1
|
Chen ZS, Wilson MA. How our understanding of memory replay evolves. J Neurophysiol 2023; 129:552-580. [PMID: 36752404 PMCID: PMC9988534 DOI: 10.1152/jn.00454.2022] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 02/09/2023] Open
Abstract
Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.
Collapse
Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, United States
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, New York, United States
- Neuroscience Institute, New York University Grossman School of Medicine, New York, New York, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York, United States
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| |
Collapse
|
2
|
Cao L, Varga V, Chen ZS. Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials. CELL REPORTS METHODS 2021; 1:100101. [PMID: 34888543 PMCID: PMC8654278 DOI: 10.1016/j.crmeth.2021.100101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 07/27/2021] [Accepted: 09/28/2021] [Indexed: 12/23/2022]
Abstract
Spatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze runs, immobility, and sleep. Here, we show that multisite hippocampal field potential amplitude at ultra-high-frequency band (FPAuhf), a generalized form of multiunit activity, provides not only a fast and reliable reconstruction of the rodent's position when awake, but also a readout of replay content during sharp-wave ripples. This FPAuhf feature may serve as a robust real-time decoding strategy from large-scale recordings in closed-loop experiments. Furthermore, we develop unsupervised learning approaches to extract low-dimensional spatiotemporal FPAuhf features during run and ripple periods and to infer latent dynamical structures from lower-rank FPAuhf features. We also develop an optical flow-based method to identify propagating spatiotemporal LFP patterns from multisite array recordings, which can be used as a decoding application. Finally, we develop a prospective decoding strategy to predict an animal's future decision in goal-directed navigation.
Collapse
Affiliation(s)
- Liang Cao
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Physics, East China Normal University, Shanghai 200241, China
| | - Viktor Varga
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Institute of Experimental Medicine, 43 Szigony Street, 1083 Budapest, Hungary
| | - Zhe S. Chen
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| |
Collapse
|
3
|
Climer JR, Dombeck DA. Information Theoretic Approaches to Deciphering the Neural Code with Functional Fluorescence Imaging. eNeuro 2021; 8:ENEURO.0266-21.2021. [PMID: 34433574 PMCID: PMC8474651 DOI: 10.1523/eneuro.0266-21.2021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/22/2021] [Accepted: 08/04/2021] [Indexed: 11/21/2022] Open
Abstract
Information theoretic metrics have proven useful in quantifying the relationship between behaviorally relevant parameters and neuronal activity with relatively few assumptions. However, these metrics are typically applied to action potential (AP) recordings and were not designed for the slow timescales and variable amplitudes typical of functional fluorescence recordings (e.g., calcium imaging). The lack of research guidelines on how to apply and interpret these metrics with fluorescence traces means the neuroscience community has yet to realize the power of information theoretic metrics. Here, we used computational methods to create mock AP traces with known amounts of information. From these, we generated fluorescence traces and examined the ability of different information metrics to recover the known information values. We provide guidelines for how to use information metrics when applying them to functional fluorescence and demonstrate their appropriate application to GCaMP6f population recordings from mouse hippocampal neurons imaged during virtual navigation.
Collapse
Affiliation(s)
- Jason R Climer
- Department of Neurobiology, Northwestern University, Evanston, 60208 IL
| | - Daniel A Dombeck
- Department of Neurobiology, Northwestern University, Evanston, 60208 IL
| |
Collapse
|
4
|
Tu M, Zhao R, Adler A, Gan WB, Chen ZS. Efficient Position Decoding Methods Based on Fluorescence Calcium Imaging in the Mouse Hippocampus. Neural Comput 2020; 32:1144-1167. [PMID: 32343646 PMCID: PMC8011981 DOI: 10.1162/neco_a_01281] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Large-scale fluorescence calcium imaging methods have become widely adopted for studies of long-term hippocampal and cortical neuronal dynamics. Pyramidal neurons of the rodent hippocampus show spatial tuning in freely foraging or head-fixed navigation tasks. Development of efficient neural decoding methods for reconstructing the animal's position in real or virtual environments can provide a fast readout of spatial representations in closed-loop neuroscience experiments. Here, we develop an efficient strategy to extract features from fluorescence calcium imaging traces and further decode the animal's position. We validate our spike inference-free decoding methods in multiple in vivo calcium imaging recordings of the mouse hippocampus based on both supervised and unsupervised decoding analyses. We systematically investigate the decoding performance of our proposed methods with respect to the number of neurons, imaging frame rate, and signal-to-noise ratio. Our proposed supervised decoding analysis is ultrafast and robust, and thereby appealing for real-time position decoding applications based on calcium imaging.
Collapse
Affiliation(s)
- Mengyu Tu
- Department of Psychiatry, New York University School of Medicine, New York, NY 10016, U.S.A., and Nanyang Technological University, 639798, Singapore
| | - Ruohe Zhao
- Skirball Institute, Department of Neuroscience and Physiology and Department of Anesthesiology, New York University School of Medicine, New York, NY 10016, U.S.A., and Key Laboratory of Chemical Genomics, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Avital Adler
- Skirball Institute, Department of Neuroscience and Physiology and Department of Anesthesiology, New York University School of Medicine, New York, NY 10016, U.S.A.
| | - Wen-Biao Gan
- Skirball Institute, Department of Neuroscience and Physiology, Department of Anesthesiology, and Neuroscience Institute, New York University School of Medicine, New York, NY 10016, U.S.A.
| | - Zhe S Chen
- Department of Psychiatry and Neuroscience Institute, New York University School of Medicine, New York, NY 10016, U.S.A.
| |
Collapse
|
5
|
van der Meer MAA, Kemere C, Diba K. Progress and issues in second-order analysis of hippocampal replay. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190238. [PMID: 32248780 DOI: 10.1098/rstb.2019.0238] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Patterns of neural activity that occur spontaneously during sharp-wave ripple (SWR) events in the hippocampus are thought to play an important role in memory formation, consolidation and retrieval. Typical studies examining the content of SWRs seek to determine whether the identity and/or temporal order of cell firing is different from chance. Such 'first-order' analyses are focused on a single time point and template (map), and have been used to show, for instance, the existence of preplay. The major methodological challenge in first-order analyses is the construction and interpretation of different chance distributions. By contrast, 'second-order' analyses involve a comparison of SWR content between different time points, and/or between different templates. Typical second-order questions include tests of experience-dependence (replay) that compare SWR content before and after experience, and comparisons or replay between different arms of a maze. Such questions entail additional methodological challenges that can lead to biases in results and associated interpretations. We provide an inventory of analysis challenges for second-order questions about SWR content, and suggest ways of preventing, identifying and addressing possible analysis biases. Given evolving interest in understanding SWR content in more complex experimental scenarios and across different time scales, we expect these issues to become increasingly pervasive. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.
Collapse
Affiliation(s)
| | - Caleb Kemere
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Kamran Diba
- Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA.,Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| |
Collapse
|
6
|
Zhang L, Lee J, Rozell C, Singer AC. Sub-second dynamics of theta-gamma coupling in hippocampal CA1. eLife 2019; 8:44320. [PMID: 31355744 PMCID: PMC6684317 DOI: 10.7554/elife.44320] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 07/28/2019] [Indexed: 01/03/2023] Open
Abstract
Oscillatory brain activity reflects different internal brain states including neurons’ excitatory state and synchrony among neurons. However, characterizing these states is complicated by the fact that different oscillations are often coupled, such as gamma oscillations nested in theta in the hippocampus, and changes in coupling are thought to reflect distinct states. Here, we describe a new method to separate single oscillatory cycles into distinct states based on frequency and phase coupling. Using this method, we identified four theta-gamma coupling states in rat hippocampal CA1. These states differed in abundance across behaviors, phase synchrony with other hippocampal subregions, and neural coding properties suggesting that these states are functionally distinct. We captured cycle-to-cycle changes in oscillatory coupling states and found frequent switching between theta-gamma states showing that the hippocampus rapidly shifts between different functional states. This method provides a new approach to investigate oscillatory brain dynamics broadly.
Collapse
Affiliation(s)
- Lu Zhang
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, United States
| | - John Lee
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, United States
| | - Christopher Rozell
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, United States.,School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, United States
| | - Annabelle C Singer
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, United States
| |
Collapse
|
7
|
Liu S, Iriate-Diaz J, Hatsopoulos NG, Ross CF, Takahashi K, Chen Z. Dynamics of motor cortical activity during naturalistic feeding behavior. J Neural Eng 2019; 16:026038. [PMID: 30721881 DOI: 10.1088/1741-2552/ab0474] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE The orofacial primary motor cortex (MIo) plays a critical role in controlling tongue and jaw movements during oral motor functions, such as chewing, swallowing and speech. However, the neural mechanisms of MIo during naturalistic feeding are still poorly understood. There is a strong need for a systematic study of motor cortical dynamics during feeding behavior. APPROACH To investigate the neural dynamics and variability of MIo neuronal activity during naturalistic feeding, we used chronically implanted micro-electrode arrays to simultaneously recorded ensembles of neuronal activity in the MIo of two monkeys (Macaca mulatta) while eating various types of food. We developed a Bayesian nonparametric latent variable model to reveal latent structures of neuronal population activity of the MIo and identify the complex mapping between MIo ensemble spike activity and high-dimensional kinematics. MAIN RESULTS Rhythmic neuronal firing patterns and oscillatory dynamics are evident in single-unit activity. At the population level, we uncovered the neural dynamics of rhythmic chewing, and quantified the neural variability at multiple timescales (complete feeding sequences, chewing sequence stages, chewing gape cycle phases) across food types. Our approach accommodates time-warping of chewing sequences and automatic model selection, and maps the latent states to chewing behaviors at fine timescales. SIGNIFICANCE Our work shows that neural representations of MIo ensembles display spatiotemporal patterns in chewing gape cycles at different chew sequence stages, and these patterns vary in a stage-dependent manner. Unsupervised learning and decoding analysis may reveal the link between complex MIo spatiotemporal patterns and chewing kinematics.
Collapse
Affiliation(s)
- Shizhao Liu
- Department of Psychiatry, Department of Neuroscience & Physiology, New York University School of Medicine, New York, NY 10016, United States of America. Department of Biomedical Engineering, Tsinghua University, Beijing, People's Republic of China
| | | | | | | | | | | |
Collapse
|
8
|
Howell S, Griesbach GS. The interplay between neuroendocrine and sleep alterations following traumatic brain injury. NeuroRehabilitation 2019; 43:327-345. [PMID: 30347624 DOI: 10.3233/nre-182483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Sleep and endocrine disruptions are prevalent after traumatic brain injury (TBI) and are likely to contribute to morbidity. OBJECTIVE To describe the interaction between sleep and hormonal regulation following TBI and elucidate the impact that alterations of these systems have on cognitive responses during the posttraumatic chronic period. METHODS Review of preclinical and clinical literature describing long-lasting endocrine dysregulation and sleep alterations following TBI. The bidirectional relationship between sleep and hormones is described. Literature describing co-occurrence between sleep-wake disturbances and hormonal dysregulation will be presented. Review of literature describing cognitive effects of seep and hormones. The cognitive and functional impact of sleep disturbances and hormonal dysregulation is discussed within the context of TBI. RESULTS/CONCLUSIONS Sleep and hormonal alterations impact cognitive and functional outcome after TBI. Diagnosis and treatment of these disturbances will impact recovery following TBI and should be considered in the post-acute rehabilitative setting.
Collapse
Affiliation(s)
| | - Grace S Griesbach
- Centre for Neuro Skills, Encino, CA, USA.,Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| |
Collapse
|
9
|
Hippocampal Reactivation Extends for Several Hours Following Novel Experience. J Neurosci 2018; 39:866-875. [PMID: 30530857 DOI: 10.1523/jneurosci.1950-18.2018] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 11/13/2018] [Accepted: 11/26/2018] [Indexed: 11/21/2022] Open
Abstract
New memories are believed to be consolidated over several hours of post-task sleep. The reactivation or "replay" of hippocampal cell assemblies has been proposed to provide a key mechanism for this process. However, previous studies have indicated that such replay is restricted to the first 10-30 min of post-task sleep, suggesting that it has a limited role in memory consolidation. We performed long-duration recordings in sleeping and behaving male rats and applied methods for evaluating the reactivation of neurons in pairs as well as in larger ensembles while controlling for the continued activation of ensembles already present during pre-task sleep ("preplay"). We found that cell assemblies reactivate for up to 10 h, with a half-maximum timescale of ∼6 h, in sleep following novel experience, even when corrected for preplay. We further confirmed similarly prolonged reactivation in post-task sleep of rats in other datasets that used behavior in novel environments. In contrast, we saw limited reactivation in sleep following behavior in familiar environments. Overall, our findings reconcile the duration of replay with the timescale attributed to cellular memory consolidation and provide strong support for an integral role of replay in memory.SIGNIFICANCE STATEMENT Neurons that are active during an experience reactivate again afterward during rest and sleep. This replay of ensembles of neurons has been proposed to help strengthen memories, but it has also been reported that replay occurs only in the first 10-30 min of sleep, suggesting a circumscribed role. We performed long-duration recordings in the hippocampus of rats and found that replay persists for several hours in sleep following novel experience, far beyond the limits found in previous reports based on shorter recordings. These findings reconcile the duration of replay with the hours-long timescales attributed to memory consolidation.
Collapse
|
10
|
Hu S, Ciliberti D, Grosmark AD, Michon F, Ji D, Penagos H, Buzsáki G, Wilson MA, Kloosterman F, Chen Z. Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes. Cell Rep 2018; 25:2635-2642.e5. [PMID: 30517852 PMCID: PMC6314684 DOI: 10.1016/j.celrep.2018.11.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 10/10/2018] [Accepted: 11/06/2018] [Indexed: 12/13/2022] Open
Abstract
Uncovering spatial representations from large-scale ensemble spike activity in specific brain circuits provides valuable feedback in closed-loop experiments. We develop a graphics processing unit (GPU)-powered population-decoding system for ultrafast reconstruction of spatial positions from rodents' unsorted spatiotemporal spiking patterns, during run behavior or sleep. In comparison with an optimized quad-core central processing unit (CPU) implementation, our approach achieves an ∼20- to 50-fold increase in speed in eight tested rat hippocampal, cortical, and thalamic ensemble recordings, with real-time decoding speed (approximately fraction of a millisecond per spike) and scalability up to thousands of channels. By accommodating parallel shuffling in real time (computation time <15 ms), our approach enables assessment of the statistical significance of online-decoded "memory replay" candidates during quiet wakefulness or sleep. This open-source software toolkit supports the decoding of spatial correlates or content-triggered experimental manipulation in closed-loop neuroscience experiments.
Collapse
Affiliation(s)
- Sile Hu
- Department of Instrument Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang 310027, China; Department of Psychiatry, Department of Neuroscience and Physiology, School of Medicine, New York University, New York, NY 10016, USA
| | - Davide Ciliberti
- Neuro-Electronics Research Flanders (NERF), IMEC, Leuven, Belgium; Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium; VIB, Leuven, Belgium
| | - Andres D Grosmark
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10019, USA
| | - Frédéric Michon
- Neuro-Electronics Research Flanders (NERF), IMEC, Leuven, Belgium; Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium
| | - Daoyun Ji
- Department of Molecular and Cellular Biology, Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hector Penagos
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02134, USA
| | - György Buzsáki
- The Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA
| | - Matthew A Wilson
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02134, USA
| | - Fabian Kloosterman
- Neuro-Electronics Research Flanders (NERF), IMEC, Leuven, Belgium; Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium; VIB, Leuven, Belgium.
| | - Zhe Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, School of Medicine, New York University, New York, NY 10016, USA.
| |
Collapse
|
11
|
Liu K, Sibille J, Dragoi G. Generative Predictive Codes by Multiplexed Hippocampal Neuronal Tuplets. Neuron 2018; 99:1329-1341.e6. [PMID: 30146305 DOI: 10.1016/j.neuron.2018.07.047] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 06/03/2018] [Accepted: 07/27/2018] [Indexed: 01/20/2023]
Abstract
Rapid internal representations are continuously formed based on single experiential episodes in space and time, but the neuronal ensemble mechanisms enabling rapid encoding without constraining the capacity for multiple distinct representations are unknown. We developed a probabilistic statistical model of hippocampal spontaneous sequential activity and revealed existence of an internal model of generative predictive codes for the regularities of multiple future novel spatial sequences. During navigation, the inferred difference between external stimuli and the internal model was encoded by emergence of intrinsic-unlikely, novel functional connections, which updated the model by preferentially potentiating post-experience. This internal model and these predictive codes depended on neuronal organization into inferred modules of short, high-repeat sequential neuronal "tuplets" operating as "neuro-codons." We propose that flexible multiplexing of neuronal tuplets into repertoires of extended sequences vastly expands the capacity of hippocampal predictive codes, which could initiate top-down hierarchical cortical loops for spatial and mental navigation and rapid learning.
Collapse
Affiliation(s)
- Kefei Liu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Jeremie Sibille
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - George Dragoi
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA; Department of Neuroscience and Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06511, USA.
| |
Collapse
|
12
|
Abstract
It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.
Collapse
Affiliation(s)
- Shizhao Liu
- Departments of Psychiatry and of Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY 10016, U.S.A., and Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Andres D Grosmark
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10019, U.S.A.
| | - Zhe Chen
- Departments of Psychiatry and of Neuroscience and Physiology, New York University School of Medicine, New York, NY 10016, U.S.A.
| |
Collapse
|
13
|
Chen Z, Wilson MA. Deciphering Neural Codes of Memory during Sleep. Trends Neurosci 2017; 40:260-275. [PMID: 28390699 PMCID: PMC5434457 DOI: 10.1016/j.tins.2017.03.005] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Revised: 03/13/2017] [Accepted: 03/14/2017] [Indexed: 02/07/2023]
Abstract
Memories of experiences are stored in the cerebral cortex. Sleep is critical for the consolidation of hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms in memory consolidation and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. Here we revisit important experimental findings on sleep-associated memory (i.e., neural activity patterns in sleep that reflect memory processing) and review computational approaches to the analysis of sleep-associated neural codes (SANCs). We focus on two analysis paradigms for sleep-associated memory and propose a new unsupervised learning framework ('memory first, meaning later') for unbiased assessment of SANCs.
Collapse
Affiliation(s)
- Zhe Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY 10016, USA.
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| |
Collapse
|
14
|
van der Meer MAA, Carey AA, Tanaka Y. Optimizing for generalization in the decoding of internally generated activity in the hippocampus. Hippocampus 2017; 27:580-595. [PMID: 28177571 DOI: 10.1002/hipo.22714] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 01/23/2017] [Accepted: 01/24/2017] [Indexed: 12/27/2022]
Abstract
The decoding of a sensory or motor variable from neural activity benefits from a known ground truth against which decoding performance can be compared. In contrast, the decoding of covert, cognitive neural activity, such as occurs in memory recall or planning, typically cannot be compared to a known ground truth. As a result, it is unclear how decoders of such internally generated activity should be configured in practice. We suggest that if the true code for covert activity is unknown, decoders should be optimized for generalization performance using cross-validation. Using ensemble recording data from hippocampal place cells, we show that this cross-validation approach results in different decoding error, different optimal decoding parameters, and different distributions of error across the decoded variable space. In addition, we show that a minor modification to the commonly used Bayesian decoding procedure, which enables the use of spike density functions, results in substantially lower decoding errors. These results have implications for the interpretation of covert neural activity, and suggest easy-to-implement changes to commonly used procedures across domains, with applications to hippocampal place cells in particular. © 2017 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
| | - Alyssa A Carey
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, North Hampshire
| | - Youki Tanaka
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, North Hampshire
| |
Collapse
|