Introducing the brain: grid cells catalog our environment (Introduction)

by David Turell , Monday, October 30, 2023, 20:04 (389 days ago) @ David Turell
edited by David Turell, Monday, October 30, 2023, 20:11

AI designs mimic our brain's ability to help us understand our environmental issues such as place:

https://techxplore.com/news/2023-10-brain-world.html

"To make our way through the world, our brain must develop an intuitive understanding of the physical world around us, which we then use to interpret sensory information coming into the brain.

"How does the brain develop that intuitive understanding? Many scientists believe that it may use a process similar to what's known as "self-supervised learning." This type of machine learning, originally developed as a way to create more efficient models for computer vision, allows computational models to learn about visual scenes based solely on the similarities and differences between them, with no labels or other information.

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"The researchers found that when they trained models known as neural networks using a particular type of self-supervised learning, the resulting models generated activity patterns very similar to those seen in the brains of animals that were performing the same tasks as the models.

"The findings suggest that these models are able to learn representations of the physical world that they can use to make accurate predictions about what will happen in that world, and that the mammalian brain may be using the same strategy, the researchers say.

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"To create a more efficient alternative, in recent years researchers have turned to models built through a technique known as contrastive self-supervised learning. This type of learning allows an algorithm to learn to classify objects based on how similar they are to each other, with no external labels provided.

"This is a very powerful method because you can now leverage very large modern data sets, especially videos, and really unlock their potential," Nayebi says.

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"These types of models, also called neural networks, consist of thousands or millions of processing units connected to each other. Each node has connections of varying strengths to other nodes in the network. As the network analyzes huge amounts of data, the strengths of those connections change as the network learns to perform the desired task.

"As the model performs a particular task, the activity patterns of different units within the network can be measured. Each unit's activity can be represented as a firing pattern, similar to the firing patterns of neurons in the brain. Previous work from Nayebi and others has shown that self-supervised models of vision generate activity similar to that seen in the visual processing system of mammalian brains.

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"Once the model was trained, the researchers had it generalize to a task they call "Mental-Pong." This is similar to the video game Pong, where a player moves a paddle to hit a ball traveling across the screen. In the Mental-Pong version, the ball disappears shortly before hitting the paddle, so the player has to estimate its trajectory in order to hit the ball.

The researchers found that the model was able to track the hidden ball's trajectory with accuracy similar to that of neurons in the mammalian brain, which had been shown in a previous study by Rajalingham and Jazayeri to simulate its trajectory—a cognitive phenomenon known as "mental simulation." Furthermore, the neural activation patterns seen within the model were similar to those seen in the brains of animals as they played the game—specifically, in a part of the brain called the dorsomedial frontal cortex. No other class of computational model has been able to match the biological data as closely as this one, the researchers say.

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"The study led by Khona, Schaeffer, and Fiete focused on a type of specialized neurons known as grid cells. These cells, located in the entorhinal cortex, help animals to navigate, working together with place cells located in the hippocampus. (my bold)

"While place cells fire whenever an animal is in a specific location, grid cells fire only when the animal is at one of the vertices of a triangular lattice. Groups of grid cells create overlapping lattices of different sizes, which allows them to encode a large number of positions using a relatively small number of cells.

In recent studies, researchers have trained supervised neural networks to mimic grid cell function by predicting an animal's next location based on its starting point and velocity, a task known as path integration. However, these models hinged on access to privileged information about absolute space at all times—information that the animal does not have.

"Inspired by the striking coding properties of the multiperiodic grid-cell code for space, the MIT team trained a contrastive self-supervised model to both perform this same path integration task and represent space efficiently while doing so. For the training data, they used sequences of velocity inputs. The model learned to distinguish positions based on whether they were similar or different—nearby positions generated similar codes, but further positions generated more different codes.

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"Once the model was trained, the researchers found that the activation patterns of the nodes within the model formed several lattice patterns with different periods, very similar to those formed by grid cells in the brain.

Comment: we see how the brain helps us, but the black box is knowing how the neurons activate and organize. At least we can mimic it . Note the bold. Hippocampus is involved.