Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3
Abstract
Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated ('replayed'), either in the same or reversed order, during bursts of activity (sharp wave-ripples; SWRs) that occur in sleep and awake rest. SWR-associated replay is thought to be critical for the creation and maintenance of long-term memory. In order to identify the cellular and network mechanisms of SWRs and replay, we constructed and simulated a data-driven model of area CA3 of the hippocampus. Our results show that the chain-like structure of recurrent excitatory interactions established during learning not only determines the content of replay, but is essential for the generation of the SWRs as well. We find that bidirectional replay requires the interplay of the experimentally confirmed, temporally symmetric plasticity rule, and cellular adaptation. Our model provides a unifying framework for diverse phenomena involving hippocampal plasticity, representations, and dynamics, and suggests that the structured neural codes induced by learning may have greater influence over cortical network states than previously appreciated.
Data availability
The source code to build, run and analyze our model is publicly available on GitHub: https://github.com/KaliLab/ca3net
Article and author information
Author details
Funding
Hungarian Scientific Research Fund (K83251)
- Maria Rita Karlocai
- Attila I Gulyás
- Szabolcs Káli
Hungarian Scientific Research Fund (K85659)
- Orsolya Steinbach-Németh
- Norbert Hájos
Hungarian Scientific Research Fund (K115441)
- Attila I Gulyás
- Szabolcs Káli
Hungarian Brain Research Program (2017-1.2.1-NKP-2017-00002)
- Norbert Hájos
European Commission (ERC 2011 ADG 294313)
- Tamás Freund
- Attila I Gulyás
- Szabolcs Káli
European Commission (FP7 no. 604102,H2020 no. 720270,no. 785907 (Human Brain Project))
- Tamás Freund
- Attila I Gulyás
- Szabolcs Káli
Hungarian Ministry of Innovation and Technology NRDI Office (Artificial Intelligence National Laboratory)
- Szabolcs Káli
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Neil Burgess, University College London, United Kingdom
Ethics
Animal experimentation: Experiments were approved by the Committee for the Scientific Ethics of Animal Research (22.1/4027/003/2009) and were performed according to the guidelines of the institutional ethical code and the Hungarian Act of Animal Care and Experimentation. Experiments were performed in acute brain slices; no animal suffering was involved as mice were deeply anaesthetized with isoflurane and decapitated before slice preparation. Data recorded in the context of other studies were used for model fitting, and therefore no additional animals were used for the purpose of this study.
Version history
- Preprint posted: February 18, 2021 (view preprint)
- Received: July 2, 2021
- Accepted: January 17, 2022
- Accepted Manuscript published: January 18, 2022 (version 1)
- Version of Record published: February 23, 2022 (version 2)
Copyright
© 2022, Ecker et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 3,730
- views
-
- 518
- downloads
-
- 18
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Evolutionary Biology
A comprehensive census of McrBC systems, among the most common forms of prokaryotic Type IV restriction systems, followed by phylogenetic analysis, reveals their enormous abundance in diverse prokaryotes and a plethora of genomic associations. We focus on a previously uncharacterized branch, which we denote coiled-coil nuclease tandems (CoCoNuTs) for their salient features: the presence of extensive coiled-coil structures and tandem nucleases. The CoCoNuTs alone show extraordinary variety, with three distinct types and multiple subtypes. All CoCoNuTs contain domains predicted to interact with translation system components, such as OB-folds resembling the SmpB protein that binds bacterial transfer-messenger RNA (tmRNA), YTH-like domains that might recognize methylated tmRNA, tRNA, or rRNA, and RNA-binding Hsp70 chaperone homologs, along with RNases, such as HEPN domains, all suggesting that the CoCoNuTs target RNA. Many CoCoNuTs might additionally target DNA, via McrC nuclease homologs. Additional restriction systems, such as Type I RM, BREX, and Druantia Type III, are frequently encoded in the same predicted superoperons. In many of these superoperons, CoCoNuTs are likely regulated by cyclic nucleotides, possibly, RNA fragments with cyclic termini, that bind associated CARF (CRISPR-Associated Rossmann Fold) domains. We hypothesize that the CoCoNuTs, together with the ancillary restriction factors, employ an echeloned defense strategy analogous to that of Type III CRISPR-Cas systems, in which an immune response eliminating virus DNA and/or RNA is launched first, but then, if it fails, an abortive infection response leading to PCD/dormancy via host RNA cleavage takes over.
-
- Computational and Systems Biology
Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream classification. Here we propose novel imputation methods based on determinantal point processes (DPP) that enhance popular techniques such as the multivariate imputation by chained equations and MissForest. Their advantages are twofold: improving the quality of the imputed data demonstrated by increased accuracy of the downstream classification and providing deterministic and reliable imputations that remove the variance from the classification results. We experimentally demonstrate the advantages of our methods by performing extensive imputations on synthetic and real clinical data. We also perform quantum hardware experiments by applying the quantum circuits for DPP sampling since such quantum algorithms provide a computational advantage with respect to classical ones. We demonstrate competitive results with up to 10 qubits for small-scale imputation tasks on a state-of-the-art IBM quantum processor. Our classical and quantum methods improve the effectiveness and robustness of clinical data prediction modeling by providing better and more reliable data imputations. These improvements can add significant value in settings demanding high precision, such as in pharmaceutical drug trials where our approach can provide higher confidence in the predictions made.