An important step in #ComputationalNeuroscience 🧠💻 was the development of the #HodgkinHuxley model, for which Hodgkin and Huxley received the #NobelPrize in 1963. The model describes the dynamics of the #MembranePotential of a #neuron 🔬 by incorporating biophysiological properties. See here how it is derived, along with a simple implementation in #Python:
The Priesemann Lab ( @ViolaPriesemann) is looking for PhD candidates (12 free positions) and PostDocs (2) in a very interesting project investigating the neural basis and cognitive properties of #curiosity. Start: summer 2024 in #Göttingen
I'm happy to announce the start of a new free and open online course on neuroscience for people with a machine learning or similar background, co-developed by @marcusghosh. YouTube videos and Jupyter-based exercises will be released weekly. There is a Discord for discussions.
For more details about the structure of the course, and to watch the first video "Why neuroscience?" go straight to the course website:
Currently available are videos for "week 0" and exercises for "week 1", but more coming soon.
Why did I create this course? Well, I think both neuroscience and ML can be enriched by knowing about each other and my feeling is that a general purpose intro to neuro or comp-neuro isn't the right way to inspire people in ML to be interested in neuro.
I hear a lot about neuroscience inspiring AI, but I think there's understandable scepticism about that from ML people. I don't want people to take neuro ideas and apply directly to ML, I just think we get a richer picture of what both fields are doing if we think more widely.
In other words, we should be thinking that we are somehow studying the same problem in different ways. You see that in the early history of the field, and it's very inspiring. (Yes, this is pretty much just saying that cognitive science is cool, but my scope is a bit narrower.)
The focus then is not on how neuroscientists think the brain works, but on the mechanisms the brain uses. These are strange, inspiring, and often their contribution to intelligent behaviour is still deeply mysterious.
The first video of the main part, on the structure of neurons, finishes with recent research (from @ilennaj and @kordinglab among others) on what the function of dendritic structure might be. No answers, just ideas.
And that's going to be another key part of this course. Research level problems are not hard to find in neuroscience, and the aim of this course is to empower students with the tools to start finding and working on them straight away.
Most of the exercises in the course won't have correct answers. They're starting points for further investigation. We'll be downloading and exploring open neuroscience datasets using methods from computational neuroscience and ML.
The course is not supposed to be comprehensive. It's a short course and the aim is more to get inspired and start on a longer road. I'd expect everyone to get something different out of it, and I'm happy if for some people their take home is "neuroscience is not for me"!
In some ways, it's the course I would have liked to get me into neuroscience and for my incoming PhD students from non-neuro backgrounds to be able to take. It's personal, and full of the sort of stuff that inspires me to be interested in neuroscience.
Well, I hope that some of you might be interested to follow along in the next few weeks, and since it's the first time I'm giving this course please do give feedback by email, Discord or however you like. Also, please feel free to re-use materials however you like.
What neuroscience / comp neuro papers would you put on a recommended reading list if you wanted to emphasise the creativity, inspiration and joy of the field? I think some suggestions would overlap with the most famous or epochal papers, but some might be quite different.
It's time to prepare your spiking neural network abstract. The submission deadline for the SNUFA workshop is September 29th. Participation is free. Submit and register here:
This is a great way to get your work out to the right community, as we get around 700 participants every year. Talks are seen by hundreds to thousands of viewers including YouTube views. Check out the library of previous talks on our channel:
I'm currently developing a new course "Neuroscience for machine learners" that I hope to be able to make publicly available, and I'd love to hear what you think should be in it.
It's aimed at people with a machine learning background to learn a bit about neuroscience. My thinking is that neuroscience and ML have had fruitful links in the past, and may again in the future (although right now they're drifting apart). This course is designed to give students the background they'd need to be able to discover, understand and make use of new opportunities arising from neuroscience (if they do). I'm not trying to tell them only about the bits of neuroscience that we already think are applicable to ML, but to give them enough background to read and understand enough neuroscience to allow them to make new discoveries about what might be applicable to ML. The constraint is that it can't just be an intro to neuro course I think, because I'm not sure how compelling that would be to students with an ML focus. The course is 10 weeks and will have quite a practical focus, with most of the attention on weekly coding based exploratory group work rather than lectures. (Similar to @neuromatch Academy.)
I have thoughts about what should be on this course, but I'd love to know what you all think would be most relevant.
It’s almost a week since my course at #neuromatch ended and it was 🔥 Learned so much about #compneuro and modelling and I had a lot of fun. I still wanted to say thanks to NMA, my amazing TAs and pod for making it such a great experience! 🧠🚀🧮
Invited speakers this year include:
⭐ Rodolphe Sepulchre
⭐ Melika Payvand
⭐ Gabriel Ocker
⭐ Jeff Krichmar
In previous years we usually get over 700 participants, and about 200 live at each talk. Not to mention hundreds to thousands of views after the event on Youtube. Best of all - it's all free so there's really no reason you shouldn't already be writing your abstract.
And if you're still not convinced, check out previous years' talks and our monthly seminar series on my Youtube channel: https://www.youtube.com/@neuralreckoning