What structures does our brain use for a quick indexing of all the data we store in our heads?

From how my brains work (i.e. that I can very quickly recollect something from the past) I would say that my brain does not do a linear scan of all the memories, otherwise it would take a lot of time to recollect something. So, we for sure have indexing structures in our head.

From the time complexity point of view I would say it should be at least logarithmic or even faster. Do we maybe have B-trees or LSM-trees implemented in our brains?

I am curious to learn more about the topic, but so far I was not able to find a good explanation about it.

I am playing around with creating my own database and would like to try to simulate the workings of my brain to get something similar. Hence, the curiosity in the question.

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    $\begingroup$ B-trees! No computer programs apply. Our brains are essentially entirely parallel and asynchronous, unlike any nontrivial machine man has ever built. $\endgroup$
    – mgkrebbs
    Commented Aug 26, 2021 at 20:28
  • 1
    $\begingroup$ We don't know.. $\endgroup$
    – DKNguyen
    Commented Aug 26, 2021 at 22:46
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    $\begingroup$ Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. $\endgroup$
    – Community Bot
    Commented Aug 27, 2021 at 0:43
  • $\begingroup$ @Community, please, edit your comment to limit it to a specific problem with enough detail to identify an adequate course of actions for updating my question. Thanks. $\endgroup$
    – qqqqqqq
    Commented Aug 29, 2021 at 6:59

1 Answer 1


Brains are not computers, and do not process information like computers.

The trees from CS you are talking about are ways to run linear information stores through a central processing unit. Even with machines that can sort of do these operations in parallel, they are still very much series computations.

Brains, on the other hand, are massively parallel, and information is encoded in a very high dimensional space, not linearly. There are no "bits" in a sequence to sort through. Information does not just move from "storage" to some "processing" part of the brain, it is constantly being processed at all stages in all places. When a memory is active, that information is linked to all the other related information physically because neurons are connected to each other. That is, when you think of a "cat", you might retrieve a memory of a specific cat you saw in a specific place because there is a physical connection between some of the same neurons that are active when you think of "cat" and the neurons encoding that engram of the specific cat memory. No "search" is necessary; every extremely high-dimensional brain state is followed by another extremely high-dimensional brain state. When you "search" your memory for cats, you are just biasing these future states to be those which involve neurons that were involved in the past during experiences of cats.

I think the most attractive models for understanding how brains index information are attractor networks. There are in silico models and implementations of these sorts of networks, but without the parallel hardware of a biological brain they aren't particularly efficient.


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