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The brain ‘rotates’ memories to save them from new sensations
- lupire 6y agoAbstract is mostly readable to a technically person: https://www.nature.com/articles/s41593-021-00821-9 https://www.nature.com/articles/s41593-021-00821-9
- ThePowerOfDirge 6y agoI am technically person.
- alexanderdmitri 6y agoYou are technically readable words of a mostly abstract person.
- ohazi 6y agoI don't remember where I came across this (was probably some pop neuroscience blog or maybe radiolab), but there was some theory about how memories seem subject to degredaton when you recall them a lot, and less so when you don't. I guess that would sort of be like the opposite of DRAM - cells maintain state when undisturbed, but the "refresh" operation is lossy.
- tshaddox 6y agoI would expect memories to change more the more they are recalled, just like I would expect a story to change the more times it’s told.
- Phenomenit 6y agoYeah I'm thinking that's because our interpretation of reality and it's abstractions är falsy and that filter is applied every time we update the memory. Maybe then when we are learning a new subject through say reading our filter is minimal and every time we read the same info we combat our falsy interpretation of reality.
- ohazi 6y agoYes, maybe change is a better term than degrade. The story was told in terms of the details in a memory changing a lot vs. remaining accurate.
- gus_massa 6y agoPerhaps the Crick and Mitchison theory about why we dream: https://en.wikipedia.org/wiki/Reverse_learning https://en.wikipedia.org/wiki/Reverse_learning (AFAIK it's totally wrong, but I really like it anyway. I hope there is another specie in the universe that use it.)
- plg 6y agoit's the theory of re-consolidation here are some references https://pubmed.ncbi.nlm.nih.gov/?term=memory+reconsolidation+AND+review+AND+nader%5Bau%5D&sort=date&size=20 https://pubmed.ncbi.nlm.nih.gov/?term=memory+reconsolidation...
- ajuc 6y ago> I guess that would sort of be like the opposite of DRAM - cells maintain state when undisturbed, but the "refresh" operation is lossy. Or like any analog data medium ever :)
- sebmellen 6y agoHow fascinating, I've experienced this myself to a large degree. I have a few songs that very vividly remind me of certain periods or points of my life. When I play them, I always feel like I'm scratching up the vinyl surface of the memory, and I lose a little bit each time. Rather disappointing :(
- deleted 6y ago[deleted]
- mncharity 6y agoI'm under the anecdotal and subjective impression that I can do a "brain dump" describing a recently-experienced physical event. But it's a one-shot exercise. Close to read-once recall. The archived magnetic 9-track tape that when read becomes a take-up reel of backing and a pile of rust. The memories feel like they're degrading as recalled, like beach sand eroding under foot, and becoming "synthetic", made up. The dump is extremely sparse and patchy. Like a limits-of-perception vision experiment: "I have moderate confidence that I saw a flash towards upper left". Not "I went through the door and down the hall" but "low-confidence of a push with right shoulder, medium-confidence passing a paper curled out from the wall at waist height, and ... that's all I've got". But what shape curl? Where in the hall? You've whatever detail was available around the moment you recalled it, because moments later extra information recalled start tasting different, speculative fill-in-the-blanks untrustworthy.
- drivers99 6y agoThat sounds like the kind of thing they talk about on Hidden Brain (NPR). I think I found it: https://www.npr.org/transcripts/788422090 https://www.npr.org/transcripts/788422090 Quote (although it’s missing context if the full show): > Yeah, I think it's really interesting. I think it's really interesting to think about why we do these things, why we misrecollect our past, how those kinds of reconstruction errors occur. And I think about it in my own personal life - I share my memories with my partner. And many of us who have partners, we have these sort of collaborative ways in which we recollect. But those collaborations often result in my incorporating information into my memories that were suggested by this individual, but I never experienced. And so I might have this vivid recollection of something that only my partner experienced because we've shared that information so often. And so that's how we can distort memories in the laboratory. We can just get individuals to try and reconstruct events over and over and over again. And with each reconstructive process, they become more and more confident that that event has occurred.
- deleted 6y ago[deleted]
- User23 6y agoIn mice.
- Jaecen 6y agoThe experiment was on mice, but the process has been observed elsewhere. From the article: > This use of orthogonal coding to separate and protect information in the brain has been seen before. For instance, when monkeys are preparing to move, neural activity in their motor cortex represents the potential movement but does so orthogonally to avoid interfering with signals driving actual commands to the muscles.
- wizzwizz4 6y ago> The work could help reconcile two sides of an ongoing debate about whether short-term memories are maintained through constant, persistent representations or through dynamic neural codes that change over time. Instead of coming down on one side or the other, “our results show that basically they were both right,” Buschman said, with stable neurons achieving the former and switching neurons the latter. The combination of processes is useful because “it actually helps with preventing interference and doing this orthogonal rotation.” This sounds like the early conservation of momentum / conservation of energy debates. (Not that they used those words back then.)
- trott 6y agoSomething to keep in mind though is that in a high-dimensional space, approximate orthogonality of independent vectors is almost guaranteed.
- filoeleven 6y agoCan you say a bit more on what that means in this context?
- FigmentEngine 6y agoprobably a reference to the curse of dimensionality
- fighterpilot 6y agoNot sure about the neuroscience context, but if you have two large ("high-dimensional") vectors of variables that have a population correlation of zero ("independent"), then the dot product of a sample is likely to be close to zero ("orthogonal") due to the law of large numbers.
- adampk 6y agoDo you mean to say that the neurons in the brain are operating in a higher-dimensional space than 3?
- frisco 6y agoYes definitely. Here the "space" doesn't refer to physical space, but an abstract vector space that neuron's tuning represents. For example, there is a famous paper[1] that showed neurons could be responsive to abstract concepts -- for example, one might fire for "Bill Clinton" regardless of whether the stimulus is a photo of him, his name written as letters, or even (with weaker activation) photos/text of other members of his family or other concepts adjacent to him. The neuron's activity gives a vector in this high dimensional concept space, and that's the "space" GP is referring to. [1] https://www.nature.com/articles/nature03687 https://www.nature.com/articles/nature03687
- ordu 6y agoCurious. I cannot understand it clearly. Lets take for example "my wife and my mother-in-law" illusion[1]. It is known for it's property that one cannot see both women at once. If we assume that it has something to do with such a coding in neurons, would it mean that those women are orthogonal, or it would mean that they refuse to go orthogonal? [1] https://brainycounty.com/young-or-old-woman https://brainycounty.com/young-or-old-woman
- chaps 6y agoHmmm.. I tried to visualize them both at the same time.. it took some effort, but quickly "oscillating" between the two ended up settling (without a jittery oscillating feeling) on seeing both at the same time. Maybe my brain was playing meta tricks on me though?
- c22 6y agoI can "see" both at the same time, but only if I am not focusing on either. I think this conflict of focus is the real effect people are talking about.
- Baeocystin 6y agoReally? I have no trouble seeing both at the same time. Nothing special about it, the angles of their respective faces are different enough that it doesn't feel like there's any interference at all.
- behnamoh 6y agoArticles on Quanta magazine have clickbait titles.
- chalst 6y agoAnd yet this title seems to capture the content quite adequately.
- lukeplato 6y agoThere was another recent article on applications of geometry to analyse neural mechanisms to encode context. It also mentioned a rotation/coiling geometry: https://www.simonsfoundation.org/2021/04/07/geometrical-thinking-offers-a-window-into-computation/ https://www.simonsfoundation.org/2021/04/07/geometrical-thin...
- deleted 6y ago[deleted]
- ivan_ah 6y agoThe Nature version is paywalled https://www.nature.com/articles/s41593-021-00821-9 https://www.nature.com/articles/s41593-021-00821-9 but I found the preprint of the paper on biorxiv.org: https://www.biorxiv.org/content/10.1101/641159v1.full https://www.biorxiv.org/content/10.1101/641159v1.full
- tgflynn 6y agoThe abstracts are a bit different so I'm not sure how close the preprint is to the published version.
- ivan_ah 6y agoNever mind the bioxiv link; the Nature article is no longer paywalled, see https://www.nature.com/articles/s41593-021-00821-9.pdf https://www.nature.com/articles/s41593-021-00821-9.pdf
- DoctorOetker 6y agothat's still paywalled from here in Belgium
- andyxor 6y agolooks similar to "Near-optimal rotation of colour space by zebrafish cones in vivo" https://www.biorxiv.org/content/10.1101/2020.10.26.356089v1 https://www.biorxiv.org/content/10.1101/2020.10.26.356089v1 "Our findings reveal that the specific spectral tunings of the four cone types near optimally rotate the encoding of natural daylight in a principal component analysis (PCA)-like manner to yield one primary achromatic axis, two colour-opponent axes as well as a secondary UV-achromatic axis for prey capture."
- fighterpilot 6y agoI read the abstract and don't really get it. How is this different from saying that a group of neurons A is responsible for memory storage and a group of neurons B is responsible for sensory processing, and A != B? I think I'm misunderstanding this "rotation" concept.
- rkp8000 6y agoIt's a good question. It looks like they actually specifically check for this and show that it's not two separate groups of neurons. Instead a subset of the neural population changes their representation of the input as it moves from sensory to memory, so it's more like a single group of neurons that represents current sensory and past memory information in two orthogonal directions.
- o_p 6y agoSo memory and sensory get multiplexed?
- fighterpilot 6y agoSo current sensory info is a vector of numbers, and past memory info is a vector of numbers, and these two vectors are orthogonal. What are these numbers, precisely?
- rkp8000 6y agoMy simplified picture of what’s going on is something like this (if I’m understanding the paper correctly). Stimulus A starts out represented by the vector (1,1,1,1) and B by (-1,-1,-1,-1). Those are the sensory representations. Later A is represented by (1,1,-1,-1) and B by (-1,-1,1,1). Those are the memory representations. The last two component/neurons have “switched” their selectivity and rotated the encoding. The directions (1,1,1,1) and (1,1,-1,-1) are orthogonal, so you can store sensory info (A vs B in the present) along one and memory info (A vs B in the past) aling the other.
- resonantjacket5 6y agoIn a simple example that I can think of it could just be a vector of <present, past> aka the current info could be encoded like [<2, 0>, <4, 0>] then rotated to ("y axis") [<0, 2>, <0, 4>] allowing you to write more "present" data to the original x dimension without overriding the past data. If you're asking about the exact numbers here's a snippet from the xlsx document. ``` ABCD_mean ABCD_se ABCD_mean ABCD_se XYCD_mean XYCD_se XYCD_mean XYCD_se day neuron subject time 0 6.012574653 0.5990308106 6.181361381 0.5737310366 6.59759636 0.6419092978 6.795648346 0.5716884524 1 2 M496 -50 ``` According to the article SEM neural activity, though this is way beyond my ability to interpret.
- de6u99er 6y agoThis makes much more sense than having secret memory cells in neurons.
- darwingr 6y agoThis really would have been harder for me to understand had I not taken linear and abstract algebra courses a few years ago. That area of maths reused common words like "rotation" but with more generalized definitions, which made it was jarring and confusing to hear and take in at the time. When someone said the word "rotate" my mind as if by reflex was already trying visualize a 3d or 2d rotation even when it made no sense for the problem at hand. Being an English speaker my whole life I thought I understood what a rotation was or could be but I didn't. Same goes for what's being alleged here: Is there even a way to visualize this that makes mathematical sense? What will be the corollaries to this discovery simply as a result of what the mathematics of rotations will dictate?
- dboreham 6y agoSame goes for the ordinary English word "Eigenvector".
- danwills 6y agoReminds me of how orthogonally polarized waves can inhabit the same bit of space without interfering with each other (and can be cleanly separated later using 2 polarized filters at 90 degrees).
- cephalicmarble 6y agoRequires a fair bit of detergent to clear up all the crumbs anyway: might varying grain sizes help any?
- jakear 6y ago> And yet those memories can’t be allowed to intrude on our perception of the present, or to be randomly rewritten by new experiences. Assumes facts not in evidence? I feel it’s incredibly common for memories to intrude on perception of present, and be rewritten by new experiences.
- lanstin 6y agoMaybe that is the origin of consciousness - when the processing becomes high level enough that the interference of past into present becomes sophisticated/multilayered enough to be useful.
- meiji163 6y agoCan someone liberate the article from behind the paywall for me?
- ddmma 6y agoIt’s this blockchain?
- throwaway189262 6y ago> They had the animals passively listen to sequences of four chords over and over again, in what Buschman dubbed “the worst concert ever.” Hahahaha!
- deleted 6y ago[deleted]
- iandanforth 6y agoTake a binary array of length N, where N is in the hundreds to thousands range. Choose 2% of the bits to set to 1. Now you have a "sparse array". Now, you want to use this sparse array to represent a note in a song. So you need every note to consistently map to a distinct* sparse array. However, you also want to be able distinguish a note as being in one song or another. The representation should tell you not only that this is note A but note A in song X. How might you do that? Well some portion of the ON bits could be held consistent for every A note and some could be used to represent specific contexts. Stable and variable bits of you will. Now if you look at two representations of the note A from two songs you'll see they're different. How different are they? Well you could just count the bits they have in common or not, or you can treat them as vectors. (Lines in high dimensional space) Then you can calculate the angle between those two lines. As that angle increases its easier to distinguish the two lines. They won't ever get to full "right angles" between them because of the shared stable bits, but they can be more or less orthogonal. That's what's happening here. The brain is encoding notes in a way that it can both recognize A, but also recall it in different contexts. *But not perfectly consistent, we use sparse representations because the brain is noisy and it's more energy efficient. Pretty close is good enough in the brain and you can encode a lot of values in 1000 choose 20 options.
- mmastrac 6y agoSo we are just walking Lucene indexes?
- lamby 6y agoIt would make an interesting essay or monograph to descrbibe the various similes and metaphors that people have variously used to describe the working of the human mind. Descartes, I believe, thought the brain worked via a series of hydraulics, and the Victorians thought that it worked by some variant of electricity, both of these being "new" and possibly exciting technologies for their times. Notwithstanding the scientific versimillitude of these previous theories, it seems only fitting that computers occupy that role today.
- screye 6y agoThis maps wonderfully onto SVD, Neural networks and embeddings. Word embeddings frequently encode particular traits in different 'regions' of a 256(ish) dimensional space. AFAIK, It is also why we think of element wise addition (merging) in neural networks as an efficient and relatively loss-less computation. The aggregation after attention step used in Transformers (GPT-3) fundamentally relies on this being true. Although from my reading, there is an inherent assumption of sparsity in such situations. So, is it reasonable to assume that human neurons are also relatively sparse in how information is stored ?
- bernardand 6y agoThis is basically just linear algebra.
- ChoGGi 6y agoSqueak squeak
- program_whiz 6y agoSimple explanation if anyone needs it. The problem is suppose you have 4 neurons that need to understand a memory and present experience at the same time to make a decision (for example that you see a hot stove and memory that hot stoves hurt). The incoming neurons from memory and experience each have 4 neuron connections, which fire at some rate. Lets represent this as the firing rate of the neurons per second in a 4d vector: experience: <1.0, 0, 0, 0> (1 pulse per sec on axis 0) memory: <1.0, 0, 0, 0> (1 pulse per sec on axis 0) If you "add" these together at the downstream neurons, you won't be able to tell which was a memory and which was sensation. A simplified explanation of how neurons work is by combining voltages from their incoming neurons. Example: downstream sees: <2.0, 0, 0, 0> upstream could be a memory with <1.0, ...> and experience <1.0, ...>, or memory <2.0, ...> experience <0.0, ....>, or memory <0.5, ....> and experience <1.5, ....>. There are many possible vectors that could "add" to produce the downstream effect, so it makes it harder for those neurons to "learn" the pattern. As a math equivalence, if I ask "what two numbers sum to 10", there are many solutions (its impossible to disentangle the original numbers). To make it easier to learn these patterns, what if we used only separate elements of the incoming vectors to represent this information (so the elements of memory and experience could be seperated)? So some intermediate neurons can transform the representation. We can constructor orthogonal vectors (since the vectors above are sparse): experience: <1.0, 0, 0, 0> memory: <1.0, 0, 0, 0> => <0, 1.0, 0, 0> experience + memory: <1.0, 1.0, 0, 0> The "memory" must undergo a "rotation" which moves data into an "unused" portion that won't conflict with the experience neuron firing pattern. Now downstream neurons can use the data from each (its effectively merging memory and experience without confusing the signal). There is only a single memory and a single experience that combined will give the firing pattern, so the pattern can be learned. Due to the way linear algebra works, its possible to do this with more complex numbers along arbitrary axes in an n-dimensional space (instead of doing it with a single axis/neuron and all others being zero). For a physical corollary, imagine two images super-imposed on each other. If they are very distinct, you might be able to infer what the two source images were, but if they are similar it would be difficult. Now imagine a "lenticular" image that clearly displays two images by printing them at orthogonal angles on the medium. You can easily determine what content belongs to which image, but only having a single "print" to store the data (this isn't a perfect anology, but it illustrates the idea): https://images.app.goo.gl/3dCH7Txigh1adTd66 https://images.app.goo.gl/3dCH7Txigh1adTd66