Principles of Neurobiology
Principles of Neurobiology presents the major concepts of neuroscience with an emphasis on how we know what we know. The text is organized around a series of key experiments to illustrate how scientific progress is made and helps upper-level undergraduate and graduate students discover the relevant primary literature. Written by a single author in a clear and consistent writing style, each topic builds in complexity from electrophysiology to molecular genetics to systems level in a highly integrative approach. Students can fully engage with the content via thematically linked chapters and will be able to read the book in its entirety in a semester-long course. Principles of Neurobiology is accompanied by a rich package of online student and instructor resources including animations, journal club suggestions, figures in PowerPoint, and a Question Bank for adopting instructors. A robust student homework platform with instructor dashboard is also available.
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2021–2026years
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- Nice. I too have an interest in "unconventional computing" (analog/quantum/biological etc.) since i think that we are reaching the limits of conventional Von Neumann architectures and discrete mathematics in terms of efficiency, power consumption etc. Nature is analog and continuous with efficient energy consumption and while we have come very far by imposing discrete and boolean algebra on it, it is inherently only an approximation. Note also that at the quantum level nature is both continuous and discrete i.e. wave/particle duality and we again have a need to model continuous wave-like characteristics.
A few books that i am currently looking at which you might also find useful;
1) Principles of Neurobiology by Liqun Luo - Uses an integrated approach to neuroscience.
2) Handbook Of Unconventional Computing in 2-vols (Vol1-Theory, Vol2- Implementations) - A comprehensive resource.
- > This layering forms the dominant narrative of how intelligence may work and is the basis for deep neural nets. The idea is, stimulus is piped into the "top" layer and filters down to the bottom layer, with each layer picking up on more and more abstract concepts.
popular deep artificial neural networks (lstms, llms, etc.) are highly recurrent, in which they are simulating not deep networks, but shallow networks that process information in loops many times.
> columns.. and that's about it.
recommend not to oversimplify structure here. what you describing is only high-level structure of single part of brain (neocortex).
1. brain has many other structures inside basal ganglia, cerebellum, midbrain, etc. each with different characteristic micro-circuits.
2. brain networks are highly interconnected on long range. neurons project (as in send signals) to very distant parts of the brain. similarly they get projections from other distant parts of brain too.
3. temporal dimension is important. your article is very ML-like focusing on information processing devoid of temporal dimension. if you want to draw parallels to real neurons in brain, need to explain how it fits into temporal dynamics (oscillations in neurons and circuits).
4. is this competition in realm of abeyant (what you can think in principle) or current (what you think now) representations? what's the timescales and neurological basis for this?
overall, my take it is a bit ML-like talk. if it describes real neurological networks it got to be closer and stronger neurological footing.
here is some good material, if you want to dive into neuroscience. "Principles of Neurobiology", Liqun Luo, 2020 and "Fundamental Neuroscience", McGraw Hill.
more resources can be found here:
- > popular deep artificial neural networks (lstms, llms, etc.) are highly recurrent, in which they are simulating not deep networks, but shallow networks that process information in loops many times.
Thanks for the info. Is there anything you would recommend to dive deeper into this? Books/papers/courses/etc.
> recommend not to oversimplify structure here. what you describing is only high-level structure of single part of brain (neocortex).
Nice suggestion. I added a bit to make it clear that I'm talking about the neocortex.
> 1 & 2
Totally. I don't think AI is a simple as building a Darwin Machine, much like it's not as simple as building a neural net. But I think the concept of a Darwin Machine is an interesting, and possibly important, component.
My goal with this post was to introduce folks who hadn't heard of this concept and, hopefully, get in contact with folks who had. I left out the other so I could try to focus on what matters.
> temporal dimension is important. your article is very ML-like focusing on information processing devoid of temporal dimension. if you want to draw parallels to real neurons in brain, need to explain how it fits into temporal dynamics (oscillations in neurons and circuits).
Correct me if I misunderstand, but I believe I did. The spatio-temporal firing patterns of minicolumns contain the temporal dimension. I touched on the song analogy but we can go deeper here.
Let's imagine the firing pattern of a minicolumn as a melody that fits within the period of some internal clock (I doubt there's actually a clock but I think it's a useful analogy). Each minicolumn starts "singing" its melody over and over, in time with the clock. Each clock cycle, every minicolumn is influenced by its neighbors within the network and they begin to sync up. Eventually they're all harmonizing to the same melody.
A network might propagate a bunch of different melodies at once. When they meet, the melodies "compete". Each tries to propagate to a new minicolumn and fitness is judged by other inputs to that minicolumn (think sensory) and the tendencies of that minicolumn (think memory).
I think the evolution is an incredible algorithm is because it relies as much as it does on time.
> is this competition in realm of abeyant (what you can think in principle) or current (what you think now) representations? what's the timescales and neurological basis for this?
I'm not familiar with these ideas but let me give it a shot. Feel free to jump in with more questions to help clarify.
Neural Darwinism points to structures - minicolumns, cortical columns, and interesting features of their connections - and describes one possibility for how those structures might lead to thought. In your words, I think the structures are the realm of abeyant representations while the theory describes current representations.
The neurological basis for this, the description of the abeyant representation (hope I'm getting that right), is Calvin's observations of the structure of the brain. Observations based on his and other's research.
To a large extent, neuroscience doesn't have a great through-line-story of how the brain works. For example the idea of regions of the brain responsible for specific functions - like the hippocampus for memory - doesn't exactly play nice with Karl Lashley's experimental work on memory.
What I liked most about this book is how Calvin tried to relate his theory to both structure and experimental results.
> overall, my take it is a bit ML-like talk. if it describes real neurological networks it got to be closer and stronger neurological footing.
If, by ML-like talk, you mean a bit woo-woo and hand wavy. Ya, I agree. Ideally I'd be a better writer. But I'm not, so I highly recommend the book.
It's written by an incredible neuroscientist and, so far, none of the neuroscience researchers I've given it to have expressed anything other than excitement about it. And I explicitly told them to keep an eye out for places they might disagree. One of them is currently reading it a second time right now with the goal verifying everything. If it all checks out, he plans on presenting the ideas to his lab. I'll update the post if he, or anyone in his lab, finds something that doesn't check out.
> here is some good material, if you want to dive into neuroscience. "Principles of Neurobiology", Liqun Luo, 2020 and "Fundamental Neuroscience", McGraw Hill.
Why these two textbooks? I got my B.S. in neuroscience so I feel good about the foundations. Happy to check these out if you believe they add something that many other textbooks are missing.
- What kind of book are you looking for? The "Springer Handbook of Odor" is a comprehensive reference, although it mainly covers the "input" side of things. If you're more interested in the Neuroscience of smell, you could try Chapter 6 in Liqun Luo's "Principles of Neurobiology".
BTW, anaesthesia is not mediated by olfaction, but by diffusion of anaesthetic agents into the bloodstream.
edit: if you are looking for a more general and accessible introduction, you could look at Charles Sell's "Chemistry and the sense of smell". It goes into reasonable detail but still maintains genral readability, unlike the science textbooks I recommended above.
- 1) Principles of Neurobiology by Liqun Luo - Uses an integrated approach to neuroscience.
Just ordered a copy, thanks for the pointer. In the meantime, I have a copy of Neurobiology by Professor Gordon M Shepherd, to work through.
2) Handbook Of Unconventional Computing in 2-vols (Vol1-Theory, Vol2- Implementations) - A comprehensive resource.
Yeah, I have those in an Amazon list waiting to be purchased "sometime soon". :-) Unfortunately, copies are currently going for around $448.00 USD. :-(
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