Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences
August 11, 2024 Β· Declared Dead Β· π Neural Information Processing Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Zhaoze Wang, Ronald W. Di Tullio, Spencer Rooke, Vijay Balasubramanian
arXiv ID
2408.05798
Category
q-bio.NC
Cross-listed
cs.AI,
cs.LG,
cs.NE
Citations
9
Venue
Neural Information Processing Systems
Last Checked
6 months ago
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 rooms. The agents move in realistic trajectories modeled from rodents and environments are modeled as high-dimensional sensory experience maps. Training our autoencoder to pattern-complete and reconstruct 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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β q-bio.NC
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
SuperSpike: Supervised learning in multi-layer spiking neural networks
R.I.P.
π»
Ghosted
Generic decoding of seen and imagined objects using hierarchical visual features
R.I.P.
π»
Ghosted
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
R.I.P.
π»
Ghosted
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
R.I.P.
π»
Ghosted
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted