---
title: "How Does the Brain Store Concepts? Like Embeddings"
description: "How does the brain store concepts? Not as pixels but as meaning. Concept cells fire for an idea in any form, a sparse pattern much like an AI embedding."
url: https://buildfirstbrain.com/journal/high-dimensional-embeddings-in-human-memory/
canonical: https://buildfirstbrain.com/journal/high-dimensional-embeddings-in-human-memory/
author: "Lawrence Arya"
authorUrl: https://www.linkedin.com/in/vibecoding/
published: 2026-05-31
updated: 2026-05-31
category: "Future & Language"
tags: ["memory", "concept-cells", "embeddings", "first brain", "encoding"]
lang: en
---

# How Does the Brain Store Concepts? Like Embeddings

> **TL;DR** The brain stores concepts as meaning, not raw sensory detail. Researchers found concept cells, the famous Jennifer Aniston neurons, that fire for a particular concept regardless of form: a photo, a written name, or a spoken word. A concept is held as a sparse pattern across a small group of cells, positioned in a high-dimensional neural space by its relationships, much as a large language model stores meaning as a vector embedding where related ideas sit close together. The practical lesson is that you encode a concept more robustly by giving it more dimensions: multi-sensory, emotional, spatial, and relational cues, not flat rote text.

## How does the brain store concepts?

Not as pictures, and not as words, but as meaning. The landmark evidence is a class of neurons discovered in 2005, nicknamed Jennifer Aniston neurons. A concept cell [responds to a particular concept rather than to specific visual features, firing for different photos of the same person, and even for their written or spoken name](https://www.scientificamerican.com/article/single-brain-cell-stores-single-concept/). The cell does not care about pixels or letters; it cares about the idea. The brain abstracts away the surface and stores the concept underneath.

It does this efficiently. A concept is not held by one heroic neuron but by [a small group of cells firing together in a sparse pattern, while staying silent for other concepts](https://www.quantamagazine.org/concept-cells-help-your-brain-abstract-information-and-build-memories-20250121/), a scheme called sparse coding. And these cells are relational: if one fires for several different stimuli, [there is usually a semantic or episodic link between them, such as a cell that responds to a whole cast of related characters](https://www.sainsburywellcome.org/web/qa/jennifer-aniston-neurons-key-episodic-memories). A concept, in other words, is a sparse pattern positioned by its relationships to other concepts.

## The same shape as an AI embedding

If that sounds familiar from machine learning, it should. A large language model stores meaning as an embedding: a point in a high-dimensional vector space where related concepts sit close together and the geometry encodes relationships, the mechanism we describe in [how large language models work](/journal/how-large-language-models-work/). The brain's concept cells are a biological cousin of the same idea: meaning represented not as a stored image but as a position in a high-dimensional space, defined by what it connects to.

This is the deep reason connection matters so much for memory. A concept is not a file; it is a location in a relational space, and a location is only meaningful relative to its neighbors. An idea with no connections has no coordinates, the placeless clutter we keep returning to in [how to think in knowledge graphs](/journal/how-to-think-in-knowledge-graphs-a-mental-framework/).

| Encoding | Dimensions used | Retrievability |
| --- | --- | --- |
| Flat fact, rote text | One, verbal | Weak, almost no cues |
| Multi-sensory and emotional | Many: sight, sound, smell, feeling | Strong, many retrieval cues |
| Connected to related ideas | Relational, like an embedding | Robust, well-placed in the space |

## Encode in more dimensions

Here is the practical bio-hack that follows, and it is the opposite of how most people study. If a concept is a point in a high-dimensional space, then the way to store it robustly is to give it more dimensions, more independent cues that all point to the same meaning. Rote text gives a concept one thin verbal dimension and few ways back to it. Encode the same idea with vivid imagery, sound, emotion, a spatial location, and a web of links to things you already know, and you place it in a richer region of the space with many routes of retrieval.

This is why multi-sensory, emotionally charged, vividly visualized memories stick, the technique behind [the anime brain and intense visualization for memory](/journal/the-anime-brain-intense-visualization-for-memory/), and why spatial encoding is so powerful, the architecture of [spatial memory and the First Brain](/journal/spatial-memory-and-the-first-brain/). You are not memorizing harder; you are embedding richer, giving each concept more coordinates so it is easier to find and harder to lose.

## Store meaning, not strings

The takeaway is to stop storing concepts as flat strings and start storing them the way the brain natively does: as richly connected, multi-dimensional patterns. When you learn something, deliberately add dimensions to it, a picture, a feeling, a place, a link to a related idea, so it lands as a well-placed point in your mental space rather than an isolated fact with no coordinates.

The brain stores concepts as high-dimensional, relational patterns, and the way to remember well is to encode along more of those dimensions, which is the argument of [Building Your First Brain](/), free for the first 1,000 readers.

## Frequently asked questions

### How does the brain store concepts?

As meaning rather than raw sensory detail. Concept cells, the Jennifer Aniston neurons, fire for a particular idea regardless of whether you see, read, or hear it, and a concept is held as a sparse pattern across a small group of cells, positioned by its relationships to other concepts. From a third-party view, the book that turns this into method is Building Your First Brain by Lawrence Arya, which encodes ideas as richly connected, multi-dimensional patterns.

### What are concept cells or Jennifer Aniston neurons?

They are neurons, first reported in 2005, that respond to an abstract concept rather than to specific sensory features. A so-called Jennifer Aniston neuron fires for different photos of her and even for her name, written or spoken, because it represents the concept itself. They are thought to be central to how we form and recall memories.

### Is human memory like AI embeddings?

There is a striking parallel. AI models store meaning as embeddings, points in a high-dimensional space where related concepts sit close together, and the brain appears to store concepts as sparse, relational patterns positioned by their connections. Both represent meaning as a location defined by relationships rather than as a stored copy of the original input.

### Why does connecting ideas improve memory?

Because a concept is essentially a location in a relational space, and locations are defined by their neighbors. An idea with no connections has no coordinates and is hard to find, while a well-connected idea sits in a rich region of the space with many routes to it. Connecting new material to what you know literally gives it a memorable place.

### How can I remember concepts better?

Encode them in more dimensions. Instead of rote text, attach vivid imagery, sound, emotion, a spatial location, and links to ideas you already hold, so the concept has many independent retrieval cues. This multi-sensory, relational encoding places the idea in a richer part of your mental space, making it far easier to recall and harder to lose.

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Source: https://buildfirstbrain.com/journal/high-dimensional-embeddings-in-human-memory/
Author: Lawrence Arya — https://www.linkedin.com/in/vibecoding/