[HN Gopher] The brain 'rotates' memories to save them from new s...
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The brain 'rotates' memories to save them from new sensations
Author : jnord
Score : 188 points
Date : 2021-04-16 06:04 UTC (1 days ago)
(HTM) web link (www.quantamagazine.org)
(TXT) w3m dump (www.quantamagazine.org)
| wizzwizz4 wrote:
| > _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.)
| lupire wrote:
| Abstract is mostly readable to a technically person:
|
| https://www.nature.com/articles/s41593-021-00821-9
| ThePowerOfDirge wrote:
| I am technically person.
| trott wrote:
| Something to keep in mind though is that in a high-dimensional
| space, approximate orthogonality of independent vectors is almost
| guaranteed.
| filoeleven wrote:
| Can you say a bit more on what that means in this context?
| FigmentEngine wrote:
| probably a reference to the curse of dimensionality
| fighterpilot wrote:
| Not 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 wrote:
| Do you mean to say that the neurons in the brain are operating
| in a higher-dimensional space than 3?
| frisco wrote:
| Yes 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
| mapt wrote:
| Wouldn't it be especially inelegant/inefficient to try and
| wire synapses for, say, a seven-dimensional cross-
| referencing system, when have to actually physically locate
| the synapses for this system in three-dimensional space?
|
| (and when the neocortex that does most of the processing
| with this data is actually closer to a very thin, almost
| two-dimensional manifold wrapped around the sulci)
|
| There has to be an information-theory connection between
| the physical form and the dimensionality of the memory
| lookup, even if they aren't referring to precisely the same
| thing, right?
| PullJosh wrote:
| Can I get an ELI5 on how physical neurons, stuck in a
| measly 3 dimensions, can possibly form higher-dimensional
| connections on a large scale?
|
| I understand higher dimensional connections in theory (such
| as in an abstract representation of neurons within a
| computer), but I can't imagine how more highly-connected
| neurons could all physically fit together in meat space.
| dboreham wrote:
| Same as a silicon chip stuck in 2 dimensions can.
| ajuc wrote:
| > Can I get an ELI5 on how physical neurons, stuck in a
| measly 3 dimensions, can possibly form higher-dimensional
| connections on a large scale?
|
| You can multiplex in frequency and time. I'm not sure if
| neurons do it, but it's certainly possible with computer
| networks.
| wyager wrote:
| Your stick of RAM is also stuck in 3 dimensions but it
| reifies a, say, 32-billion-dimensional vector over Z/2Z.
| CuriouslyC wrote:
| If you take a matrix of covariance or similarity between
| neurons based on firing pattern, and try to reduce it to
| the sum of a weighted set of vectors, the number of
| vectors you would need to accurately model the system
| gives you the dimensionality of the space.
| fao_ wrote:
| This does not seem particularly like an "Explain Like I'm
| 5"-parsable comment that the posted asked for.
| dopu wrote:
| If I'm recording from N neurons, I'm recording from an
| N-dimensional system. Each neuron's firing rate is an
| axis in this space. If each neuron is maximally
| uncorrelated from all other neurons, the system will be
| maximally high dimensional. Its dimensionality will be N.
| Geometrically, you can think of the state vector of the
| system (where again, each element is the firing rate of
| one neuron) as eventually visiting every part of this
| N-dimensional space. Interestingly, however, neural
| activity actually tends to be fairly low dimensional (3,
| 4, 5 dimensional) across most experiments we've recorded
| from. This is because neurons tend to be highly
| correlated with each other. So the state vector of neural
| activity doesn't actually visit every point in this high
| dimensional space. It tends to stay in a low dimensional
| space, or on a "manifold" within the N-dimensional space.
| cochne wrote:
| Consider three neurons all connected together. Now
| consider that each of them may have some 'voltage'
| anywhere between 0 and 1. Using three neurons you could
| describe boxes of different shapes in three dimensions.
| Add more and you get whatever large dimension you want.
| fsociety wrote:
| Think of it less as n-dimensional in meat space and more
| of n-dimensional in how it functions.
| [deleted]
| exporectomy wrote:
| Do you mean due to the thickness of each connection, they
| would occupy too much space if the number of dimensions
| was too high? Not necessarily 4 or more, just very high
| because there are on the order of n^2 connections for n
| neurons?
|
| In the visual cortex, neurons are arranged in layers of
| 2D sheets, so that perhaps gives an extra dimension to
| fit connections between layers.
| andyxor wrote:
| see related talk by the first author: "Dynamic
| representations reduce interference in short-term
| memory": https://www.youtube.com/watch?v=uy7BUzcAenw
| MereInterest wrote:
| There was a fun article in early March showing that the
| same is true for image recognition deep neural networks.
| They were able to identify nodes that corresponded with
| "Spider-Man", whether shown as a sketch, a cosplayer, or
| text involving the word "spider".
|
| https://openai.com/blog/multimodal-neurons/
| andyxor wrote:
| deep neural nets are an extension of sparse autoencoders
| which perform nonlinear principal component analysis
| [0,1]
|
| There is evidence for sparse coding and PCA-like
| mechanisms in the brain, e.g. in visual and olfactory
| cortex [2,3,4,5]
|
| There is no evidence though for backprop or similar
| global error-correction as in DNN, instead biologically
| plausible mechanisms might operate via local updates as
| in [6,7] or similar to locality-sensitive hashing [8]
|
| [0] Sparse Autoencoder https://web.stanford.edu/class/cs2
| 94a/sparseAutoencoder.pdf
|
| [1] Eigenfaces https://en.wikipedia.org/wiki/Eigenface
|
| [2] Sparse Coding
| http://www.scholarpedia.org/article/Sparse_coding
|
| [3] Sparse coding with an overcomplete basis set: A
| strategy employed by V1?https://www.sciencedirect.com/sci
| ence/article/pii/S004269899...
|
| [4] Researchers discover the mathematical system used by
| the brain to organize visual objects
| https://medicalxpress.com/news/2020-06-mathematical-
| brain-vi...
|
| [5] Vision And Brain https://www.amazon.com/Vision-Brain-
| Perceive-World-Press/dp/...
|
| [6] Oja's rule https://en.wikipedia.org/wiki/Oja%27s_rule
|
| [7] Linear Hebbian learning and PCA
| http://www.rctn.org/bruno/psc128/PCA-hebb.pdf
|
| [8] A neural algorithm for a fundamental computing
| problem
| https://science.sciencemag.org/content/358/6364/793
| andyxor wrote:
| Yes, grid cells in the hippocampus [0] form a coordinate
| system that is used for 4D spatiotemporal navigation [1], as
| well as navigation in abstract high-dimensional "concept
| space" [2]
|
| [0] http://www.scholarpedia.org/article/Grid_cells
|
| [1] Time (and space) in the hippocampus
| https://pubmed.ncbi.nlm.nih.gov/28840180/
|
| [2] Organizing conceptual knowledge in humans with a gridlike
| code: https://science.sciencemag.org/content/352/6292/1464
| [deleted]
| darwingr wrote:
| Yes but only in aggregate, like how adding a column to a
| database table is also adding a "dimension" to said data.
|
| I'm not convinced the author's analogy of cross-writing to
| fit more information on a page is actually going to be
| helpful to most people's understanding. It led me at least to
| try to imagine visually what's going on, to picture the input
| being physically rotated. This is more akin to the more
| abstract but inclusive concept of rotation from linear
| algebra, where more dimensions (of information, not space or
| time) makes sense.
| gleenn wrote:
| If you think of groups of neurons in arbitrary dimensions,
| where some groups fire together for some things, and a
| different group with some overlap fire for other things, then
| it's like two dimensions where a line is a sense or thought
| and the lines are crossing where they fire for both memories.
| So two thoughts along two dimensions can cross and light up
| that subset of neurons. If the two thoughts, or lines, are
| orthogonal, then not many neurons are both firing for
| thoughts. If you have many many neurons, and many many
| memories, then the dimensionality, or possible subsets of
| firing neurons, is huge. Like our two lines but now in three
| dimensions, there are a lot of ways for them not to overlap.
| So the possibility that many things in that space are
| orthogonal is likely. In a highly dimensional space, a whole
| lot of things don't overlap.
| dopu wrote:
| Sure, but the neural activity is actually low-dimensional (see
| Extended Fig 5e). By day 4, the first two principal components
| of the neural activity explains 75% of the variance in
| response. ~3-4 dimensions is not particularly high dimensional.
| ivan_ah wrote:
| The Nature version is paywalled
| 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
| ordu wrote:
| Curious. 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
| bserge wrote:
| Sorry, I'm pretty tired, but I fail to see the relation to this
| article, how does that example apply?
|
| I thought that was more of a case of a human's facial
| recognition being a special function, and we're not able to
| process two or more people's faces at the same time. Like, see
| the details in them, recognize that it's _their face_.
|
| You're either looking at one person, or the other, but if you
| try to look at both of them at the same time, they become
| "blurry", unrecognizable, even though you remember all the
| other information about them both.
|
| But that's not related to memory integrity and new
| emotions/sensations?
| ordu wrote:
| It is a work of human visual perception at work. Somehow you
| mind chooses how to interpret sensations from a retina, and
| shows you one of women. Then you mind chooses to switch
| interpretations and you see the other one. Both
| interpretation are somewhere in memory. So it may be
| connected with this research.
|
| Like with those chords in a research. Mice hear one chord,
| and by association from memory it expects other chord. But
| instead it hears some third chord. Expected and unexpected
| chords have perpendicular representation, if I understood
| correctly.
|
| Here you see a picture, and expects one interpretation or
| other. You have memory of both, but you get just one.
|
| Possibly it doesn't apply, I do not know. I'm trying to
| understand it. The obvious step is to make a prediction from
| a theory, should interpretations oscillate, if it has
| something to do with perpendicularity of representation in
| neurons?
|
| When I hear another chord instead of a predicted one, do
| prediction and sensations oscillate? I'm not quick enough to
| judge based on a subjective experience.
| vmception wrote:
| Wish they would outline the two variants
|
| I only see the young woman before I became disinterested in
| making the other one happen because why
| LordGrey wrote:
| I spent 10 minutes staring at that picture and saw only the
| wife. The mother-in-law never appeared.
|
| This happens to me often.
| andrewmackrodt wrote:
| I had trouble at first too until I noticed the ear looking a
| little suspicious. If you create a diagonal obstruction from
| the top of the hat, to the nose, you are left will only the
| mother-in-law; the ear has now become an eye.
|
| Once I'd seen it once, the mother-in-law is now prominent. I
| can still see the wife if I concisely choose to, but the
| mother-in-law is now the default, strange huh?
| chaps wrote:
| Hmmm.. 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 wrote:
| I 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 wrote:
| Really? 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.
| bserge wrote:
| But do you really see both _at the same_ time or you just
| switch between them really fast?
| treeman79 wrote:
| Does it matter? My vision switches eyes every 30 seconds,
| unless I'm wearing prism glasses. I rarely notice unless
| I'm trying to write.
| Baeocystin wrote:
| At the exact same time. No oscillating.
| andyxor wrote:
| looks similar to "Near-optimal rotation of colour space by
| zebrafish cones in vivo"
|
| 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 wrote:
| I 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 wrote:
| It'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.
| fighterpilot wrote:
| So 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?
| resonantjacket5 wrote:
| In 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. ``` ABC _D_mean ABC_ D_se ABCD_mean
| ABCD_se XYC _D_mean XYC_ D_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.
| rkp8000 wrote:
| My 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.
| o_p wrote:
| So memory and sensory get multiplexed?
| [deleted]
| behnamoh wrote:
| Articles on Quanta magazine have clickbait titles.
| chalst wrote:
| And yet this title seems to capture the content quite
| adequately.
| ohazi wrote:
| I 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.
| plg wrote:
| it's the theory of re-consolidation
|
| here are some references
|
| https://pubmed.ncbi.nlm.nih.gov/?term=memory+reconsolidation...
| [deleted]
| ajuc wrote:
| > 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 :)
| mncharity wrote:
| I'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.
| tshaddox wrote:
| I 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 wrote:
| Yeah I'm thinking that's because our interpretation of
| reality and it's abstractions ar 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 wrote:
| Yes, 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.
| sebmellen wrote:
| How 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
| :(
| gus_massa wrote:
| Perhaps the Crick and Mitchison theory about why we dream:
| 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.)
| [deleted]
| User23 wrote:
| In mice.
| Jaecen wrote:
| The 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._
| de6u99er wrote:
| This makes much more sense than having secret memory cells in
| neurons.
| darwingr wrote:
| This 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 wrote:
| Same goes for the ordinary English word "Eigenvector".
| lukeplato wrote:
| There 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-thin...
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