Electricity in biological systems, mostly due to movement of free Ions (ionic current)
Main biological ions
3 to 5nm with = 90mV –> V/m. Very high!
Using Nernst equation we can calculate voltage due to separation of a single ion species across membrane. Equilibrium condition..
Goldman-Hotchkin-Katz.. equation.
Sodium-potassium pump pumps 3 sodium ions out per potassium ion in. There are also ion channels that are only permeable to potassium, which increases the membrane potential
Luigi Galvani. Make muscle moves with tweezers, Electrochemistry involved. His nephew would reanimate certain parts of the human body –> inspired Frankestein. Galvanized corpse!
Salt is actually enough to make muscle twitch, by changing ion balance in the nerves. Don't put salt in a Wound...
Bodies have bioelectrical patterns that store information "memories" separately from genomic, and anatomical states. These bioelectrical patterns play a huge role in developmental processes, so that being able to control them is basically like a holy grail of regenerative medicine. It also offers interesting new insights for AI and cognition.
https://www.youtube.com/watch?v=RjD1aLm4Thg
Detailed notes:
2:55 memories are preserved in metamorphosis/regeneration. 5:16 unicelular creatures "think" 9:00 planarian can be cut into pieces and each will regrow to the correct full organism! Each piece knows about the whole (a bit like a hologram!), and can do collective decision-making to guide themselves to the correct structure (important to know when to stop 8:41). 9:21 planarians have conquered aging! (new favourite animal here) 10:00 further examples of pattern homeostasis (keeping its shape, robustly, to the programmed shape) 14:40 biology is dealing with hardware right now; can we move to dealing with software? 16:26 bioelectric mechanisms in brainn come from ancient mechanisms found in cells through the body 20:00 seeing cancer via electrical signal anomaly 20:20 how to control these bioelectric processes 22:20 editing the morphology of organisms, without changing the genome, just by interacting with the developmental processes! wow! 24:07 computational modeling 24:30 altering pattern memory. Wow, an electrical memory that holds information, separately from anatomy and genome! 27:34 extending connectionist models to understand this. Stable attractors (like Hopfield nets!) 28:31 applications in *regenerative medicine*. Making frogs regrow their legs! 29:20 and reversing birth defects. Hmm, it's really cool that you can bypass genomics, but this patterning only affects anatomy right? If a gene generating some protein essential for some biochemical pathway is missing, you can't fix it with bioelectrics right? See comments at 40:27 But still the applications seem awesome 30:50. The endgame. A biological compiler to design organisms. As Freeman Dyson wrote: "a new generation of artists will be writing, composing genomes with the fluency that Blake and Byron wrote verses" 31:40. The future. a highly-robust ML technology, based on non-neural architectures. I can smell our friend Physarum polycepharum appearing soon :P (didn't but would have been cool) 32:52 "non-neural networks" lol. Robot scientist lol. 33:36 Thank Q&A 34:36 difference between behavioural and anatomical electrical patterns, and how to control the anatomical ones. Anatomy at low frequencies, behavior at high frequncies; they are pretty well-separated. 37:18 best approach to create truly intelligent systems 39:30 what about plants, and mechanical signaling. Plants independently evolved bioelectric control. Mechanical forces interact with electrical effects (and also with genetics ofcourse). Key question: "at what point in that control structure is it most efficient to intervene" (to me the hardware-software divide is just about identifying the parts of a system that are more suitable for control 41:56 Consciousness. 44:12 Timescale of control signals. Very short interventions, as you are basically just rewriting the electrical memory :) 46:22 non-neural nets 48:23 Ethical concerns 49:42 relations to signal transduction networks and systems biology models 50:51 relation to reaction-diffusion models