[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 : 385 points
Date : 2021-04-16 06:04 UTC (2 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.)
| jakear wrote:
| > 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.
| 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.
| alexanderdmitri wrote:
| You are technically readable words of a mostly abstract
| person.
| throwaway189262 wrote:
| > They had the animals passively listen to sequences of four
| chords over and over again, in what Buschman dubbed "the worst
| concert ever."
|
| Hahahaha!
| trott wrote:
| Something to keep in mind though is that in a high-dimensional
| space, approximate orthogonality of independent vectors is almost
| guaranteed.
| iandanforth wrote:
| No? If the samples are randomly chosen then you'd expect the
| cosign similarity to be low, but there's no such assumption
| here, in fact it's the exact opposite.
| 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?
| mrbungie wrote:
| Yes, just as a set of 1000-levers (arbitrary number, but
| highly dimensional) can influence a machine (in our 3d
| reality).
| 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?
| riwsky wrote:
| The issue with your question is that the dimensions of
| the configuration space and the physical form aren't even
| _approximately_ the same thing. Take, for example, a
| 100x100 grayscale image. It's a flat image; the physical
| dimensions are 2. There are 10,000 different pixels
| though, and they are all allowed to vary independently of
| each other; the configuration-space dimensions are
| 10,000. Neurons are like the pixels in this analogy, not
| like the the physical dimensions.
| posterboy wrote:
| Neurons are not random access. The analogy is otherwise
| pretty apt, except that an image doesn't store
| information about what it displays and I don't mean EXIF.
| mapt wrote:
| Neurons aren't allowed to vary independently of each
| other, and neither are pixels; A grayscale image with
| random pixels is static, not even recognizable as an
| image. The mind cannot decode those pixels in a seven-
| dimensional indexing scheme, it can't even decode them in
| the given two dimensions if you have an array size error
| and store the same data in an array 87 columns wide. In
| your analogy, if you put a stop sign into the upper right
| side of the image, that is always going to be recalled
| associativity with the green caterpillar you put in the
| lower left side of the image. These properties don't work
| so well for memories & imperfect/error-prone but
| statistically correct biological systems.
|
| The average neuron has 1000 synapses, and for geometric
| reasons (Synaptic connections take up space) most of
| those are to other neurons that aren't very far away in
| 3D space.
| true_religion wrote:
| Maybe, but my guess would be that there's a trade off
| made here. Either you can use higher dimensionality in
| the abstract, or you can have a much much bigger brain. A
| bigger brain processes slower merely because of volume
| and requires a lot more resources to support it.
|
| Nature stumbled onto the path that it did because we
| don't have high enough nutrient food or fast enough
| neurons.
| 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.
| austinjp wrote:
| This is fun, I'm enjoying reading the replies :) I'm
| certainly no expert, but attempting an explanation helps
| me exercise my personal understanding, so here goes.
| Corrections welcome.
|
| The "connections" you mention aren't the issue, in my
| understanding of the biology. Neurons are already very
| strongly interconnected by numerous synapses, so they
| already _do_ physically fit together in their available
| 3D space, and appear capable of representing high-
| dimensional concepts. (See caveat below.)
|
| The "higher dimensions" here are not where the neurons
| exist, only what they're capable of representing. If we
| think about a representation of the concept of a "dog"
| for example, there are many dimensions. Size, colour,
| breed, temperament, barking, growling, panting, etc etc.
| Those attributes are dimensions.
|
| Take two dog attributes: size and breed. You can plot a
| graph of dogs, each dog being a mark on the graph of size
| vs breed. Add a third dimension and turn the graph into a
| cube: temperament. You can probably imagine plotting dogs
| inside this three dimensional space.
|
| It's very difficult to imagine that graph extending into
| 4th, 5th or further dimensions. And yet, you can easily
| imagine, say, a dog that's a large, black, friendly
| Labrador with a deep bark who growls only rarely. We
| could say that dog can be represented as a point in
| 6-dimensional space (or perhaps a 6-dimensional slice
| through a space with even more dimensions, just a slice
| through 3D space could produce a 2D graph).
|
| The number of connections between neurons may be related
| to the number of dimensions they can represent. In
| honesty, I don't know, and I guess that if there _is_ a
| relationship it may not be linear. So neurons might be
| capable of representing 4 dimensions with fewer than 4
| synapses, for example, I don 't know. Seems possible to
| me, though.
|
| Caveat: I think my reasoning here may be fallacious: "the
| fact that neurons are capable of representing high-
| dimension concepts demonstrates that they have adequate
| synapses to do so". It seems akin to anthropocentrism,
| I'm not sure. Perhaps it's just a circular argument. I
| think it provides an adequate basis for an ELI5 though.
|
| I look forward to further comments!
| ww520 wrote:
| The vector here refers to the "feature vector" where the
| dimension is the number of elements in the vector. E.g. a
| feature vector of [size, length, width, height, color,
| shape, smell] has 7 dimensions. A feature vector for the
| space has 3 dimensions [x, y, z]. The term "higher
| dimension" just means the number of features encoded in
| the vector is higher than usual.
|
| In the context of neurons, while the neurons are in the 3
| spatial dimensions, the connections of each neuron can be
| encoded in a feature vector. Each connection can
| specialize on one feature, e.g. the hair color of the
| person. These connection features can be encoded in a
| vector. The number of connections becomes the dimension
| of the vector. Not to be confused with the physical 3D
| spatial dimensions of the neurons.
|
| The nice thing about encoding things in vectors is that
| you can use generic math to manipulate them. E.g.
| rotation mentioned in this article, orthogonality of
| vectors implies they have no overlap, or dot product of
| vectors measures how "similar" they are. Apparently this
| article shows that different versions of the sensory data
| encoded in neurons can be rotated just like vector
| rotation so that they are orthogonal and won't interfere
| with each other.
|
| Linear algebra usually deals with 2 or 3 dimensions.
| Geometric algebra works better on higher dimension
| vectors.
| posterboy wrote:
| the ELI 5 of higher dimensions explained mathematically
| in text is that a coordinate in R^3 is identified
| uniquely by a three tuple u = (x, y, z). A four touple
| simply adds one dimension. That might be a time
| coordinate, color, etc.
|
| If I remember correctly, the integers Z form spaces, too.
| Z^2 can be illustrated as grid, where every node is
| uniquely identified again coordinates or by two of its
| neighbours, eitherway v = (a, b).
|
| Adjency lists or index matrices are common ways to encode
| graphs. My modelnof a neuron network is then a graph.
|
| I imagine that, since Neurons have many more Synapses,
| that's how you get a manifold with many more coordinates.
|
| Each Neuron stores action potential much like color of a
| pixel and its state evolves over time, but that's when
| the model becomes limited.
|
| How it actually represents complex information in this
| structure I don't know.
|
| PS: Or very simply put, physics has more than three
| dimensions.
| dboreham wrote:
| Same as a silicon chip stuck in 2 dimensions can.
| Salgat wrote:
| Don't conflate physical and logical, in this case we
| don't care about the physical dimensions, only how the
| logic is expressed. Even a 2D function can be expressed
| in N-dimensional parameters, such as
|
| y = a1 * x + a2 * x^2 + a3 * x^3 + a4 * x^4
|
| where you only have one input and one output, but 4
| constants that can be adjusted. These 4 constants make up
| a 4D vector.
| [deleted]
| 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.
| dogma1138 wrote:
| This isn't about the 3 dimensional structure the neurons
| occupy, but about their operational degrees of freedom.
|
| Think about how a CNC machine works, you can have CNC
| with more than 3 axis, for example a 4 axis CNC machine
| can move left/right up/down backwards/forwards and also
| have another axis which can rotate in a given plane.
|
| From a more mathematical perspective just think about the
| number of parameters in a system (excluding reduction)
| each parameter would be a dimension.
| anu7df wrote:
| Appreciate the attempt, but in this example the 4th axis
| is not independent since the motion along that axis can
| be achieved, with some complexity, by the motion along
| the other axes. Granted this is not very useful for a
| machinist because it will be very tedious to machine a
| part this way compared to the dedicated 4th rotating
| axis, but mathematically it is redundant.
|
| I have found it easiest to think of a logical dimensions
| or configurations when thinking of higher dimensions.
| Physically it can be a row of bulbs (lighted or not)
| wherein N bulbs (dimensions) can represent 2^n states in
| total. The 2 here can be increased by having bulbs that
| can light up in many colours.
| posterboy wrote:
| its not redundant. without rotation it could only ever
| drill downwards.
|
| Smartphones eg. measure six dimensions of freedome,
| including rotation about every axis. 3 for location, 3
| for orientation.
|
| this has very little to do with synapses.
| hitlerism wrote:
| The vectors are in a configuration vector space, not a
| physical vector space.
| fao_ wrote:
| I _roughly_ understand what the article says about
| dimensional space (Reading higher mathematics books on
| the way to my meagre college course way back when, helps
| me a little, even if it is all half-remembered and a bit
| wrong -- this understanding is sufficient enough to
| satisfy _me_ ), however the poster above me _doesn 't_,
| and clearly asked for a definition a 5 year old layman
| could understand.
|
| The comment I am replying to, your comment in the tree,
| and the one next to you, does not seem to match that
| request in any sense.
|
| Now, simplified definitions are an art, but Feynman
| managed it with Quantum Electrodynamics -- so it is not
| impossible to do it for complex subjects. And it seems to
| me the less you understand a subject, the less simple and
| more confusing your explanation will be, such as the
| explanations given by the other posters here. (fyi: I do
| not understand enough to properly convey my understanding
| clearly -- which is why I have not attempted to do so)
| 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.
| chadcmulligan wrote:
| Would you have any further reading on this? Sounds
| fascinating.
| dopu wrote:
| Agreed, it's really cool :). A lot of this is very new --
| it's only been in the past decade and a half or so that
| we've been able to record from large populations of
| neurons (on the order of hundreds and up, see [0]). But
| there are a lot of smart people working on figuring out
| how to make sense of this data, and why we see low-
| dimensional signals in these population recordings. Here
| are some good reviews on the subject: [1], [2], [3], [4],
| and [5].
|
| [0]: https://stevenson.lab.uconn.edu/scaling/ [1]:
| https://www.nature.com/articles/nn.3776 [2]:
| https://doi.org/10.1016/j.conb.2015.04.003 [3]:
| https://doi.org/10.1016/j.conb.2019.02.002 [4]:
| https://arxiv.org/abs/2104.00145 [5]:
| https://doi.org/10.1016/j.neuron.2017.05.025
| trott wrote:
| I'm curious about how much of this apparent low
| dimensionality is explained by (1) the physical proximity
| of the neurons being recorded, (2) poverty of the stimuli
| (just 4 sequences in this paper, if I'm not mistaken)
| dopu wrote:
| Both good questions. It could very well be that low
| dimensionality is simply a byproduct of the fact that
| neuroscientists train animals on such simple (i.e., low-
| dimensional) tasks. This paper argues that [0]. As for
| your first point, it is known that auditory cortex
| exhibits tonotopy, such that nearby neurons in auditory
| cortex respond to similar frequencies. But much of cortex
| doesn't really exhibit this kind of simple organization.
| Regardless, technological advancements are making it
| easier for us to record from large populations of neurons
| (as well as track behavior in 3D) while animals freely
| move in more naturalistic environments. I think these
| kinds of experiments will make it clearer whether low-
| dimensional dynamics are a byproduct of simple task
| designs.
|
| [0]:
| https://www.biorxiv.org/content/10.1101/214262v1.abstract
| DavidSJ wrote:
| This is basically just linear algebra.
|
| For an abstract perspective, try Sheldon Axler's _Linear
| Algebra Done Right_.
|
| For a more concrete perspective, Gilbert Strang's
| lectures:
| https://www.youtube.com/playlist?list=PL49CF3715CB9EF31D
| 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
| tgflynn wrote:
| The abstracts are a bit different so I'm not sure how close the
| preprint is to the published version.
| 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
| jamiek88 wrote:
| Wow. That blew my tiny little mind.
|
| I figured out how to change it at will eventually, if you close
| your eyes then open them and look at the bottom of the picture
| first it's an old woman. Do the reverse and it's a young woman.
| Eventually you can do that without the eye closing step but
| never would I say I could see both at once.
|
| Just rapidly switch.
|
| Very interesting!
| 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?
| LordGrey wrote:
| Thanks! That helped me finally see the mother-in-law.
|
| I showed my wife the picture and she couldn't see either
| woman until I pointed out features. Interesting!
| 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?
| mrbungie wrote:
| I'm not really sure if I'm able to see both (or the three
| of them in the case of the 'Mother, Father and Daugther'
| figure), but at least I can switch stupidly 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.
| [deleted]
| ddmma wrote:
| It's this blockchain?
| 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]
| [deleted]
| behnamoh wrote:
| Articles on Quanta magazine have clickbait titles.
| chalst wrote:
| And yet this title seems to capture the content quite
| adequately.
| meiji163 wrote:
| Can someone liberate the article from behind the paywall for me?
| 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]
| drivers99 wrote:
| That sounds like the kind of thing they talk about on Hidden
| Brain (NPR). I think I found it:
|
| 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.
| 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.
| bernardand wrote:
| This is basically just linear algebra.
| 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".
| danwills wrote:
| Reminds 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 wrote:
| Requires a fair bit of detergent to clear up all the crumbs
| anyway: might varying grain sizes help any?
| NavinF wrote:
| Was this comment generated by a markov chain?
| danwills wrote:
| Yeah super wierd, can't make any sense of it at all
| totetsu wrote:
| In the last few months there has been more of these not
| people posts. If we call them out are we just training
| the algorithms?
| zeeshanqureshi wrote:
| And yet the main image on the article illustrates a 45 degree
| rotation along an axis.
|
| From what I understand, you are saying this rotation is non-
| intuitive. Could you elaborate more or share some relevant
| links?
| iandanforth wrote:
| Take 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 wrote:
| So we are just walking Lucene indexes?
| screye wrote:
| This 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 ?
| 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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