A Universal Theory of Brain Function

[@TRVMASMAS]

The cool part of the simulator in our heads is when we go to sleep and turn off the gyroscope, close the eyes, turn off lights, keep warm, long term memory recalling, prediction, planning, discriminator all off reach as gaba starts to stop communication between parts, then the info from the senses is noise to the generator to make dreams out of the things we experienced during the day and react to those as if it would be real. The best part is when gaba lowers as cortisol rises and you realize you are in a dream. This is not nature's bug but an important feature which often reminds us that the waking world is real and we are conscious as we hace successfully classified the dream experience as a forgery. This knowledge about what is not reality is what allows us to acknowledge consciousness by contrasting it with the fake experiences of dreams. A feature that could be achieved on artificial systems too.

[@mhuruuk]

Good point

[@jeroenhekking8398]

"right, fellow humans?"

[@protestthebread1046]

We made them dream, and they woke to see us for the first time

[@deltamico]

I always explained consciousness to myself as a sensory input of brain activity itself, but this is nice.

[@anywallsocket]

to be fair i don't think the lucid experience is at all necessary for understanding reality by contrast with the imaginations of sleep -- you can just compare it with all the non-lucid dreams you remember when you wake up.

[@keylime6]

I’m on the ML to neuroscience pipeline now 😂

[@ibthesam]

I taught this to an LLM Model through interactive conversation 😂

[@4thpdespanolo]

Take the good ideas and come back to ML

[@bes1desme]

same shit lmao

[@alexanderhemming6148]

this video is asorta about the books "being you" by anil seth and the book "the experience machine by his colleague and similar sussex professor "andy clarke"

[@atommax_1676]

Same here. But I think it's better to get new ideas and knowledge in neuroscience and come back to ml. Anyway, good luck

[@DR-54]

as someone experienced with psychosis, I can attest to the validity of reality itself being a hallucination. If I hallucinate something in the corner of my eye and then look directly at it or use my phone camera, it'll immediately disappear most of the time. This is much more difficult to model if you see sensory first, but if you see prediction first, it makes perfect sense. I've supported this "backwards" sensory model since the first moment I learned of it.

[@JohnSmith-op7ls]

Reality isn’t a hallucination. By definition.

[@DR-54]

@@JohnSmith-op7ls What defines reality is actually just some component of your brain. That part of the brain serves as a representative of all things real. Thereby, all things marked within your experience as "reality" can simultaneously be entrapped within only your existence, hallucinated. If it can be hallucinated, it is ALREADY hallucinated. Every sense you have can suffer hallucinations, but that means that every sense you have is already hallucinated, and that all parts of your experience are a strictly-managed hallucination--that's still a hallucination. This model easily handles your argument. It's just a really powerful model. Doesn't this remind you of how psychosis can present in some people with true hallucinations, but in other people as only pseudo-hallucinations? The only difference between the two is how your brain perceives its "reality"ness, with pseudo-hallucinations being defined by how the person having them does not perceive them to be real.

[@JohnSmith-op7ls]

@ Reslity is not a hallucination. Reality is reality, its state is what it is regardless of how you perceive it or if you’re even there to perceive it. What you’re talking about is how you preconceive that reality, what your subjective model of reality is. If you’re blind and don’t see a wall, it doesn’t mean you can walk through it. Hallucinations are pre icing something that isn’t actually there. Perceiving it doesn’t make it real, it simply means you think it’s real to some extent. Seeing a fire that doesn’t exist won’t burn your hand if you touch it.

[@mou8762]

@@DR-54Your argument is just a hyperbolic use of “hallucination”. This reads more as dogma than an intellectual argument.

[@C90-k2f]

​@@JohnSmith-op7ls According to advances in neuroscience, it is a controlled hallucination, as per neuroscientist Anil Seth.

[@disgruntledwookie369]

I think one of the most interesting aspects of this is that it demonstrates the ability for the brain to disagree with itself. In the sense that it is possible for us to know at some higher cognitive level that the face is concave whilst still being unable to "see" it that way.

[@SamogitianJesus]

It's our conscious ability to reason that is able to disagree with sensory input (valid or not) and formulate reactions to it accordingly.

[@xavierkibet4170]

​@@SamogitianJesus I am really interested in how this "reasoning" works as I guess it would entail the brain modelling and understanding itself. I'm not sure how it works. I'm just curious.

[@SamogitianJesus]

@@xavierkibet4170 it's so complex, it is very difficult to properly model, especially as our understanding of consciousness is still in early stages.

[@Rihzi]

Your channel is a goldmine

[@mohammedmokdadrocks]

If you could really describe the math behind Friston's work in a digestable format, that would be an enormous feat

[@anguscampbell3020]

Its just Bayes theorem more or less.

[@laviniamitiko7222]

I’m a computational neuroscientist and have been following your channel since your video on neuronal manifolds three years ago. I’m always impressed by how you cover such a wide range of topics in computational neuroscience-from biophysical modeling (like Hodgkin-Huxley) to concepts like the Hopfield model. Congratulations on your amazing work! :)

[@shubhamrasal8930]

Great video. Your explanations are so succinct that I get it on the first try. I don't think there's anyone in the world who explains the brain as well as you. Thank you for putting in the effort.

[@ArtemKirsanov]

Thank you! I’m glad you found it helpful!!

[@samuelbucher5189]

14:55 I've actually experienced a relatively long version this process. It's that feeling of when you are looking at something, but are not sure what it is for almost a second, while your brain comes up with numerous barely tangible suggestions.

[@scubajotaro]

happens a lot when i'm super sleep deprived

[@jay_13875]

The "Name one thing in this photo" meme comes to mind

[@kn49]

Definitely happens when I'm sleepy, just waking up and staring at something I don't quite recognize yet; it's a moon, it's a plane, oh no it's just my ceiling fan.

[@TheForbiddenLOL]

This is strangely intuitive - I feel like many inquisitive people who have taken psychedelics have likely gained an understanding of the internal world model and the way perception can be manipulated by causes - and it aligns so well with active machine learning research. Fascinating stuff, Artem. Your videos really tempt me to go back to school for computational neuroscience.

[@TheARN44]

I was thinking the same thing. It feels weird to watch a video about pattern recognition in neuroscience and recognize the patterns in your own experience of thinking.

[@ArtemKirsanov]

Thank you!

[@user-yh6tf6ne4s]

This is the best videos I have seen on explaining free energy principle in simple terms as related to cognition. Thank you so much! I will be eagerly waiting for the one explaining its math. Great job!

[@falsegod3164]

Idk why this Channel doesn't have a billion subscribers lol. The quality and topics are top of the line. Gonna go thru all ir vids now, it's a gold mine ✨️

[@lucadoge]

MIND BLOWN BY THE INTRO!!

[@quiksilver10152]

Just in time as I begin my PhD in neuroengineering. You are my hero!

[@starlight36887]

The parallels between the brain and machine learning algorithms are obvious, what really surprised me was how much we apply this same thought process to solve problems in Geophysics... Mind blowing!

[@mrazo]

This is an incredibly well-produced video. I have been obsessed with the ideas behind the Free Energy Principle for several years now, and this is by far the most straightforward explanation I have seen. Keep up the fantastic work! I would like to add that I believe Friston's ideas are much more general than a theory about how the brain works. The Free Energy Principle might be at the core of the existence of complex adaptive systems--what David Krakauer calls problem-solving matter. The late (and sadly mostly unknown John O. Campbell) put it best in his book "The Thinking Universe."

[@ArtemKirsanov]

Thank you!! I agree, FEP is much more broad - about living systems in general

[@hyperduality2838]

Cause is dual to effect -- causality. Information is dual. Syntropy (knowledge, prediction) is dual to increasing entropy (lack of knowledge) -- the 4th law of thermodynamics! Concept are dual to percepts -- the mind duality of Immanuel Kant. "Always two there are" -- Yoda.

[@hyperduality2838]

@@ArtemKirsanov Complexity is dual to simplicity -- free energy is dual. Perceptions (effects) are becoming concepts (causes) -- retro-causality or syntropy!

[@DistortedV12]

@@mrazo yep, that is why Karl Friston distinguishes free energy principle with “active inference” ;)

[@DistortedV12]

FINALLY THE VIDEO IS HERE, Karl Friston is basically saying we are doing what LLMs do when they train on data, but updating all the time to make our sensorial world (not token world), reach some equilibrium (easily predictable state) by acting or predicting accuractely head of time (akin to reducing loss on next word prediction). Sometimes we even predict future sensory state surprise like being in dark room too long will cause us hunger and be out of whack/equilibrium

[@hyperduality2838]

Making predictions is a syntropic process -- teleological. Cause is dual to effect -- causality. Information is dual. Syntropy (knowledge, prediction) is dual to increasing entropy (lack of knowledge) -- the 4th law of thermodynamics! Concept are dual to percepts -- the mind duality of Immanuel Kant. "Always two there are" -- Yoda.

[@derickd6150]

@@hyperduality2838You leave this comment on every video and I guarantee you it is just word salad

[@hyperduality2838]

@@derickd6150 Antinomy (duality) is two truths that contradict each other -- Immanuel Kant. Truth is dual to falsity -- propositional logic or Boolean algebra. "This sentence is false" -- the sentence or words. If the sentence is true then the sentence is false. If the sentence is false then the sentence is true -- antinomy. The sentence is true and false both at the same time -- duality. Syntax is dual to semantics -- languages, communication or information. Words, sentences, languages are all dual. If you want a word salad then you are already using duality but you seem unaware of this fact! Enantiodromia is the unconscious opposite or opposame (duality) -- Carl Jung. Sense is dual to nonsense (word salad). You are using duality to claim that duality does not exist hence your perspective is contradictory or dual. All languages (words) are dual.

[@elibarbq]

@@hyperduality2838 Not sure if Haiku or schizo

[@hyperduality2838]

@@elibarbq Haiku. Your mind is dual, perceptions or measurements are becoming conceptions, ideas or causes. Mind (syntropy, synergy) is dual to matter (entropy, energy) -- Descartes or Plato's divided line. All languages are dual -- LLMs. Syntax is dual to semantics -- languages, communication or information. Lie groups (exponentials) are dual to Lie algebras (logarithms) -- information is dual.

[@Amfivolia]

Yesss excited to watch this :) your videos inspire mine a lot - keep up the amazing work!

[@carpeet3743]

0:17 bombastic side eye

[@HominidPetro]

This is the basis of DishBrain - "In vitro neurons learn and exhibit sentience when embodied in a simulated game-world", which was published 3 years ago.

[@MantrTheSpiceGuy]

Damn, we got Black Mirror-level horrors beyond our comprehension before GTA6

[@IvanSpaceBiker]

Hal life 3 confirmed

[@sevdattufanogullari6581]

Controlled hallucination is a really good way of explaining what humans experience

[@starrmont4981]

"Life is a dream within a dream"

[@U-inverse369]

That puts me in the Mindset: let go and let life guide you. This is whole another level of being, instead of the egomind. Intuition is my guidance.

[@testboga5991]

Calling it free energy principle is interesting, as apparently the creator didn't predict which association this would cause in other scientists. Why not call it prediction error principle? It's not energy, energy is a thing that can perform work. Prediction errors don't perform work.

[@DistortedV12]

yeah i hate that term.. probably wanted it to be connected to physics in some way

[@SingularityEternal]

@@DistortedV12 It _is_ connected to physics.

[@hyperduality2838]

Making predictions is a syntropic process -- teleological. Cause is dual to effect -- causality. Information is dual. Syntropy (knowledge, prediction) is dual to increasing entropy (lack of knowledge) -- the 4th law of thermodynamics! Concept are dual to percepts -- the mind duality of Immanuel Kant. "Always two there are" -- Yoda.

[@epajarjestys9981]

@@hyperduality2838 ur mom

[@hyperduality2838]

@@epajarjestys9981 There is also a 5th law of thermodynamics as energy is dual! And everything in physics is made out of energy.

[@intptointp]

I find it beautiful that we really have been able to demonstrate to ourselves how we think by building out these ML models and demonstrating their validity.

[@rand0mletters1]

The map is not the territory. It’s critical not to assume we are understanding ourselves any better through concepts in ML. We have demonstrated a way in which a type of thinking is possible but it could be as completely alien to how our brain works, as a beehive is.

[@JohnSmith-op7ls]

We haven’t. Machine learning models are nothing like BNNs

[@tmas]

⁠​⁠@@JohnSmith-op7lssurely on a higher level the behaviour derived from ml is similar to that of brains. Like i could be wrong but to me the way i recognise faces is just through picking out key features, then describing someone’s face as a specific combination of those features with each feature weighted differently person to person, just like how cnn facial recognition works. Its all emergent behaviour after all. Though i will obviously admit that there are massive lower level distances, not least that human brains don’t have “layers”, instead woven “sections”.

[@vastabyss6496]

@@JohnSmith-op7ls while most ML models share very little in common with BNNs, Deep RL seems to share a surprising amount in common. Here are some examples: - CNNs are inspired by the mammalian visual cortex, and conceptually work very similar, with each neuron looking for patterns in a small patch rather than looking at the entire image. - Research show that dopamine neurons in some areas of the brain mimic the TD error, which is the most common algorithm used for training RL algorithms. - Experience replay (and by extension prioritized experience replay) is a biologically inspired mechanism used to improve sample efficiency in deep RL algorithms (especially off-policy RL, like Q-learning). - Some RL algorithms judge actions against a baseline, which balances the amount of positive and negative rewards, loosely similar to how our brain balances pain and pleasure through the quantity of dopamine. - The SOTA RL algorithm DreamerV3 uses trained world models to imagine and learn from future scenarios. These world models are trained in a way that emulates the free energy principle.

[@kn49]

What this video has got me thinking about is how this recognition -> 'latent distribution space' (an embedding space...?) -> generative/predictive pipeline might work in machine learning for, well, actually learning things. Right now our models (as far as I'm aware) require extensive training which is basically 'baked' into the model itself, and that's that. You have a context window, but you can't adjust any of its weights or activations during inference; the AI won't remember anything you say or do the moment you open a new, out of context, window. This is clearly not how humans or other animals work - we are actively taking information in, and re-weighting our internal model which allows us to learn and then do new things (like learning language, math, how to do carpentry, etc.) - bridging this gap seems to be something still being tackled in machine learning.

[@Moe5Tavern]

3:58 "weighing" is the past tense of "to weigh" not "weighting" . Just for those out there still learning English. Good luck!

[@rogerzen8696]

The similarity between the math of this and PPO/GRPO in reinforcement learning is astounding! 😮

[@raajchatterjee3901]

Nice work! Excited for you to cover some of the FEP math foundations in future videos.

[@isaacgroen3692]

AMAZING! This explains the moses illusion in a very direct and technical manner.

[@4thpdespanolo]

This gives me hope for curriculum learning

[@WheelScreech]

Dude watching this high is insane

[@Dominis.]

Well, I've got a few questions now. What does this mean for the theory on schizophrenia? What could we say about "being in the moment"? What could we say about anxiety and groundedness?

[@JaredQueiroz]

wait, I can easilly break the illusion if I want.... I can literally shift between one and the other as I please (I remember to be able to do the same with that blue/black or white/yellow dress, shifting between one and the other as I wanted.... The same thing with various optical illustions... Maybe I'm just really good at self-induced pareidolia)

[@misslayer999]

Yeah same here. I've always been able to intentionally shift between which way I see these kinds of illusions (the dress, rabbit/duck, face/vase, etc)

[@ArtemKirsanov]

That's interesting! I can also easily switch between the colors of the blue/yellow dress and things like face vs vase. But not for this one. I think it's because for other illusions there are 2 alternative explanation that are equally likely - there is no strong bias whether you should expect to see a rabbit or a duck. As a result, the brain is "flickering" between the 2 equally compatible explanations. But here the convex face has a strong prior, which is why is typically dominates (for most people). But there are individual variations for sure!

[@kn49]

Some people apparently don't get tricked by the hollow face illusion. I can't recall if it was determined to be a genetic cause or a brain disorder however.

[@cavesalamander6308]

It appears that Occam's principle is a verbal expression of the principle of free energy minimization.

[@starrmont4981]

I agree. Low energy systems have a higher probability of existing than high energy systems, hence "the simplest solution is the most likely."

[@ArtemKirsanov]

Yes! They are really related!

[@idopaz1659]

Thank you very much for all the amazing content. I think that without explaining Markov chains and the boundary conditions of 'external' vs 'sensory' vs 'internal' states, it's not very clear why inner models would develop. And without the connection to Legendre trasform the term 'free energy' seems unrelated. That being said, this was a great introduction to inference, which is obviously related.

[@Murilo-r4w]

Seu trabalho é incrível! Obrigado pelo cuidado com os áudios, facilitou muito!

[@jackaboiXD]

i literally thought of this yesterday and now i see a whole video on it

[@johann-lh392]

OMG, I was thinking of revisiting the Free Energy Principle recently and then here you uploaded it 🤩! Thank you so much! I have always been intrigued by this model (and your explanation)! 😄

[@carlosserrano4048]

Nice work putting this together. So attainable.

[@GrowlingBearMedia]

Thank you Artem for this !!! 😍👌 Been diving deep into Karl Friston by Micheal Levin's talks with him, super glad to have you cover this !

[@philosophia5577]

Love you, thanks for this awesome work you're doing!!

[@Vlow52]

Beautifully explained. Perhaps it would be a good idea to make suggestions of how creativity works in the base of this brain model.

[@saneboysoup4168]

That's a good explanation for visual illusions and maybe even audio illusions like the yanny laurel thing

[@wp9860]

Friston states that things like living systems must APPEAR to be making inferences about their environment and the effects of their actions: predictive processing. It doesn't say they actually are implementing an inference algorithms. When people are dealing with complicated decisions like career planing, it is hard to believe this inference process is not internalized. In complex decision making, or deliberative decision making, is inference actually being applied? In his writings, Friston references E T Jaynes. Jaynes observed that a physical free energy problem was easiest to solve, required the fewest assumptions, if it was analyzed as a subjective probability calculation. Does this come into play in the human mind? Does the brain do its thermodynamic thing, and as a result, creates subjective judgements as co-products, similar to what a dual problem does in mathematics?

[@92kmoreno]

Very interesting❤, I’m also very intrigued to know how the brain creates concepts and which parts are responsible for that function.

[@Tearlach-Aengus]

At this time stamp I had the realization 16:15 That there has to be something different with people with adhd because sometimes they knock things off And it's it it's as if it appears so there has to be something there be interesting to look into it

[@ShardulIyer]

Kinda but also nope, it's sort of a data mismatch/corrupted signals seen with dyspraxia & other movement disorders meaning A. Essentially adhd, dyspraxia & others with certain movement and perceptual distortions, compute the sensory data correctly but when the signal is being transmitted to other parts, it could get corrupted (incomplete data), so the brain does what it does best & imputes/fill in the blanks but the imputed data could be from a source near the object or last known location/estimated trajectory. B. the brain(atleast adhd) wants to sort through multiple concurrent sensing during the multiple comparative free-energy state between the generative vs observation models. But since ADHDers are better at serial task vs parallel tasks as ADHD can cause the brain to be constantly overwhelmed by unnecessary sensory data many filter to generating garbage data to avoid something that catches them off guard, thus further causes a mis match between projected/estimated trajectory as the signal is much slower than time required for movement centers to initiate response, leading to delayed/mismatched signals. C. This is seen many times when ADHDers think they did something, only to shockingly realise that they didn't as some sort of signal processing mismatch occurred within the attention vs reaction networks leading to us realising that perception distortions are occurring at multiple instances but not like say a psychosis/hallucinations etc states but rather requiring signal boosting (stimulant or NDRI boosters) to offset the lag, attention networks face while relaying information for action/response in less stressful environments.

[@Froany]

Such a great video!!! Always inspired by these

[@avboy6481]

Cool video, I wonder how this ties in with other functions of the brain such as the effect of hormonal or emotion changes. I'm also curious as to whether this is something that is partly "pre-programmed" or pre-determined in the brain like how we have a larger response to things that look like spiders and snakes or how animals seem to know what is needed for survival right after being born / no prior experience.

[@AB-wf8ek]

The more I observe and learn about how we perceive the world, the more the word resonance, becomes pertinent to describing cognitive functions.

[@alexharvey9721]

A very bold title and one I'm excited to see explored!

[@fiachhoffman9590]

This gets real interesting when you apply Metzinger's phenomenal self-model

[@QRstudy-g2h]

18:57 isn't this also similar to the loss function of a variational autoencoder?

[@josephlabs]

Looking forward to the math video!

[@devrim-oguz]

I think after the minute 14:00 you diverge from how the brain works and approach more to how computer vision systems work.

[@dinghaoluo2769]

Actually right from the start when he starts talking about Friston’s theory he’s mistaking theory for reality. The FEP is supported by some literature but it is a highly theoretical framework that’s hotly debated, and he’s taking it all as if it’s just ground truth. But you are right, when he starts talking about the ‘sensory model’ in the brain it’s just a complete mess. It makes sense in terms of computer vision but biologically it’s not even remotely that clean-cut, and to boot he doesn’t mention any neuroscience research that supports any of his points in this video. His past videos are not perfect, but much better supported than this one. This one watched like a video with the singular purpose of doing an ad for Brilliant.

[@Neomadra]

So, according to this hypothesis we would be able to learn to see inward faces? If I looked at inward faces every day for weeks, then my priors would get updated right? I am also not 100% convinced that an inward face can be physically differentiated between an outward face all the time.

[@ArtemKirsanov]

Hmm, yeah, I think so! It's similar to those experiments where they make people wear glasses that turn everything upside down. At first you see everything as inverted, and navigation is super hard. But around a week in your perception "flips" and you start to see in those glasses as if everything is normal. So the brain has the ability to overcome priors like these with enough exposure

[@zekulir6419]

I remember seeing a few fun youtube videos about a "backwards" bike. The takeaway is always that you can get used to it even if it is oddly inoperable at first. Not the same but similar. The brain is fairly adaptive.

[@anywallsocket]

Friston’s work and Bayesian brain model are definitely onto something, but it’s not the whole story, somehow we still need to couple this with the critical state hypothesis and add more nuance.

[@ubertrashcat]

What do you mean that FEP doesn't explain everything? Haven't you seen all the paper that Friston produces? 😂 jk Obviously you're right. In reality many of his papers are very similar and many don't contain results, merely "considerations". There needs to be more people and more interest and RESULTS.

[@anywallsocket]

@@ubertrashcatI think that’s somewhat sensible tho, because the FEP is there to explain ‘what makes it go’ not ‘how it goes’ - the meat of his papers are trying to explain the Baysian brain hypothesis in terms of physics for this reason. I will say his Markov blankets are a new addition tho, well worth taking seriously.

[@CuriousDNB]

Im doing this for my thesis hahaha

[@ubertrashcat]

@@CuriousDNB Jealous!

[@ickkckmagma3192]

I read an argument against Chomsky view of language, it say that also exists some class of stadistical learning, proven in infants by how they detect patron tendency in syllables way before they have sentence of the language. Is that something related to that?

[@wex2808]

what a good video, subscribed

[@johnnada9196]

Really appreciate your work! I love it!

[@jhonyandrade4970]

Wow, thank you man.

[@factoral2645]

Awesome video! Looking forward to see the math

[@ЧайныйВетер]

14:02 and this stuff related to those brain parts that have deal with anxiety, is it?

[@IdPreferNot1]

There is a theory that LLMs organize their learning in a physical form to minimize "free energy", in this case the instability as predicted by Deitriech's principal. This is an actual physical phenomenon... like potential energy. I wonder if the free energy here has a physical representation?

[@bogdantataru843]

Excellent work! Thank you!

[@1three7]

Imagine encountering someone late at night on a dark sidewalk. Their face seems off but you can't put your finger on why. Then as they pass and you see from the side that their face is concave.

[@NoPodcastsHere]

I think it would have been cool to talk about 'performance magic' and sleight of hand in general as exploiting this phenomena

[@elefantsnablar]

Super well explained!

[@ArtemKirsanov]

Thank you!

[@MrGustavier]

I'm a bit surprise your talk about "free energy" but almost nothing about bayesian inferences and bayesian networks. If I understood well, the definition of "free energy" is basically the difference between the likelihood and the priors (in bayesian jargon). But this is not what is used in bayesian inferences, since the bayesian inference would be the product of the likelihood and the priors... Why the difference then ?

[@hyperduality2838]

Making predictions is a syntropic process -- teleological. Cause is dual to effect -- causality. Information is dual. Syntropy (knowledge, prediction) is dual to increasing entropy (lack of knowledge) -- the 4th law of thermodynamics! Concept are dual to percepts -- the mind duality of Immanuel Kant. "Always two there are" -- Yoda.

[@ArtemKirsanov]

Yes, you're correct! It's just that aimed to keep the video on the conceptual level and emphasize the biological perspective without diving into the formalism just yet. But this is one way to think about Bayesian Inference, so there is no difference really :)

[@MrGustavier]

@@ArtemKirsanov One way to think about bayesian inference is to subtract the priors to the likelihood ... ? I hope that will the topic of a future video !

[@hyperduality2838]

@@ArtemKirsanov "The brain is a prediction machine" -- Karl Friston, neuroscientist. Making predictions to track targets, goals and objectives is a syntropic process -- teleological. "Through imagination and reason we turn experience into foresight (prediction)" -- Spinoza describing syntropy. Probability amplitudes (waves, Bosons) are becoming probability densities (particles, Fermions) -- the Born rule in physics. Alternating currents are dual to direct currents -- rectification or diodes. Your brain is rectifying probability waves into information (signals) -- AC is dual to DC. The brain acts like a giant diode as it is rectifying information -- signals are dual to noise.

[@VISHWAp.s-w8v]

thank you , you helped me a lot

[@tmas]

Damn didn’t know the human brain ran on dlss frame generation

[@MichaelParrish-kk3ys]

so complexity = expected energy and accuracy = entropy? Just trying to understand how your simplification relates to the math. Thanks!

[@kaiko2020]

Hi! I'm a physics student interested in computational neuroscience. What starting resources do you recommend for the topic?

[@zerotwo7319]

Learn that there is two neuroscience. One is actual models that resemble neurons, like spiking networks The other is marketing for statistical models that fit lines to the data. That's why they are called 'AI models' and they don't have anything to do with natural neurons. Then you can decide if you will learn statistics to understand modern AI, or if you want real biological inspired models.

[@jakokaiser1169]

Extremely interesting concept and good explanation. Well done :) Since you sparked my interest: is there evidence for this theory yet?

[@ArtemKirsanov]

Thanks! It's notoriously hard to experimentally verify, but there is some recent progress. For instance see www.nature.com/articles/s41467-023-40141-z

[@jakokaiser1169]

@ArtemKirsanov thank you :)

[@stormoffists]

Are there physical predictions/evidence for this besides just that multi-layer hierarchical networks exist? Guess I need to read the papers myself.

[@DistortedV12]

Being an ML researcher, this video is funny because learn about most concepts in intro to ml 101 & this is probably where they all come from, but would've never known. 7:17 just looks like a CNN to me, 13:58, yeah that's a VAE... etc. etc.

[@ArtemKirsanov]

Exactly! There are a lot of parallels, as a good chunk of these models were inspired by neuroscience observations

[@hayesbrenner8095]

While the Free Energy Principle is compelling, there are some very interesting alternative theories as to how Perception works, specifically the Ecological Psychology principles of J. Gibson. Instead of saying that the brain works like a 'prediction machine', ecological theories of perception (and, by extension, action) posit that in order to understand behavior, one must consider the role of an organism within the environment as an interconnected dynamical system. If one posits that the brain is a 'prediction machine', it creates a whole host of problems, as being able to predict the outside world becomes computationally impossible when considering all the various variables and factors. One can still use principles of physics when thinking of an organism as an embedded system. I still think the video was well done, just wanted to provide an alternative perspective.

[@DistortedV12]

Thanks for sharing. I’m interested in other hypotheses as well because some claim that this principle is unfalsifiable.

[@nicbarth3838]

Maybe it may not be a problem if prediction occurs about and from an organism's prior state or inputs. maybe

[@JohnkingDoiner]

nice explanation of optical illusion

[@Entropy67]

Great video

[@tedkottke]

Does anyone have examples of testable hypotheses based on this theory?

[@r.s.e.9846]

Looks like a folded variational autoencoder structured hierarchically.

[@Pedritox0953]

Great video! Peace out

[@mubashshiruddin3567]

amazing video 🤓

[@fabriziobrown4454]

I feel this is more an AI video than a brain video... In 1800s people might have said the brain is like a loom, one of the most complex machines at the time 1850s a steam engine 1900s a mechanical watch 1990s a computer 2020s an AI model...

[@MtsBrg-ob6gf]

Thought about that too. Our understanding of the brain (and body for some time) seems always to be the most advanced technology. But I would say that today’s explanation has a slight advantage. The idea behind the AI-stuff is actually is inspired by actual biology hence the first mathematical iteration was called neural networks. On the other hand: that’s what people would have said in the olden days…

[@diadetediotedio6918]

@@MtsBrg-ob6gf You can be "inspired" in many ways by anything, unfortunately this don't imply a real connection

[@fabriziobrown4454]

​@MtsBrg-ob6gf I agree with both of you. I'm pretty sure the brain works with networks of neurons, but there are other speculative ideas that for example are based on the resonance of the signal transmitted, and probably many other, reducing the brain to just a network is a claim I am not sure all neuroscientists would feel doing. There's also alot of chemistry influencing the signals in the brain... But I guess I am also just speculating

[@MtsBrg-ob6gf]

@ True

[@danielmoralestorres7884]

Teoría universal del funcionamiento del cerebro Tema central El video explora la teoría del principio de la energía libre en neurociencia, que propone que el cerebro no recibe información pasivamente, sino que genera activamente predicciones sobre el mundo y utiliza la información sensorial para verificar esas predicciones. Subtemas La ilusión de la máscara: Se presenta la ilusión de la máscara como un ejemplo de cómo el cerebro se aferra a sus predicciones, incluso cuando son incorrectas. El cerebro como máquina de predicción: Se introduce la idea central de que el cerebro no es un receptor pasivo, sino que genera activamente predicciones sobre el mundo. El principio de la energía libre: Se explica que el cerebro busca minimizar la "energía libre", que es la tensión entre lo que los sentidos perciben y lo que el cerebro cree que debería ser. Evolución de los cerebros: Se describe cómo los cerebros evolucionaron no solo como máquinas de reacción, sino como constructores de modelos sofisticados que intentan explicar las entradas sensoriales infiriendo sus causas ocultas. Modelos internos del mundo: El cerebro tiene un modelo interno de cómo funciona el mundo y lo que es probable que ocurra, y utiliza este modelo para interpretar la información sensorial. El cerebro como un juez: Se compara el cerebro con un juez que sopesa la evidencia sensorial frente a las creencias previas para llegar a una interpretación de la realidad. Neuronas latentes y causas ocultas: Se explica cómo las neuronas latentes representan características o causas abstractas a diferentes niveles de abstracción, lo que permite al cerebro comprimir la información sensorial en una forma manejable. Modelo generativo y prioris: El cerebro utiliza un modelo generativo que puede sintetizar datos sensoriales para una causa dada, y prioris, que son las probabilidades aprendidas de diferentes causas, para dar sentido a situaciones ambiguas. Inferencia de causas a partir de observaciones: Se describe el desafío computacional de la inferencia, que es el proceso de averiguar qué causó una entrada sensorial, y cómo el cerebro utiliza una red de reconocimiento para hacer una aproximación de las causas probables. Redes de reconocimiento y generativas: El cerebro utiliza una red de reconocimiento para proponer posibles explicaciones y una red generativa para comprobar su funcionamiento, en un proceso de retroalimentación hasta encontrar una explicación que minimice la energía libre. Percepción y aprendizaje: La percepción es el proceso rápido de ajustar la actividad de las neuronas latentes para encontrar una explicación que minimice la energía libre, mientras que el aprendizaje es un proceso a largo plazo de refinar los modelos de reconocimiento y generativo. Explicación de la ilusión de la máscara: Se explica la ilusión de la máscara a través del principio de la energía libre, donde el cerebro prefiere la explicación de una cara convexa con iluminación inusual a la de una cara cóncava, ya que la creencia previa de que las caras son convexas es muy fuerte. Resumen del principio de la energía libre: El cerebro es una máquina de predicción que utiliza un modelo generativo y una red de reconocimiento para equilibrar la evidencia observada con las creencias previas, minimizando la energía libre para encontrar explicaciones que se ajusten a los datos sensoriales y al modelo del mundo actual. Profundización en el futuro: Se anuncia que en futuros videos se explorará la base matemática del principio de la energía libre y su conexión con el aprendizaje automático. Advertencias sobre altos riesgos potenciales El video es principalmente conceptual y no profundiza en la base matemática del principio de la energía libre, lo que podría ser una limitación para quienes buscan una comprensión más profunda. La analogía del cerebro como un "juez" es una simplificación y no debe tomarse como una descripción literal de los procesos neuronales. El video no aborda las posibles limitaciones o críticas a la teoría del principio de la energía libre, lo que puede llevar a una comprensión incompleta del tema. No se presentan escenarios o alternativas plausibles en los que la teoría de la energía libre no sea suficiente para explicar la actividad neuronal, lo que implica que es una teoría completa y sin puntos débiles, algo poco probable en cualquier teoría científica. El video no aborda de forma explícita la posible manipulación o los problemas de control que pueden surgir del entendimiento profundo del funcionamiento del cerebro, lo que puede constituir un riesgo en manos equivocadas.

[@mediali1]

¡Gracias!

[@jurian0101]

"Melancholy creature. / Paranoid secret. / Hypothetic victim of prediction." ~~ Lyrics from Sufjan Stevens' "Saturn" (It is referring to the myth's patricidal prophecy.) Either it fit nicely or it's just my brain doing bogus inference aka pareidolic pattern matching. But I won't be surprised if the Free Energy Principle turned out to have been used in the Variational Autoencoder architecture. :D Then like Prof. Jeff Hinton jokingly said in his Nobel lecture: Were the architecture really how the brain works, they get a Nobel in Physiology, or else get it in Physics.

[@aaronkriegman]

How is this free energy related to the physical free energy? Because it seems pretty clear to me that the brain does not minimize physical free energy. At least, other better understood learning systems use a controlled increase in entropy in their learning process (mutation, VDJ recombination, random weight initialization and dropout, etc), and it seems likely to me that this increase in entropy is unavoidable in any physical system that learns.

[@fujiclimber]

Amazing video! ty

[@approaching404]

17:13 will look at the face while high on lsd to see if my eyes are fooled

[@ubertrashcat]

If only it wasn't so obscured by "fristonian". This field is suspiciously monopolar. I'm looking forward to the book by Sanjeev Namjoshi, his interview at MLST was exceptional.

[@monishrules6580]

Could you elaborate

[@ubertrashcat]

@monishrules6580 I think it's still early days for this theory and as long as Friston is the one who's driving it it's going to remain obscure. His papers are very tough reads. The book is a step in the right direction but there needs to be more actionable material and real examples. I appreciate that it's very hard to take something like a new PRINCIPLE off the ground because it's so hard to prove that it is actually conserved. At the same time the state of affairs is that it's so universal, yet there are few concrete results, that it raises the question of scientific validity. If a theory explains almost everything (especially post hoc), that's a case for caution. I'm personally very in favor of FEP and it could be like Darwin's theory indeed but there needs to be more people and diverse approaches to try to chew on this thing.

[@IUT-e8x]

​@@ubertrashcat Very weird claim papers are mostly tough to read to classical neuroscientist, people who read his work are mostly either computational neuroscientists or people who work in ML, if you mean only read by specific communities when you say "obscure" I get it but also true for almost every academic field otherwise, weird claim.

[@ubertrashcat]

@@IUT-e8x I'm an ML engineer and I study CogSci, I find the papers by Friston extremely hard to follow and understand. Especially his 2010 Nature paper. I have a background in variational inference and I had a really hard time. Maybe it's a me problem. I read a lot of ML and neuroscientific literature but I need 2 or 3 takes for Friston. Maybe he's a genius, but how would I know?

[@cristianfalan6320]

@@ubertrashcat He could very well be (or not, to fully state the obvious). I really appreciate the honesty and coming forward with this since I'm from a (clinical) psychology background with a significant interest in research and, concerning neuroscience, striving for at least a minimum understanding of brain function and I found Friston's papers really inaccessible even thought I can somewhat navigate some of the neuroscience literature and I have a conceptual understanding of predictive coding with practical applications in psychology. I certainly thought it was exclusively a me problem, but I feel much better now

[@Hossheavy]

Are there any good novice level syntheses of this?

[@jwhi419]

The math? 3blue1brown or so has videos explaining the basic math of... Well relevant topic. But it is all math. Which translates to computation in this video

[@jwhi419]

Of course this channel itself should have knowledge to form a foundation.

[@SirLightfire]

Is this why the lack of stimulation drives us insane?

[@MtsBrg-ob6gf]

Can‘t say anything about Lack of stimulation drives us insane. But Friston argues that delusions, active schizophrenic episodes stem from to heavily weighted priors (hence I see or hear things that arenˋt really there) and autism might be a condition where the sensory inputs are wheighted to much which is way it´s hard to put things in abstract categories like emotion, context and so on

[@DistortedV12]

Where do our priors come from doe? what are "cognitive mechanics of priors and prior-forcing?" and some say this principle is unfalsifiable is tht true?

[@hyperduality2838]

Making predictions is a syntropic process -- teleological. Cause is dual to effect -- causality. Information is dual. Syntropy (knowledge, prediction) is dual to increasing entropy (lack of knowledge) -- the 4th law of thermodynamics! Concept are dual to percepts -- the mind duality of Immanuel Kant. "Always two there are" -- Yoda.

[@chrisracer2007]

I wish that was what they taught us at school

[@4thpdespanolo]

i ❤️ Bayesian inference

[@TonyIceCream]

is this why when in complete dark you still seem to "see" the outline of your room, even tho when you turn on the light it's all misaligned and wrong?

[@williamwilkinson2748]

Excellent.

[@zeroonetime]

Yes, our brains brain are no other Creation ITSELF in Action. 010

[@DimasPangestu-dx7hn]

If perception is a result of probabilistic predictions, does this mean there’s no objective reality, only subjective interpretations influenced by personal history?

[@tim40gabby25]

Interesting question, but we can broadly agree on mice or elephants, though both are grey 4 legged animals

[@falalala83]

Awesome video. Hot take: I think that a lot of people tend to neglect the role of the brain in stereotyping and generalizing other people. It always happens in our brains no matter what, no matter how wrong it may be. They tend to see generalizations as shallow and immoral conscious decisions. Ultimately, is up to us to choose to not react to the signals our brain sends us when dealing with individuals in front of us, rather than shame ourselves for thinking those things in the first place. Can't wait to see where the neuroscience field goes in general especially with BCIs. Thanks for sharing!

[@baleoconnell9216]

How certain are neuroscientists that the free energy minimise model actually matches how the brain works? What evidence could prove this model wrong?