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Jauch J, Becker M, Tetzlaff C, Fauth MJ. Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites. PLoS Comput Biol 2024; 20:e1012218. [PMID: 38917228 DOI: 10.1371/journal.pcbi.1012218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 05/31/2024] [Indexed: 06/27/2024] Open
Abstract
Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories.
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Affiliation(s)
- Jannik Jauch
- Third Institute for Physics, Georg-August-University, Göttingen, Germany
| | - Moritz Becker
- Group of Computational Synaptic Physiology, Department for Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany
| | - Christian Tetzlaff
- Group of Computational Synaptic Physiology, Department for Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany
| | - Michael Jan Fauth
- Third Institute for Physics, Georg-August-University, Göttingen, Germany
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2
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Schieferstein N, Schwalger T, Lindner B, Kempter R. Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations. PLoS Comput Biol 2024; 20:e1011886. [PMID: 38377147 PMCID: PMC10923461 DOI: 10.1371/journal.pcbi.1011886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 03/08/2024] [Accepted: 02/01/2024] [Indexed: 02/22/2024] Open
Abstract
Hippocampal ripple oscillations have been implicated in important cognitive functions such as memory consolidation and planning. Multiple computational models have been proposed to explain the emergence of ripple oscillations, relying either on excitation or inhibition as the main pacemaker. Nevertheless, the generating mechanism of ripples remains unclear. An interesting dynamical feature of experimentally measured ripples, which may advance model selection, is intra-ripple frequency accommodation (IFA): a decay of the instantaneous ripple frequency over the course of a ripple event. So far, only a feedback-based inhibition-first model, which relies on delayed inhibitory synaptic coupling, has been shown to reproduce IFA. Here we use an analytical mean-field approach and numerical simulations of a leaky integrate-and-fire spiking network to explain the mechanism of IFA. We develop a drift-based approximation for the oscillation dynamics of the population rate and the mean membrane potential of interneurons under strong excitatory drive and strong inhibitory coupling. For IFA, the speed at which the excitatory drive changes is critical. We demonstrate that IFA arises due to a speed-dependent hysteresis effect in the dynamics of the mean membrane potential, when the interneurons receive transient, sharp wave-associated excitation. We thus predict that the IFA asymmetry vanishes in the limit of slowly changing drive, but is otherwise a robust feature of the feedback-based inhibition-first ripple model.
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Affiliation(s)
- Natalie Schieferstein
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Tilo Schwalger
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Institute for Mathematics, Technische Universität Berlin, Berlin, Germany
| | - Benjamin Lindner
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Department of Physics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Richard Kempter
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neurosciences, Berlin, Germany
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3
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Aksamaz S, Mölle M, Akinola EO, Gromodka E, Bazhenov M, Marshall L. Single closed-loop acoustic stimulation targeting memory consolidation suppressed hippocampal ripple and thalamo-cortical spindle activity in mice. Eur J Neurosci 2024; 59:595-612. [PMID: 37605315 DOI: 10.1111/ejn.16116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 06/20/2023] [Accepted: 07/24/2023] [Indexed: 08/23/2023]
Abstract
Brain rhythms of sleep reflect neuronal activity underlying sleep-associated memory consolidation. The modulation of brain rhythms, such as the sleep slow oscillation (SO), is used both to investigate neurophysiological mechanisms as well as to measure the impact of sleep on presumed functional correlates. Previously, closed-loop acoustic stimulation in humans targeted to the SO Up-state successfully enhanced the slow oscillation rhythm and phase-dependent spindle activity, although effects on memory retention have varied. Here, we aim to disclose relations between stimulation-induced hippocampo-thalamo-cortical activity and retention performance on a hippocampus-dependent object-place recognition task in mice by applying acoustic stimulation at four estimated SO phases compared to sham condition. Across the 3-h retention interval at the beginning of the light phase closed-loop stimulation failed to improve retention significantly over sham. However, retention during SO Up-state stimulation was significantly higher than for another SO phase. At all SO phases, acoustic stimulation was accompanied by a sharp increase in ripple activity followed by about a second-long suppression of hippocampal sharp wave ripple and longer maintained suppression of thalamo-cortical spindle activity. Importantly, dynamics of SO-coupled hippocampal ripple activity distinguished SOUp-state stimulation. Non-rapid eye movement (NREM) sleep was not impacted by stimulation, yet preREM sleep duration was effected. Results reveal the complex effect of stimulation on the brain dynamics and support the use of closed-loop acoustic stimulation in mice to investigate the inter-regional mechanisms underlying memory consolidation.
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Affiliation(s)
- Sonat Aksamaz
- Institute of Experimental and Clinical Pharmacology, University of Lübeck, Lübeck, Germany
- University Medical Center Schleswig-Holstein, Lübeck, Germany
| | - Matthias Mölle
- University Medical Center Schleswig-Holstein, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, Lübeck, Germany
| | - Esther Olubukola Akinola
- Institute of Experimental and Clinical Pharmacology, University of Lübeck, Lübeck, Germany
- University Medical Center Schleswig-Holstein, Lübeck, Germany
| | - Erik Gromodka
- Institute of Experimental and Clinical Pharmacology, University of Lübeck, Lübeck, Germany
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Lisa Marshall
- Institute of Experimental and Clinical Pharmacology, University of Lübeck, Lübeck, Germany
- University Medical Center Schleswig-Holstein, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, Lübeck, Germany
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4
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Pochinok I, Stöber TM, Triesch J, Chini M, Hanganu-Opatz IL. A developmental increase of inhibition promotes the emergence of hippocampal ripples. Nat Commun 2024; 15:738. [PMID: 38272901 PMCID: PMC10810866 DOI: 10.1038/s41467-024-44983-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
Sharp wave-ripples (SPW-Rs) are a hippocampal network phenomenon critical for memory consolidation and planning. SPW-Rs have been extensively studied in the adult brain, yet their developmental trajectory is poorly understood. While SPWs have been recorded in rodents shortly after birth, the time point and mechanisms of ripple emergence are still unclear. Here, we combine in vivo electrophysiology with optogenetics and chemogenetics in 4 to 12-day-old mice to address this knowledge gap. We show that ripples are robustly detected and induced by light stimulation of channelrhodopsin-2-transfected CA1 pyramidal neurons only from postnatal day 10 onwards. Leveraging a spiking neural network model, we mechanistically link the maturation of inhibition and ripple emergence. We corroborate these findings by reducing ripple rate upon chemogenetic silencing of CA1 interneurons. Finally, we show that early SPW-Rs elicit a more robust prefrontal cortex response than SPWs lacking ripples. Thus, development of inhibition promotes ripples emergence.
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Affiliation(s)
- Irina Pochinok
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology (ZMNH), Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Tristan M Stöber
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
| | - Jochen Triesch
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
| | - Mattia Chini
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology (ZMNH), Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany.
| | - Ileana L Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology (ZMNH), Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany.
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5
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Borges FS, Gabrick EC, Protachevicz PR, Higa GSV, Lameu EL, Rodriguez PXR, Ferraz MSA, Szezech JD, Batista AM, Kihara AH. Intermittency properties in a temporal lobe epilepsy model. Epilepsy Behav 2023; 139:109072. [PMID: 36652897 DOI: 10.1016/j.yebeh.2022.109072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/22/2022] [Accepted: 12/26/2022] [Indexed: 01/18/2023]
Abstract
Neuronal synchronization is important for communication between brain regions and plays a key role in learning. However, changes in connectivity can lead to hyper-synchronized states related to epileptic seizures that occur intermittently with asynchronous states. The activity-regulated cytoskeleton-associated protein (ARC) is related to synaptic alterations which can lead to epilepsy. Induction of status epilepticus in rodent models causes the appearance of intense ARC immunoreactive neurons (IAINs), which present a higher number of connections and conductance intensity than non-IAINs. This alteration might contribute to abnormal epileptic seizure activity. In this work, we investigated how IAINs connectivity influences the firing pattern and synchronization in neural networks. Firstly, we showed the appearance of synchronized burst patterns due to the emergence of IAINs. Second, we described how the increase of IAINs connectivity favors the appearance of intermittent up and down activities associated with synchronous bursts and asynchronous spikes, respectively. Once the intermittent activity was properly characterized, we applied the optogenetics control of the high synchronous activities in the intermittent regime. To do this, we considered that 1% of neurons were transfected and became photosensitive. We observed that optogenetics methods to control synchronized burst patterns are effective when IAINs are chosen as photosensitive, but not effective in non-IAINs. Therefore, our analyses suggest that IAINs play a pivotal role in both the generation and suppression of highly synchronized activities.
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Affiliation(s)
- F S Borges
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, USA; Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, SP, Brazil.
| | - E C Gabrick
- Graduate in Science Program - Physics, State University of Ponta Grossa, Ponta Grossa, PR, Brazil
| | - P R Protachevicz
- Institute of Physics, University of São Paulo, São Paulo, SP, Brazil
| | - G S V Higa
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, SP, Brazil; Institute of Chemistry, University of São Paulo, São Paulo, SP, Brazil
| | - E L Lameu
- Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
| | - P X R Rodriguez
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, SP, Brazil; Faculty of Medicine, University of Bonn, Bonn, Germany
| | - M S A Ferraz
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, SP, Brazil
| | - J D Szezech
- Graduate in Science Program - Physics, State University of Ponta Grossa, Ponta Grossa, PR, Brazil; Department of Mathematics and Statistics, State University of Ponta Grossa, Ponta Grossa, PR, Brazil
| | - A M Batista
- Graduate in Science Program - Physics, State University of Ponta Grossa, Ponta Grossa, PR, Brazil; Institute of Physics, University of São Paulo, São Paulo, SP, Brazil; Department of Mathematics and Statistics, State University of Ponta Grossa, Ponta Grossa, PR, Brazil
| | - A H Kihara
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, SP, Brazil.
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6
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Mysin I, Shubina L. Hippocampal non-theta state: The "Janus face" of information processing. Front Neural Circuits 2023; 17:1134705. [PMID: 36960401 PMCID: PMC10027749 DOI: 10.3389/fncir.2023.1134705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 02/14/2023] [Indexed: 03/09/2023] Open
Abstract
The vast majority of studies on hippocampal rhythms have been conducted on animals or humans in situations where their attention was focused on external stimuli or solving cognitive tasks. These studies formed the basis for the idea that rhythmical activity coordinates the work of neurons during information processing. However, at rest, when attention is not directed to external stimuli, brain rhythms do not disappear, although the parameters of oscillatory activity change. What is the functional load of rhythmical activity at rest? Hippocampal oscillatory activity during rest is called the non-theta state, as opposed to the theta state, a characteristic activity during active behavior. We dedicate our review to discussing the present state of the art in the research of the non-theta state. The key provisions of the review are as follows: (1) the non-theta state has its own characteristics of oscillatory and neuronal activity; (2) hippocampal non-theta state is possibly caused and maintained by change of rhythmicity of medial septal input under the influence of raphe nuclei; (3) there is no consensus in the literature about cognitive functions of the non-theta-non-ripple state; and (4) the antagonistic relationship between theta and delta rhythms observed in rodents is not always observed in humans. Most attention is paid to the non-theta-non-ripple state, since this aspect of hippocampal activity has not been investigated properly and discussed in reviews.
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7
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Fukai T. Computational models of Idling brain activity for memory processing. Neurosci Res 2022; 189:75-82. [PMID: 36592825 DOI: 10.1016/j.neures.2022.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/01/2023]
Abstract
Studying the underlying neural mechanisms of cognitive functions of the brain is one of the central questions in modern biology. Moreover, it has significantly impacted the development of novel technologies in artificial intelligence. Spontaneous activity is a unique feature of the brain and is currently lacking in many artificially constructed intelligent machines. Spontaneous activity may represent the brain's idling states, which are internally driven by neuronal networks and possibly participate in offline processing during awake, sleep, and resting states. Evidence is accumulating that the brain's spontaneous activity is not mere noise but part of the mechanisms to process information about previous experiences. A bunch of literature has shown how previous sensory and behavioral experiences influence the subsequent patterns of brain activity with various methods in various animals. It seems, however, that the patterns of neural activity and their computational roles differ significantly from area to area and from function to function. In this article, I review the various forms of the brain's spontaneous activity, especially those observed during memory processing, and some attempts to model the generation mechanisms and computational roles of such activities.
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Affiliation(s)
- Tomoki Fukai
- Okinawa Institute of Science and Technology, Tancha 1919-1, Onna-son, Okinawa 904-0495, Japan.
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8
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Via G, Baravalle R, Fernandez FR, White JA, Canavier CC. Interneuronal network model of theta-nested fast oscillations predicts differential effects of heterogeneity, gap junctions and short term depression for hyperpolarizing versus shunting inhibition. PLoS Comput Biol 2022; 18:e1010094. [PMID: 36455063 PMCID: PMC9747050 DOI: 10.1371/journal.pcbi.1010094] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 12/13/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
Theta and gamma oscillations in the hippocampus have been hypothesized to play a role in the encoding and retrieval of memories. Recently, it was shown that an intrinsic fast gamma mechanism in medial entorhinal cortex can be recruited by optogenetic stimulation at theta frequencies, which can persist with fast excitatory synaptic transmission blocked, suggesting a contribution of interneuronal network gamma (ING). We calibrated the passive and active properties of a 100-neuron model network to capture the range of passive properties and frequency/current relationships of experimentally recorded PV+ neurons in the medial entorhinal cortex (mEC). The strength and probabilities of chemical and electrical synapses were also calibrated using paired recordings, as were the kinetics and short-term depression (STD) of the chemical synapses. Gap junctions that contribute a noticeable fraction of the input resistance were required for synchrony with hyperpolarizing inhibition; these networks exhibited theta-nested high frequency oscillations similar to the putative ING observed experimentally in the optogenetically-driven PV-ChR2 mice. With STD included in the model, the network desynchronized at frequencies above ~200 Hz, so for sufficiently strong drive, fast oscillations were only observed before the peak of the theta. Because hyperpolarizing synapses provide a synchronizing drive that contributes to robustness in the presence of heterogeneity, synchronization decreases as the hyperpolarizing inhibition becomes weaker. In contrast, networks with shunting inhibition required non-physiological levels of gap junctions to synchronize using conduction delays within the measured range.
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Affiliation(s)
- Guillem Via
- Louisiana State University Health Sciences Center, Department of Cell Biology and Anatomy, New Orleans, Louisiana, United States of America
| | - Roman Baravalle
- Louisiana State University Health Sciences Center, Department of Cell Biology and Anatomy, New Orleans, Louisiana, United States of America
| | - Fernando R. Fernandez
- Department of Biomedical Engineering, Center for Systems Neuroscience, Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| | - John A. White
- Department of Biomedical Engineering, Center for Systems Neuroscience, Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| | - Carmen C. Canavier
- Louisiana State University Health Sciences Center, Department of Cell Biology and Anatomy, New Orleans, Louisiana, United States of America
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9
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Granado M, Collavini S, Baravalle R, Martinez N, Montemurro MA, Rosso OA, Montani F. High-frequency oscillations in the ripple bands and amplitude information coding: Toward a biomarker of maximum entropy in the preictal signals. CHAOS (WOODBURY, N.Y.) 2022; 32:093151. [PMID: 36182366 DOI: 10.1063/5.0101220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/29/2022] [Indexed: 06/16/2023]
Abstract
Intracranial electroencephalography (iEEG) can directly record local field potentials (LFPs) from a large set of neurons in the vicinity of the electrode. To search for possible epileptic biomarkers and to determine the epileptogenic zone that gives rise to seizures, we investigated the dynamics of basal and preictal signals. For this purpose, we explored the dynamics of the recorded time series for different frequency bands considering high-frequency oscillations (HFO) up to 240 Hz. We apply a Hilbert transform to study the amplitude and phase of the signals. The dynamics of the different frequency bands in the time causal entropy-complexity plane, H × C, is characterized by comparing the dynamical evolution of the basal and preictal time series. As the preictal states evolve closer to the time in which the epileptic seizure starts, the, H × C, dynamics changes for the higher frequency bands. The complexity evolves to very low values and the entropy becomes nearer to its maximal value. These quasi-stable states converge to equiprobable states when the entropy is maximal, and the complexity is zero. We could, therefore, speculate that in this case, it corresponds to the minimization of Gibbs free energy. In this case, the maximum entropy is equivalent to the principle of minimum consumption of resources in the system. We can interpret this as the nature of the system evolving temporally in the preictal state in such a way that the consumption of resources by the system is minimal for the amplitude in frequencies between 220-230 and 230-240 Hz.
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Affiliation(s)
- Mauro Granado
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
| | - Santiago Collavini
- Instituto de Electrónica Industrial, Control y Procesamiento de Se nales (LEICI), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP-CONICET), La Plata 1900, Buenos Aires, Argentina
| | - Roman Baravalle
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
| | - Nataniel Martinez
- Instituto de Física de Mar del Plata, Universidad Nacional de Mar del Plata & CONICET, Mar del Plata 7600, Buenos Aires, Argentina
| | - Marcelo A Montemurro
- School of Mathematics & Statistics, Faculty of Science, Technology, Engineering & Mathematics, The Open University, Walton Hall, Milton Keynes MK7 6AA, United Kingdom
| | - Osvaldo A Rosso
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
| | - Fernando Montani
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
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10
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Berners-Lee A, Feng T, Silva D, Wu X, Ambrose ER, Pfeiffer BE, Foster DJ. Hippocampal replays appear after a single experience and incorporate greater detail with more experience. Neuron 2022; 110:1829-1842.e5. [PMID: 35381188 DOI: 10.1016/j.neuron.2022.03.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 11/01/2021] [Accepted: 03/08/2022] [Indexed: 01/20/2023]
Abstract
The hippocampus is implicated in memory formation, and neurons in the hippocampus take part in replay sequences that have been proposed to reflect memory of explored space. By recording from large ensembles of hippocampal neurons as rats explored various tracks, we show that sustained replay appears after a single experience. Further, we found that with repeated experience in a novel environment, replay slows down, taking more time to traverse the same trajectory. This effect was dependent on experience, not passage of time, and was environment specific. By investigating the slow-gamma (25-50 Hz) hover-and-jump dynamics within replays, we show that replay slows by adding more hover locations, increasing the resolution of the behavioral trajectory. We provide evidence that inhibition and cortical engagement both increase as replay slows. Thus, replays can reflect single experiences and evolve with re-exposure, in a manner consistent with the encoding of greater detail into replay memories with experience.
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Affiliation(s)
- Alice Berners-Lee
- Helen Wills Neuroscience Institute and Department of Psychology, University of California Berkeley, CA 94720, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Ting Feng
- Philips Research North America, Cambridge, MA 02141, USA
| | | | - Xiaojing Wu
- New York University Comprehensive Epilepsy Center, 223 East 34th Street, New York, NY 10016, USA
| | | | - Brad E Pfeiffer
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - David J Foster
- Helen Wills Neuroscience Institute and Department of Psychology, University of California Berkeley, CA 94720, USA.
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11
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Braun W, Memmesheimer RM. High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1. PLoS Comput Biol 2022; 18:e1009891. [PMID: 35176028 PMCID: PMC8890743 DOI: 10.1371/journal.pcbi.1009891] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 03/02/2022] [Accepted: 02/02/2022] [Indexed: 11/19/2022] Open
Abstract
Hippocampal sharp wave/ripple oscillations are a prominent pattern of collective activity, which consists of a strong overall increase of activity with superimposed (140 − 200 Hz) ripple oscillations. Despite its prominence and its experimentally demonstrated importance for memory consolidation, the mechanisms underlying its generation are to date not understood. Several models assume that recurrent networks of inhibitory cells alone can explain the generation and main characteristics of the ripple oscillations. Recent experiments, however, indicate that in addition to inhibitory basket cells, the pattern requires in vivo the activity of the local population of excitatory pyramidal cells. Here, we study a model for networks in the hippocampal region CA1 incorporating such a local excitatory population of pyramidal neurons. We start by investigating its ability to generate ripple oscillations using extensive simulations. Using biologically plausible parameters, we find that short pulses of external excitation triggering excitatory cell spiking are required for sharp/wave ripple generation with oscillation patterns similar to in vivo observations. Our model has plausible values for single neuron, synapse and connectivity parameters, random connectivity and no strong feedforward drive to the inhibitory population. Specifically, whereas temporally broad excitation can lead to high-frequency oscillations in the ripple range, sparse pyramidal cell activity is only obtained with pulse-like external CA3 excitation. Further simulations indicate that such short pulses could originate from dendritic spikes in the apical or basal dendrites of CA1 pyramidal cells, which are triggered by coincident spike arrivals from hippocampal region CA3. Finally we show that replay of sequences by pyramidal neurons and ripple oscillations can arise intrinsically in CA1 due to structured connectivity that gives rise to alternating excitatory pulse and inhibitory gap coding; the latter denotes phases of silence in specific basket cell groups, which induce selective disinhibition of groups of pyramidal neurons. This general mechanism for sequence generation leads to sparse pyramidal cell and dense basket cell spiking, does not rely on synfire chain-like feedforward excitation and may be relevant for other brain regions as well.
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Affiliation(s)
- Wilhelm Braun
- Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- * E-mail: (WB); (R-MM)
| | - Raoul-Martin Memmesheimer
- Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
- * E-mail: (WB); (R-MM)
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12
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Schumm SN, Gabrieli D, Meaney DF. Plasticity impairment exposes CA3 vulnerability in a hippocampal network model of mild traumatic brain injury. Hippocampus 2022; 32:231-250. [PMID: 34978378 DOI: 10.1002/hipo.23402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/08/2021] [Accepted: 11/18/2021] [Indexed: 11/10/2022]
Abstract
Proper function of the hippocampus is critical for executing cognitive tasks such as learning and memory. Traumatic brain injury (TBI) and other neurological disorders are commonly associated with cognitive deficits and hippocampal dysfunction. Although there are many existing models of individual subregions of the hippocampus, few models attempt to integrate the primary areas into one system. In this work, we developed a computational model of the hippocampus, including the dentate gyrus, CA3, and CA1. The subregions are represented as an interconnected neuronal network, incorporating well-characterized ex vivo slice electrophysiology into the functional neuron models and well-documented anatomical connections into the network structure. In addition, since plasticity is foundational to the role of the hippocampus in learning and memory as well as necessary for studying adaptation to injury, we implemented spike-timing-dependent plasticity among the synaptic connections. Our model mimics key features of hippocampal activity, including signal frequencies in the theta and gamma bands and phase-amplitude coupling in area CA1. We also studied the effects of spike-timing-dependent plasticity impairment, a potential consequence of TBI, in our model and found that impairment decreases broadband power in CA3 and CA1 and reduces phase coherence between these two subregions, yet phase-amplitude coupling in CA1 remains intact. Altogether, our work demonstrates characteristic hippocampal activity with a scaled network model of spiking neurons and reveals the sensitive balance of plasticity mechanisms in the circuit through one manifestation of mild traumatic injury.
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Affiliation(s)
- Samantha N Schumm
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David Gabrieli
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David F Meaney
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Neurosurgery, Penn Center for Brain Injury and Repair, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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13
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A Model of the CA1 Field Rhythms. eNeuro 2021; 8:ENEURO.0192-21.2021. [PMID: 34670820 PMCID: PMC8577063 DOI: 10.1523/eneuro.0192-21.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 08/23/2021] [Accepted: 09/19/2021] [Indexed: 12/03/2022] Open
Abstract
We propose a model of the main rhythms in the hippocampal CA1 field: theta rhythm; slow, middle, and fast gamma rhythms; and ripple oscillations. We have based this on data obtained from animals behaving freely. We have considered the modes of neuronal discharges and the occurrence of local field potential oscillations in the theta and non-theta states at different inputs from the CA3 field, the medial entorhinal cortex, and the medial septum. In our work, we tried to reproduce the main experimental phenomena about rhythms in the CA1 field: the coupling of neurons to the phase of rhythms, cross-rhythm phase–phase coupling, and phase–amplitude coupling. Using computational experiments, we have proved the hypothesis that the descending phase of the theta rhythm in the CA1 field is formed by the input from the CA3 field via the Shaffer collaterals, and the ascending phase of the theta rhythm is formed by the IPSPs from CCK basket cells. The slow gamma rhythm is coupled to the descending phase of the theta rhythm, since it also depends on the arrival of the signal via the Shaffer collaterals. The middle gamma rhythm is formed by the EPSPs of the principal neurons of the third layer of the entorhinal cortex, corresponds to experimental data. We were able to unite in a single mathematical model several theoretical ideas about the mechanisms of rhythmic processes in the CA1 field of the hippocampus.
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14
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The essential role of hippocampo-cortical connections in temporal coordination of spindles and ripples. Neuroimage 2021; 243:118485. [PMID: 34425227 DOI: 10.1016/j.neuroimage.2021.118485] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 08/17/2021] [Accepted: 08/18/2021] [Indexed: 11/22/2022] Open
Abstract
The predominant activity of slow wave sleep is cortical slow oscillations (SOs), thalamic spindles and hippocampal sharp wave ripples. While the precise temporal nesting of these rhythms was shown to be essential for memory consolidation, the coordination mechanism is poorly understood. Here we develop a minimal hippocampo-cortico-thalamic network that can explain the mechanism underlying the SO-spindle-ripple coupling indicating of the succession of regional neuronal interactions. Further we verify the model predictions experimentally in naturally sleeping rodents showing our simple model provides a quantitative match to several experimental observations including the nesting of ripples in the spindle troughs and larger duration but lower amplitude of the ripples co-occurring with spindles or SOs compared to the isolated ripples. The model also predicts that the coupling of ripples to SOs and spindles monotonically enhances by increasing the strength of hippocampo-cortical connections while it is stronger at intermediate values of the cortico-hippocampal projections.
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15
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Isoflurane Suppresses Hippocampal High-frequency Ripples by Differentially Modulating Pyramidal Neurons and Interneurons in Mice. Anesthesiology 2021; 135:122-135. [PMID: 33951177 DOI: 10.1097/aln.0000000000003803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Isoflurane can induce anterograde amnesia. Hippocampal ripples are high-frequency oscillatory events occurring in the local field potentials of cornu ammonis 1 involved in memory processes. The authors hypothesized that isoflurane suppresses hippocampal ripples at a subanesthetic concentration by modulating the excitability of cornu ammonis 1 neurons. METHODS The potencies of isoflurane for memory impairment and anesthesia were measured in mice. Hippocampal ripples were measured by placing recording electrodes in the cornu ammonis 1. Effects of isoflurane on the excitability of hippocampal pyramidal neurons and interneurons were measured. A simulation model of ripples based on the firing frequency of hippocampal cornu ammonis 1 neurons was used to validate the effects of isoflurane on neuronal excitability in vitro and on ripples in vivo. RESULTS Isoflurane at 0.5%, which did not induce loss of righting reflex, impaired hippocampus-dependent fear memory by 97.4 ± 3.1% (mean ± SD; n = 14; P < 0.001). Isoflurane at 0.5% reduced ripple amplitude (38 ± 13 vs. 42 ± 13 μV; n = 9; P = 0.003), rate (462 ± 66 vs. 538 ± 81 spikes/min; n = 9; P = 0.002) and duration (36 ± 5 vs. 48 ± 9 ms; n = 9; P < 0.001) and increased the interarrival time (78 ± 7 vs. 69 ± 6 ms; n = 9; P < 0.001) and frequency (148.2 ± 3.9 vs. 145.0 ± 2.9 Hz; n = 9; P = 0.001). Isoflurane at the same concentration depressed action potential frequency in fast-spiking interneurons while slightly enhancing action potential frequency in cornu ammonis 1 pyramidal neurons. The simulated effects of isoflurane on hippocampal ripples were comparable to recordings in vivo. CONCLUSIONS The authors' results suggest that a subanesthetic concentration of isoflurane can suppress hippocampal ripples by differentially modulating the excitability of pyramidal neurons and interneurons, which may contribute to its amnestic action. EDITOR’S PERSPECTIVE
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16
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Tomar A, Polygalov D, Chattarji S, McHugh TJ. Stress enhances hippocampal neuronal synchrony and alters ripple-spike interaction. Neurobiol Stress 2021; 14:100327. [PMID: 33937446 PMCID: PMC8079661 DOI: 10.1016/j.ynstr.2021.100327] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 04/01/2021] [Accepted: 04/07/2021] [Indexed: 12/20/2022] Open
Abstract
Adverse effects of chronic stress include anxiety, depression, and memory deficits. Some of these stress-induced behavioural deficits are mediated by impaired hippocampal function. Much of our current understanding about how stress affects the hippocampus has been derived from post-mortem analyses of brain slices at fixed time points. Consequently, neural signatures of an ongoing stressful experiences in the intact brain of awake animals and their links to later hippocampal dysfunction remain poorly understood. Further, no information is available on the impact of stress on sharp-wave ripples (SPW-Rs), high frequency oscillation transients crucial for memory consolidation. Here, we used in vivo tetrode recordings to analyze the dynamic impact of 10 days of immobilization stress on neural activity in area CA1 of mice. While there was a net decrease in pyramidal cell activity in stressed animals, a greater fraction of CA1 spikes occurred specifically during sharp-wave ripples, resulting in an increase in neuronal synchrony. After repeated stress some of these alterations were visible during rest even in the absence of stress. These findings offer new insights into stress-induced changes in ripple-spike interactions and mechanisms through which chronic stress may interfere with subsequent information processing.
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Affiliation(s)
- Anupratap Tomar
- Laboratory for Circuit & Behavioral Physiology, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0021, Japan
| | - Denis Polygalov
- Laboratory for Circuit & Behavioral Physiology, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0021, Japan
| | - Sumantra Chattarji
- National Centre for Biological Sciences, Bellary Road, Bangalore, 560065, India.,Centre for Discovery Brain Sciences, Deanery of Biomedical Sciences, University of Edinburgh, Hugh Robson Building, 15 George Square, Edinburgh, EH89XD, UK
| | - Thomas J McHugh
- Laboratory for Circuit & Behavioral Physiology, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0021, Japan
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17
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He X, Li J, Zhou G, Yang J, McKenzie S, Li Y, Li W, Yu J, Wang Y, Qu J, Wu Z, Hu H, Duan S, Ma H. Gating of hippocampal rhythms and memory by synaptic plasticity in inhibitory interneurons. Neuron 2021; 109:1013-1028.e9. [PMID: 33548174 DOI: 10.1016/j.neuron.2021.01.014] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 12/17/2020] [Accepted: 01/14/2021] [Indexed: 12/31/2022]
Abstract
Mental experiences can become long-term memories if the hippocampal activity patterns that encode them are broadcast during network oscillations. The activity of inhibitory neurons is essential for generating these neural oscillations, but molecular control of this dynamic process during learning remains unknown. Here, we show that hippocampal oscillatory strength positively correlates with excitatory monosynaptic drive onto inhibitory neurons (E→I) in freely behaving mice. To establish a causal relationship between them, we identified γCaMKII as the long-sought mediator of long-term potentiation for E→I synapses (LTPE→I), which enabled the genetic manipulation of experience-dependent E→I synaptic input/plasticity. Deleting γCaMKII in parvalbumin interneurons selectively eliminated LTPE→I and disrupted experience-driven strengthening in theta and gamma rhythmicity. Behaviorally, this manipulation impaired long-term memory, for which the kinase activity of γCaMKII was required. Taken together, our data suggest that E→I synaptic plasticity, exemplified by LTPE→I, plays a gatekeeping role in tuning experience-dependent brain rhythms and mnemonic function.
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Affiliation(s)
- Xingzhi He
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jiarui Li
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Guangjun Zhou
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jing Yang
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Sam McKenzie
- Department of Neurosciences, University of New Mexico, Albuquerque, NM 87131, USA
| | - Yanjun Li
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wenwen Li
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jun Yu
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yang Wang
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jing Qu
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhiying Wu
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hailan Hu
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China; Research Units for Emotion and Emotion Disorders, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Shumin Duan
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China; Research Units for Emotion and Emotion Disorders, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Huan Ma
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China; Research Units for Emotion and Emotion Disorders, Chinese Academy of Medical Sciences, Beijing 100730, China.
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18
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Sanda P, Malerba P, Jiang X, Krishnan GP, Gonzalez-Martinez J, Halgren E, Bazhenov M. Bidirectional Interaction of Hippocampal Ripples and Cortical Slow Waves Leads to Coordinated Spiking Activity During NREM Sleep. Cereb Cortex 2021; 31:324-340. [PMID: 32995860 PMCID: PMC8179633 DOI: 10.1093/cercor/bhaa228] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 06/19/2020] [Accepted: 07/16/2020] [Indexed: 01/17/2023] Open
Abstract
The dialogue between cortex and hippocampus is known to be crucial for sleep-dependent memory consolidation. During slow wave sleep, memory replay depends on slow oscillation (SO) and spindles in the (neo)cortex and sharp wave-ripples (SWRs) in the hippocampus. The mechanisms underlying interaction of these rhythms are poorly understood. We examined the interaction between cortical SO and hippocampal SWRs in a model of the hippocampo-cortico-thalamic network and compared the results with human intracranial recordings during sleep. We observed that ripple occurrence peaked following the onset of an Up-state of SO and that cortical input to hippocampus was crucial to maintain this relationship. A small fraction of ripples occurred during the Down-state and controlled initiation of the next Up-state. We observed that the effect of ripple depends on its precise timing, which supports the idea that ripples occurring at different phases of SO might serve different functions, particularly in the context of encoding the new and reactivation of the old memories during memory consolidation. The study revealed complex bidirectional interaction of SWRs and SO in which early hippocampal ripples influence transitions to Up-state, while cortical Up-states control occurrence of the later ripples, which in turn influence transition to Down-state.
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Affiliation(s)
- Pavel Sanda
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Institute of Computer Science of the Czech Academy of Sciences, Prague 18207, Czech Republic
| | - Paola Malerba
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Battelle Center for Mathematical Medicine, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215, USA
- Department of Pediatrics and Biophysics Graduate Program, Ohio State University, Columbus, OH 43215, USA
| | - Xi Jiang
- Neurosciences Graduate Program, University of California, San Diego, La Jolla 92093, USA
- Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB T1K4G9, Canada
| | - Giri P Krishnan
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - Eric Halgren
- Neurosciences Graduate Program, University of California, San Diego, La Jolla 92093, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Maxim Bazhenov
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Neurosciences Graduate Program, University of California, San Diego, La Jolla 92093, USA
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19
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Howe T, Blockeel AJ, Taylor H, Jones MW, Bazhenov M, Malerba P. NMDA receptors promote hippocampal sharp-wave ripples and the associated coactivity of CA1 pyramidal cells. Hippocampus 2020; 30:1356-1370. [PMID: 33112474 PMCID: PMC8645203 DOI: 10.1002/hipo.23276] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 10/05/2020] [Accepted: 10/15/2020] [Indexed: 10/10/2023]
Abstract
Hippocampal sharp-wave ripples (SWRs) support the reactivation of memory representations, relaying information to neocortex during "offline" and sleep-dependent memory consolidation. While blockade of NMDA receptors (NMDAR) is known to affect both learning and subsequent consolidation, the specific contributions of NMDAR activation to SWR-associated activity remain unclear. Here, we combine biophysical modeling with in vivo local field potential (LFP) and unit recording to quantify changes in SWR dynamics following inactivation of NMDAR. In a biophysical model of CA3-CA1 SWR activity, we find that NMDAR removal leads to reduced SWR density, but spares SWR properties such as duration, cell recruitment and ripple frequency. These predictions are confirmed by experiments in which NMDAR-mediated transmission in rats was inhibited using three different NMDAR antagonists, while recording dorsal CA1 LFP. In the model, loss of NMDAR-mediated conductances also induced a reduction in the proportion of cell pairs that co-activate significantly above chance across multiple events. Again, this prediction is corroborated by dorsal CA1 single-unit recordings, where the NMDAR blocker ketamine disrupted correlated spiking during SWR. Our results are consistent with a framework in which NMDA receptors both promote activation of SWR events and organize SWR-associated spiking content. This suggests that, while SWR are short-lived events emerging in fast excitatory-inhibitory networks, slower network components including NMDAR-mediated currents contribute to ripple density and promote consistency in the spiking content across ripples, underpinning mechanisms for fine-tuning of memory consolidation processes.
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Affiliation(s)
- Timothy Howe
- School of Physiology, Pharmacology and Neuroscience,
University of Bristol, Bristol, UK
| | - Anthony J. Blockeel
- School of Physiology, Pharmacology and Neuroscience,
University of Bristol, Bristol, UK
| | - Hannah Taylor
- School of Physiology, Pharmacology and Neuroscience,
University of Bristol, Bristol, UK
| | - Matthew W. Jones
- School of Physiology, Pharmacology and Neuroscience,
University of Bristol, Bristol, UK
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego,
La Jolla, California
| | - Paola Malerba
- School of Physiology, Pharmacology and Neuroscience,
University of Bristol, Bristol, UK
- Battelle Center for Mathematical Medicine, Columbus,
Ohio
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20
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McKenzie S, Nitzan N, English DF. Mechanisms of neural organization and rhythmogenesis during hippocampal and cortical ripples. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190237. [PMID: 32248777 PMCID: PMC7209923 DOI: 10.1098/rstb.2019.0237] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/26/2019] [Indexed: 12/19/2022] Open
Abstract
Neural activity during ripples has attracted great theoretical and experimental attention over the last three decades. Perhaps one reason for such interest is that ripples occur during quiet waking moments and during sleep, times when we reflect and dream about what has just occurred and what we expect to happen next. The hope is that understanding such 'offline' activity may yield insights into reflection, planning, and the purposes of sleep. This review focuses on the mechanisms by which neurons organize during these high-frequency events. In studying ripples, broader principles have emerged that relate intrinsic neural properties, network topology and synaptic plasticity in controlling neural activity. Ripples, therefore, serve as an excellent model for studying how properties of a neural network relate to neural dynamics. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.
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Affiliation(s)
- Sam McKenzie
- NYULMC Neuroscience Institute, New York, NY, USA
| | - Noam Nitzan
- Neuroscience Research Center NWFZ, Berlin, Germany
| | - Daniel F. English
- Virginia Tech School of Neuroscience Blacksburg, Blacksburg, VA, USA
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21
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Liu X, Kuzum D. Hippocampal-Cortical Memory Trace Transfer and Reactivation Through Cell-Specific Stimulus and Spontaneous Background Noise. Front Comput Neurosci 2019; 13:67. [PMID: 31680922 PMCID: PMC6798041 DOI: 10.3389/fncom.2019.00067] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 09/10/2019] [Indexed: 01/07/2023] Open
Abstract
The hippocampus plays important roles in memory formation and retrieval through sharp-wave-ripples. Recent studies have shown that certain neuron populations in the prefrontal cortex (PFC) exhibit coordinated reactivations during awake ripple events. These experimental findings suggest that the awake ripple is an important biomarker, through which the hippocampus interacts with the neocortex to assist memory formation and retrieval. However, the computational mechanisms of this ripple based hippocampal-cortical coordination are still not clear due to the lack of unified models that include both the hippocampal and cortical networks. In this work, using a coupled biophysical model of both CA1 and PFC, we investigate possible mechanisms of hippocampal-cortical memory trace transfer and the conditions that assist reactivation of the transferred memory traces in the PFC. To validate our model, we first show that the local field potentials generated in the hippocampus and PFC exhibit ripple range activities that are consistent with the recent experimental studies. Then we demonstrate that during ripples, sequence replays can successfully transfer the information stored in the hippocampus to the PFC recurrent networks. We investigate possible mechanisms of memory retrieval in PFC networks. Our results suggest that the stored memory traces in the PFC network can be retrieved through two different mechanisms, namely the cell-specific input representing external stimuli and non-specific spontaneous background noise representing spontaneous memory recall events. Importantly, in both cases, the memory reactivation quality is robust to network connection loss. Finally, we find out that the quality of sequence reactivations is enhanced by both increased number of SWRs and an optimal background noise intensity, which tunes the excitability of neurons to a proper level. Our study presents a mechanistic explanation for the memory trace transfer from the hippocampus to neocortex through ripple coupling in awake states and reports two different mechanisms by which the stored memory traces can be successfully retrieved.
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Affiliation(s)
- Xin Liu
- Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, United States
| | - Duygu Kuzum
- Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, United States
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22
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Tokuda K, Katori Y, Aihara K. Chaotic dynamics as a mechanism of rapid transition of hippocampal local field activity between theta and non-theta states. CHAOS (WOODBURY, N.Y.) 2019; 29:113115. [PMID: 31779345 DOI: 10.1063/1.5110327] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 10/21/2019] [Indexed: 06/10/2023]
Abstract
We propose a dynamical model of the local hippocampal circuit realizing the transition between the theta and non-theta states. We model the interaction between hippocampal local rhythm generators and the external periodic input from the medial septum and diagonal band of Broca (MS-DBB). With our model, bifurcation of the nonlinear dynamics serves as a mechanism that realizes two distinctive oscillations in the hippocampus, where the amplitude of the oscillatory input from the MS-DBB works as a bifurcation parameter. We model the network of the hippocampal interneurons with a network of simple class 1 neuron models connected mutually with gap junctions. The model neurons exhibit highly synchronous periodic oscillations under the existence of an external force from the MS-DBB, just as the real hippocampus shows theta oscillation under the rhythmic input from the MS-DBB. The model shows diffusion-induced chaotic dynamics under an aperiodic MS-DBB activity, just as the large amplitude irregular activity appears following the disappearance of the rhythmicity of the MS-DBB neurons in the real brain. The model is consistent with both previous experimental findings reporting the existence of local rhythm generators in the hippocampus and the executive role of the MS-DBB in synchronizing theta oscillation in vivo. Our model also replicates the traveling waves of theta oscillations in two-dimensionally coupled networks.
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Affiliation(s)
- Keita Tokuda
- Department of Pharmacy, Faculty of Medicine, The University of Tokyo Hospital, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yuichi Katori
- School of Systems Information Science, Future University Hakodate, 116-2 Kamedanakano-cho, Hakodate, Hokkaido 041-8655, Japan
| | - Kazuyuki Aihara
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
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23
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Malerba P, Rulkov NF, Bazhenov M. Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3. COMMUNICATIONS IN NONLINEAR SCIENCE & NUMERICAL SIMULATION 2019; 72:162-175. [PMID: 33814862 PMCID: PMC8015963 DOI: 10.1016/j.cnsns.2018.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Reduced models of neuronal spiking activity simulated with a fixed integration time are frequently used in studies of spatio-temporal dynamics of neurobiological networks. The choice of fixed time step integration provides computational simplicity and efficiency, especially in cases dealing with large number of neurons and synapses operating at a different level of activity across the population at any given time. A network model tuned to generate a particular type of oscillations or wave patterns is sensitive to the intrinsic properties of neurons and synapses and, therefore, commonly susceptible to changes the time step of integration. In this study, we analyzed a model of sharp-wave activity in the network of hippocampal area CA3, to examine how an increase of the integration time step affects network behavior and to propose adjustments of intrinsic properties neurons and synapses that help minimize or remove the damage caused by the time step increase.
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Affiliation(s)
- Paola Malerba
- Department of Medicine, University of California San Diego,
9500 Gilman Drive, La Jolla, CA 92093, United States
- Department of Cognitive Sciences, University of California
Irvine, Irvine, CA 92697-5100, United States
| | - Nikolai F. Rulkov
- BioCircuits Institute, University of California San Diego,
9500 Gilman Drive, La Jolla, CA 92093, United States
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego,
9500 Gilman Drive, La Jolla, CA 92093, United States
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24
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Melonakos ED, White JA, Fernandez FR. A model of cholinergic suppression of hippocampal ripples through disruption of balanced excitation/inhibition. Hippocampus 2018; 29:773-786. [PMID: 30417958 DOI: 10.1002/hipo.23051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 10/02/2018] [Accepted: 10/31/2018] [Indexed: 11/11/2022]
Abstract
Sharp wave-ripples (140-220 Hz) are patterns of brain activity observed in the local field potential of the hippocampus which are present during memory consolidation. As rodents switch from memory consolidation to memory encoding behaviors, cholinergic inputs to the hippocampus from neurons in the medial septum-diagonal band of Broca cause a marked reduction in ripple incidence. The mechanism for this disruption in ripple power is not fully understood. In isolated neurons, the major effect of cholinergic input on hippocampal neurons is depolarization of the membrane potential, which affects both hippocampal pyramidal neurons and inhibitory interneurons. Using an existing model of ripple-frequency oscillations that includes both pyramidal neurons and interneurons, we investigated the mechanism whereby depolarizing inputs to these neurons can affect ripple power and frequency. We observed that ripple power and frequency are maintained, as long as inputs to pyramidal neurons and interneurons are balanced. Preferential drive to pyramidal neurons or interneurons, however, affects ripple power and can disrupt ripple oscillations by pushing ripple frequency higher or lower. Thus, an imbalance in drive to pyramidal neurons and interneurons provides a means whereby cholinergic input can suppress hippocampal ripples.
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Affiliation(s)
- Eric D Melonakos
- Department of Bioengineering, University of Utah, Salt Lake City, Utah
| | - John A White
- Department of Bioengineering, University of Utah, Salt Lake City, Utah.,Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Fernando R Fernandez
- Department of Bioengineering, University of Utah, Salt Lake City, Utah.,Department of Biomedical Engineering, Boston University, Boston, Massachusetts
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25
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Ramirez-Villegas JF, Willeke KF, Logothetis NK, Besserve M. Dissecting the Synapse- and Frequency-Dependent Network Mechanisms of In Vivo Hippocampal Sharp Wave-Ripples. Neuron 2018; 100:1224-1240.e13. [DOI: 10.1016/j.neuron.2018.09.041] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 06/25/2018] [Accepted: 09/24/2018] [Indexed: 01/14/2023]
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26
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Weissenberger F, Einarsson H, Matheus Gauy M, Meier F, Mujika A, Lengler J, Steger A. On the origin of lognormal network synchrony in CA1. Hippocampus 2018; 28:824-837. [PMID: 30024075 DOI: 10.1002/hipo.23004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 06/08/2018] [Accepted: 06/19/2018] [Indexed: 11/12/2022]
Abstract
The sharp wave ripple complex in rodent hippocampus is associated with a network burst in CA3 (NB) that triggers a synchronous event in the CA1 population (SE). The number of CA1 pyramidal cells participating in a SE has been observed to follow a lognormal distribution. However, the origin of this skewed and heavy-tailed distribution of population synchrony in CA1 remains unknown. Because the size of SEs is likely to originate from the size of the NBs and the underlying neural circuitry, we model the CA3-CA1 circuit to study the underlying mechanisms and their functional implications. We show analytically that if the size of a NB in CA3 is distributed according to a normal distribution, then the size of the resulting SE in CA1 follows a lognormal distribution. Our model predicts the distribution of the NB size in CA3, which remains to be tested experimentally. Moreover, we show that a putative lognormal NB size distribution leads to an extremely heavy-tailed SE size distribution in CA1, contradicting experimental evidence. In conclusion, our model provides general insight on the origin of lognormally distributed network synchrony as a consequence of synchronous synaptic transmission of normally distributed input events.
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Affiliation(s)
| | | | | | | | - Asier Mujika
- Department of Computer Science, Zürich, Switzerland
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27
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Malerba P, Bazhenov M. Circuit mechanisms of hippocampal reactivation during sleep. Neurobiol Learn Mem 2018; 160:98-107. [PMID: 29723670 DOI: 10.1016/j.nlm.2018.04.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 03/13/2018] [Accepted: 04/30/2018] [Indexed: 10/17/2022]
Abstract
The hippocampus is important for memory and learning, being a brain site where initial memories are formed and where sharp wave - ripples (SWR) are found, which are responsible for mapping recent memories to long-term storage during sleep-related memory replay. While this conceptual schema is well established, specific intrinsic and network-level mechanisms driving spatio-temporal patterns of hippocampal activity during sleep, and specifically controlling off-line memory reactivation are unknown. In this study, we discuss a model of hippocampal CA1-CA3 network generating spontaneous characteristic SWR activity. Our study predicts the properties of CA3 input which are necessary for successful CA1 ripple generation and the role of synaptic interactions and intrinsic excitability in spike sequence replay during SWRs. Specifically, we found that excitatory synaptic connections promote reactivation in both CA3 and CA1, but the different dynamics of sharp waves in CA3 and ripples in CA1 result in a differential role for synaptic inhibition in modulating replay: promoting spike sequence specificity in CA3 but not in CA1 areas. Finally, we describe how awake learning of spatial trajectories leads to synaptic changes sufficient to drive hippocampal cells' reactivation during sleep, as required for sleep-related memory consolidation.
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Affiliation(s)
- Paola Malerba
- Department of Medicine, University of California San Diego, United States
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, United States.
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28
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Hippocampal Ripple Oscillations and Inhibition-First Network Models: Frequency Dynamics and Response to GABA Modulators. J Neurosci 2018; 38:3124-3146. [PMID: 29453207 DOI: 10.1523/jneurosci.0188-17.2018] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 01/25/2018] [Accepted: 02/05/2018] [Indexed: 11/21/2022] Open
Abstract
Hippocampal ripples are involved in memory consolidation, but the mechanisms underlying their generation remain unclear. Models relying on interneuron networks in the CA1 region disagree on the predominant source of excitation to interneurons: either "direct," via the Schaffer collaterals that provide feedforward input from CA3 to CA1, or "indirect," via the local pyramidal cells in CA1, which are embedded in a recurrent excitatory-inhibitory network. Here, we used physiologically constrained computational models of basket-cell networks to investigate how they respond to different conditions of transient, noisy excitation. We found that direct excitation of interneurons could evoke ripples (140-220 Hz) that exhibited intraripple frequency accommodation and were frequency-insensitive to GABA modulators, as previously shown in in vitro experiments. In addition, the indirect excitation of the basket-cell network enabled the expression of intraripple frequency accommodation in the fast-gamma range (90-140 Hz), as in vivo In our model, intraripple frequency accommodation results from a hysteresis phenomenon in which the frequency responds differentially to the rising and descending phases of the transient excitation. Such a phenomenon predicts a maximum oscillation frequency occurring several milliseconds before the peak of excitation. We confirmed this prediction for ripples in brain slices from male mice. These results suggest that ripple and fast-gamma episodes are produced by the same interneuron network that is recruited via different excitatory input pathways, which could be supported by the previously reported intralaminar connectivity bias between basket cells and functionally distinct subpopulations of pyramidal cells in CA1. Together, our findings unify competing inhibition-first models of rhythm generation in the hippocampus.SIGNIFICANCE STATEMENT The hippocampus is a part of the brain of humans and other mammals that is critical for the acquisition and consolidation of memories. During deep sleep and resting periods, the hippocampus generates high-frequency (∼200 Hz) oscillations called ripples, which are important for memory consolidation. The mechanisms underlying ripple generation are not well understood. A prominent hypothesis holds that the ripples are generated by local recurrent networks of inhibitory neurons. Using computational models and experiments in brain slices from rodents, we show that the dynamics of interneuron networks clarify several previously unexplained characteristics of ripple oscillations, which advances our understanding of hippocampus-dependent memory consolidation.
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29
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Sun ZY, Bozzelli PL, Caccavano A, Allen M, Balmuth J, Vicini S, Wu JY, Conant K. Disruption of perineuronal nets increases the frequency of sharp wave ripple events. Hippocampus 2017; 28:42-52. [PMID: 28921856 DOI: 10.1002/hipo.22804] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 08/22/2017] [Accepted: 09/13/2017] [Indexed: 12/30/2022]
Abstract
Hippocampal sharp wave ripples (SWRs) represent irregularly occurring synchronous neuronal population events that are observed during phases of rest and slow wave sleep. SWR activity that follows learning involves sequential replay of training-associated neuronal assemblies and is critical for systems level memory consolidation. SWRs are initiated by CA2 or CA3 pyramidal cells (PCs) and require initial excitation of CA1 PCs as well as participation of parvalbumin (PV) expressing fast spiking (FS) inhibitory interneurons. These interneurons are relatively unique in that they represent the major neuronal cell type known to be surrounded by perineuronal nets (PNNs), lattice like structures composed of a hyaluronin backbone that surround the cell soma and proximal dendrites. Though the function of the PNN is not completely understood, previous studies suggest it may serve to localize glutamatergic input to synaptic contacts and thus influence the activity of ensheathed cells. Noting that FS PV interneurons impact the activity of PCs thought to initiate SWRs, and that their activity is critical to ripple expression, we examine the effects of PNN integrity on SWR activity in the hippocampus. Extracellular recordings from the stratum radiatum of horizontal murine hippocampal hemisections demonstrate SWRs that occur spontaneously in CA1. As compared with vehicle, pre-treatment (120 min) of paired hemislices with hyaluronidase, which cleaves the hyaluronin backbone of the PNN, decreases PNN integrity and increases SWR frequency. Pre-treatment with chondroitinase, which cleaves PNN side chains, also increases SWR frequency. Together, these data contribute to an emerging appreciation of extracellular matrix as a regulator of neuronal plasticity and suggest that one function of mature perineuronal nets could be to modulate the frequency of SWR events.
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Affiliation(s)
- Zhi Yong Sun
- Jilin Women and Children's Health Hospital, Changchun, Jilin, China
| | - P Lorenzo Bozzelli
- Department of Neuroscience, Georgetown University School of Medicine, Washington, District of Columbia.,Interdisciplinary Program in Neuroscience, Georgetown University School of Medicine, Washington, District of Columbia
| | - Adam Caccavano
- Interdisciplinary Program in Neuroscience, Georgetown University School of Medicine, Washington, District of Columbia.,Department of Pharmacology, Georgetown University School of Medicine, Washington, District of Columbia
| | - Megan Allen
- Department of Neuroscience, Georgetown University School of Medicine, Washington, District of Columbia.,Interdisciplinary Program in Neuroscience, Georgetown University School of Medicine, Washington, District of Columbia
| | - Jason Balmuth
- Applied Physics Laboratory, Johns Hopkins University, Baltimore, Maryland
| | - Stefano Vicini
- Interdisciplinary Program in Neuroscience, Georgetown University School of Medicine, Washington, District of Columbia.,Department of Pharmacology, Georgetown University School of Medicine, Washington, District of Columbia
| | - Jian-Young Wu
- Department of Neuroscience, Georgetown University School of Medicine, Washington, District of Columbia.,Interdisciplinary Program in Neuroscience, Georgetown University School of Medicine, Washington, District of Columbia
| | - Katherine Conant
- Department of Neuroscience, Georgetown University School of Medicine, Washington, District of Columbia.,Interdisciplinary Program in Neuroscience, Georgetown University School of Medicine, Washington, District of Columbia
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30
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Canakci S, Toy MF, Inci AF, Liu X, Kuzum D. Computational analysis of network activity and spatial reach of sharp wave-ripples. PLoS One 2017; 12:e0184542. [PMID: 28915251 PMCID: PMC5600383 DOI: 10.1371/journal.pone.0184542] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Accepted: 08/25/2017] [Indexed: 11/19/2022] Open
Abstract
Network oscillations of different frequencies, durations and amplitudes are hypothesized to coordinate information processing and transfer across brain areas. Among these oscillations, hippocampal sharp wave-ripple complexes (SPW-Rs) are one of the most prominent. SPW-Rs occurring in the hippocampus are suggested to play essential roles in memory consolidation as well as information transfer to the neocortex. To-date, most of the knowledge about SPW-Rs comes from experimental studies averaging responses from neuronal populations monitored by conventional microelectrodes. In this work, we investigate spatiotemporal characteristics of SPW-Rs and how microelectrode size and distance influence SPW-R recordings using a biophysical model of hippocampus. We also explore contributions from neuronal spikes and synaptic potentials to SPW-Rs based on two different types of network activity. Our study suggests that neuronal spikes from pyramidal cells contribute significantly to ripples while high amplitude sharp waves mainly arise from synaptic activity. Our simulations on spatial reach of SPW-Rs show that the amplitudes of sharp waves and ripples exhibit a steep decrease with distance from the network and this effect is more prominent for smaller area electrodes. Furthermore, the amplitude of the signal decreases strongly with increasing electrode surface area as a result of averaging. The relative decrease is more pronounced when the recording electrode is closer to the source of the activity. Through simulations of field potentials across a high-density microelectrode array, we demonstrate the importance of finding the ideal spatial resolution for capturing SPW-Rs with great sensitivity. Our work provides insights on contributions from spikes and synaptic potentials to SPW-Rs and describes the effect of measurement configuration on LFPs to guide experimental studies towards improved SPW-R recordings.
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Affiliation(s)
- Sadullah Canakci
- Electrical and Computer Engineering Department, Boston University, Boston, Massachusetts, United States of America
| | - Muhammed Faruk Toy
- Electrical and Computer Engineering Department, University of California San Diego, La Jolla, California, United States of America
- Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey
| | - Ahmet Fatih Inci
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Xin Liu
- Electrical and Computer Engineering Department, University of California San Diego, La Jolla, California, United States of America
| | - Duygu Kuzum
- Electrical and Computer Engineering Department, University of California San Diego, La Jolla, California, United States of America
- * E-mail:
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31
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Dorsoventral and Proximodistal Hippocampal Processing Account for the Influences of Sleep and Context on Memory (Re)consolidation: A Connectionist Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:8091780. [PMID: 28757864 PMCID: PMC5512097 DOI: 10.1155/2017/8091780] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 05/23/2017] [Accepted: 06/01/2017] [Indexed: 11/23/2022]
Abstract
The context in which learning occurs is sufficient to reconsolidate stored memories and neuronal reactivation may be crucial to memory consolidation during sleep. The mechanisms of context-dependent and sleep-dependent memory (re)consolidation are unknown but involve the hippocampus. We simulated memory (re)consolidation using a connectionist model of the hippocampus that explicitly accounted for its dorsoventral organization and for CA1 proximodistal processing. Replicating human and rodent (re)consolidation studies yielded the following results. (1) Semantic overlap between memory items and extraneous learning was necessary to explain experimental data and depended crucially on the recurrent networks of dorsal but not ventral CA3. (2) Stimulus-free, sleep-induced internal reactivations of memory patterns produced heterogeneous recruitment of memory items and protected memories from subsequent interference. These simulations further suggested that the decrease in memory resilience when subjects were not allowed to sleep following learning was primarily due to extraneous learning. (3) Partial exposure to the learning context during simulated sleep (i.e., targeted memory reactivation) uniformly increased memory item reactivation and enhanced subsequent recall. Altogether, these results show that the dorsoventral and proximodistal organization of the hippocampus may be important components of the neural mechanisms for context-based and sleep-based memory (re)consolidations.
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32
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Memory replay in balanced recurrent networks. PLoS Comput Biol 2017; 13:e1005359. [PMID: 28135266 PMCID: PMC5305273 DOI: 10.1371/journal.pcbi.1005359] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Revised: 02/13/2017] [Accepted: 01/09/2017] [Indexed: 11/19/2022] Open
Abstract
Complex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global-potentially neuromodulatory-alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.
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