Angus Chadwick
Flexible theta sequence compression mediated via phase precessing interneurons
Chadwick, Angus; van Rossum, Mark C.W.; Nolan, Matthew F.
Authors
Professor MARK VAN ROSSUM Mark.VanRossum@nottingham.ac.uk
CHAIR AND DIRECTOR/NEURAL COMPUTATION RESEARCH GROUP
Matthew F. Nolan
Abstract
Encoding of behavioral episodes as spike sequences during hippocampal theta oscillations provides a neural substrate for computations on events extended across time and space. However, the mechanisms underlying the numerous and diverse experimentally observed properties of theta sequences remain poorly understood. Here we account for theta sequences using a novel model constrained by the septo-hippocampal circuitry. We show that when spontaneously active interneurons integrate spatial signals and theta frequency pacemaker inputs, they generate phase precessing action potentials that can coordinate theta sequences in place cell populations. We reveal novel constraints on sequence generation, predict cellular properties and neural dynamics that characterize sequence compression, identify circuit organization principles for high capacity sequential representation, and show that theta sequences can be used as substrates for association of conditioned stimuli with recent and upcoming events. Our results suggest mechanisms for flexible sequence compression that are suited to associative learning across an animal's lifespan.
Citation
Chadwick, A., van Rossum, M. C., & Nolan, M. F. (2016). Flexible theta sequence compression mediated via phase precessing interneurons. eLife, 5, Article e20349. https://doi.org/10.7554/eLife.20349
Journal Article Type | Article |
---|---|
Acceptance Date | Dec 7, 2016 |
Publication Date | Dec 8, 2016 |
Deposit Date | Feb 7, 2018 |
Publicly Available Date | Feb 7, 2018 |
Journal | eLife |
Electronic ISSN | 2050-084X |
Publisher | eLife Sciences Publications |
Peer Reviewed | Peer Reviewed |
Volume | 5 |
Article Number | e20349 |
DOI | https://doi.org/10.7554/eLife.20349 |
Public URL | https://nottingham-repository.worktribe.com/output/835988 |
Publisher URL | https://elifesciences.org/articles/20349 |
Contract Date | Feb 7, 2018 |
Files
chadwick_16.pdf
(4.7 Mb)
PDF
Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
You might also like
Reinforcement learning when your life depends on it: a neuro-economic theory of learning
(2024)
Preprint / Working Paper
Energetically efficient learning in neuronal networks
(2023)
Journal Article
Competitive plasticity to reduce the energetic costs of learning
(2023)
Preprint / Working Paper
Lazy learning: a biologically-inspired plasticity rule for fast and energy efficient synaptic plasticity
(2023)
Preprint / Working Paper
Rule Abstraction Is Facilitated by Auditory Cuing in REM Sleep
(2023)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search