Predictive Coding Neural Network Constructs Implicit Spatial Maps from Visual Exploration

Core Concepts

A predictive coding neural network can construct an implicit spatial map of an environment by learning to predict future visual observations during exploration, without access to explicit spatial coordinates.

Abstract

The content describes how a predictive coding neural network can construct an implicit spatial map of an environment by learning to predict future visual observations during exploration. The key insights are:

The predictive coding problem can be formulated as an inference procedure that constructs an implicit representation of the agent's environment to predict future sensory observations. This suggests that the underlying inference problem can be solved by an encoder-decoder neural network.

The predictive coding neural network embeds images collected during exploration into an internal representation of space, where the distances between images reflect their relative spatial position, not object-level similarity.

The predictive coding network generates units with localized receptive fields (place fields) that cover the entire environment. The combination of overlapping place fields at each location provides a unique code that can be used to measure distances and perform vector-based navigation.

In contrast, an autoencoder network that learns to reconstruct individual images, without the sequential prediction task, fails to recover the spatial structure of the environment, especially in regions with visually degenerate landmarks.

The content demonstrates how predictive coding provides a general mechanism for constructing spatial and non-spatial cognitive maps from sensory experience.

Stats

The predictive coding neural network has a mean-squared error of 0.094 between the actual and predicted images.
The predictive coder has a mean position prediction error of 5.04 lattice units, with over 80% of samples having an error less than 7.3 lattice units.
The predictive coder's latent distances have a Pearson correlation coefficient of 0.827 and a Kullback-Leibler divergence of 0.429 bits with the environment's physical distances.
The autoencoder has a mean position prediction error of 13.1 lattice units and a Kullback-Leibler divergence of 3.806 bits between its latent distances and the environment's physical distances.
The proposed mechanism for vector navigation using the predictive coder's binary place field vectors has a mutual information of 0.542 bits, compared to the predictive coder's 0.627 bits and the autoencoder's 0.227 bits.

Quotes

"Predictive coding has been proposed as a unifying theory of neural function where the fundamental goal of a neural system is to predict future observations given past data."
"Fundamentally, we connect predictive coding and mapping tasks, demonstrating a computational and mathematical strategy for integrating information from local measurements into a global self-consistent environmental model."
"Broadly, our work introduces predictive coding as a unified algorithmic framework for constructing cognitive maps that can naturally extend to the mapping of auditory, sensorimotor, and linguistic inputs."

Key Insights Distilled From

Automated mapping of virtual environments with visual predictive coding

by James Gornet... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2308.10913.pdf

Deeper Inquiries

How can the predictive coding framework be extended to construct cognitive maps for non-visual sensory modalities, such as auditory or somatosensory inputs?

The predictive coding framework can be extended to construct cognitive maps for non-visual sensory modalities by adapting the same principles used for visual predictive coding. Just as visual predictive coding involves predicting future visual observations based on past data, the same concept can be applied to auditory or somatosensory inputs.
For auditory inputs, the neural network can be trained to predict future auditory stimuli based on past auditory data. By encoding the sequential auditory information into a latent space and using predictive coding to generate accurate predictions, the network can construct an internal representation of the auditory environment. This representation would capture the spatial relationships between different auditory stimuli and enable the network to navigate and make predictions based on sound cues.
Similarly, for somatosensory inputs, the network can be trained to predict future tactile sensations or proprioceptive feedback based on past somatosensory data. By encoding the sequential somatosensory information into a latent space and using predictive coding, the network can construct a cognitive map of the somatosensory environment. This map would allow the network to navigate and interact with the environment based on touch and body position information.
Overall, by applying the predictive coding framework to non-visual sensory modalities, cognitive maps can be constructed that capture the spatial relationships and dynamics of auditory or somatosensory inputs, enabling the network to make accurate predictions and navigate effectively in these sensory domains.

What are the implications of the predictive coding mechanism for understanding the emergence of place cells and grid cells in the mammalian brain?

The predictive coding mechanism has significant implications for understanding the emergence of place cells and grid cells in the mammalian brain. Place cells and grid cells are specialized neurons found in the hippocampus and entorhinal cortex, respectively, that play a crucial role in spatial navigation and cognitive mapping. These cells are known to fire in specific patterns in response to an animal's location in its environment, forming a cognitive map of space.
The predictive coding mechanism provides a computational framework for how these spatially selective cells may emerge in the brain. By predicting future sensory observations based on past data, the brain can construct an internal representation of the environment that reflects spatial relationships and distances. This process is akin to how place cells and grid cells encode spatial information by firing in specific patterns based on an animal's location and movement.
The predictive coding mechanism suggests that the brain may use a similar algorithmic strategy to construct cognitive maps of space, with place cells and grid cells acting as neural correlates of this predictive coding process. The emergence of place cells and grid cells could be a result of the brain's ability to predict and encode spatial information in a way that supports efficient navigation and spatial memory.
Understanding how predictive coding relates to the emergence of place cells and grid cells can provide insights into the neural mechanisms underlying spatial cognition and navigation in the mammalian brain. It highlights the importance of predictive processing in constructing cognitive maps and spatial representations that are essential for adaptive behavior in complex environments.

Can the insights from this work on spatial mapping be applied to construct cognitive maps for more abstract, non-spatial domains, such as semantic or conceptual knowledge?

The insights from this work on spatial mapping can indeed be applied to construct cognitive maps for more abstract, non-spatial domains, such as semantic or conceptual knowledge. The predictive coding framework, which involves predicting future observations based on past data, can be adapted to construct cognitive maps for these non-spatial domains by encoding sequential information into a latent space and using predictive coding to generate accurate predictions.
For semantic knowledge, the network can be trained to predict future linguistic inputs based on past language data. By encoding the sequential linguistic information into a latent space and using predictive coding, the network can construct an internal representation of semantic relationships and concepts. This representation would capture the associations between words and meanings, enabling the network to navigate and make predictions based on semantic cues.
Similarly, for conceptual knowledge, the network can be trained to predict future abstract concepts or ideas based on past data. By encoding the sequential conceptual information into a latent space and using predictive coding, the network can construct a cognitive map of abstract knowledge domains. This map would capture the relationships between different concepts and facilitate reasoning and inference based on conceptual associations.
Overall, the predictive coding framework can be a powerful tool for constructing cognitive maps in non-spatial domains, allowing for the representation and manipulation of complex semantic and conceptual knowledge. By applying the principles of predictive coding to abstract domains, researchers can gain insights into how the brain organizes and processes non-spatial information, leading to a deeper understanding of cognitive processes and neural representations in these domains.