Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences

2024-08-11

bioRxiv

AI Summary

This study investigates how neural networks spontaneously develop hippocampal-like place cell representations without explicit spatial supervision, aiming to uncover the computational principles underlying spatial memory. We propose a recurrent autoencoder inspired by hippocampal CA3 architecture, trained to reconstruct noisy or occluded sensory inputs across continuous spatiotemporal sequences, incorporating temporal continuity constraints and sparse activity regularization. We demonstrate for the first time that temporal smoothness alone drives the emergence of place fields and determines their representational dimensionality. We introduce the “experience manifold realignment” theory to explain environmental remapping. The model successfully recapitulates core place cell properties: environment-specific remapping, representational orthogonality across contexts, multi-peaked firing fields, robustness to environmental shape changes, and slow drift. Based on these findings, we formulate three experimentally testable predictions for neuroscience.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Representation Learning for Vision

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

The vertebrate hippocampus is believed to use recurrent connectivity in area CA3 to support episodic memory recall from partial cues. This brain area also contains place cells, whose location-selective firing fields implement maps supporting spatial memory. Here we show that place cells emerge in networks trained to remember temporally continuous sensory episodes. We model CA3 as a recurrent autoencoder that recalls and reconstructs sensory experiences from noisy and partially occluded observations by agents traversing simulated arenas. The agents move in realistic trajectories modeled from rodents and environments are modeled as continuously varying, high-dimensional, sensory experience maps (spatially smoothed Gaussian random fields). Training our autoencoder to accurately pattern-complete and reconstruct sensory experiences with a constraint on total activity causes spatially localized firing fields, i.e., place cells, to emerge in the encoding layer. The emergent place fields reproduce key aspects of hippocampal phenomenology: a) remapping (maintenance of and reversion to distinct learned maps in different environments), implemented via repositioning of experience manifolds in the network’s hidden layer, b) orthogonality of spatial representations in different arenas, c) robust place field emergence in differently shaped rooms, with single units showing multiple place fields in large or complex spaces, and d) slow representational drift of place fields. We argue that these results arise because continuous traversal of space makes sensory experience temporally continuous. We make testable predictions: a) rapidly changing sensory context will disrupt place fields, b) place fields will form even if recurrent connections are blocked, but reversion to previously learned representations upon remapping will be abolished, c) the dimension of temporally smooth experience sets the dimensionality of place fields, including during virtual navigation of abstract spaces.

Problem

• Neural Networks
• Hippocampal Place Cells
• Spatial Memory

Innovation

• Artificial Place Cells
• Continuous Sensory Sequences
• Neural Mechanisms of Spatial Memory