want. Current neuromorphic models cannot compete with deep learning.
How privileged is it that I was paid to study for 5 years? Thank you to my family, colleagues, supervisors, committee, opponent, and many many others who made this possible. I've been rewarded with personal and professional insights, skills, friendships, awards, and memories that will last me a lifetime.
❤️
https://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A2001119&dswid=-1044
We built spiking neural networks that outperform ANNs on event-based vision by initialising them with spatio-temporal receptive fields. Our theory-guided design beats naive approaches by 42%!
New paper in Nature Communications 🧠⚡
Paper (open access): https://www.nature.com/articles/s41467-025-63493-0
Video summary: https://youtu.be/qtHkrx4tYfI
Neuromorphic computing mimics brain efficiency but lacks theoretical guidance. Here, authors develop a computational foundation for processing signals in space and time in spiking neural networks that can outperform standard neural networks in event-based vision.
Neuromorphic computing mimics brain efficiency but lacks theoretical guidance. Here, authors develop a computational foundation for processing signals in space and time in spiking neural networks that can outperform standard neural networks in event-based vision.
Why should you read our paper on covariant #neuromorphic networks, you ask? Good question!
We're connecting decades of work in computer vision with decades of work in spiking networks. It's cool because it explains signal processing in spiking networks really well.
The theory is also cool because it works in practice! Trained on an event-based vision task against regular ANNs of similar complexity, spiking networks wipe the floor with ANNs. Interesting, right?
Neuromorphic computing mimics brain efficiency but lacks theoretical guidance. Here, authors develop a computational foundation for processing signals in space and time in spiking neural networks that can outperform standard neural networks in event-based vision.
@neuralreckoning Thank you! And thanks for giving it a read!
I experienced several times that my experiments were *wrong* because the underlying code failed in some way.
Did that happen to you? Do you know of examples where conclusions were made that turned out to be faulty (can be anonymous)? I imagine it's a common thing, but I haven't heard about it a lot in the community...
It's the first post out of three that describes our strategy, and we'll be launching a host of initiatives (check out the shiny new website!) to help accelerate open and accessible science in the neuromorphic domain!
Give it a read, join the discussion on Discord, and stay tuned for more! 🚀
2/2
Throughout my PhD I put in a lot of effort in building open-source software, because I fundamentally believe that software is *necessary* for good science.
As the newly elected chair of Open Neuromorphic, I've put elaborated the argument in our newly launched strategic vision for ONM: https://open-neuromorphic.org/blog/strategic-vision-open-neuromorphic/
1/2
