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Coulter ME, Gillespie AK, Chu J, Denovellis EL, Nguyen TTK, Liu DF, Wadhwani K, Sharma B, Wang K, Deng X, Eden UT, Kemere C, Frank LM. Closed-loop modulation of remote hippocampal representations with neurofeedback. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593085. [PMID: 38766135 PMCID: PMC11100667 DOI: 10.1101/2024.05.08.593085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Humans can remember specific events without acting on them and can influence which memories are retrieved based on internal goals. However, current animal models of memory typically present sensory cues to trigger retrieval and assess retrieval based on action 1-5 . As a result, it is difficult to determine whether measured patterns of neural activity relate to the cue(s), the retrieved memory, or the behavior. We therefore asked whether we could develop a paradigm to isolate retrieval-related neural activity in animals without retrieval cues or the requirement of a behavioral report. To do this, we focused on hippocampal "place cells." These cells primarily emit spiking patterns that represent the animal's current location (local representations), but they can also generate representations of previously visited locations distant from the animal's current location (remote representations) 6-13 . It is not known whether animals can deliberately engage specific remote representations, and if so, whether this engagement would occur during specific brain states. So, we used a closed-loop neurofeedback system to reward expression of remote representations that corresponded to uncued, experimenter-selected locations, and found that rats could increase the prevalence of these specific remote representations over time; thus, demonstrating memory retrieval modulated by internal goals in an animal model. These representations occurred predominately during periods of immobility but outside of hippocampal sharp-wave ripple (SWR) 13-15 events. This paradigm enables future direct studies of memory retrieval mechanisms in the healthy brain and in models of neurological disorders.
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2
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Mukherjee S, Babadi B. Adaptive modeling and inference of higher-order coordination in neuronal assemblies: A dynamic greedy estimation approach. PLoS Comput Biol 2024; 20:e1011605. [PMID: 38805569 PMCID: PMC11161120 DOI: 10.1371/journal.pcbi.1011605] [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: 10/16/2023] [Revised: 06/07/2024] [Accepted: 05/20/2024] [Indexed: 05/30/2024] Open
Abstract
Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states.
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Affiliation(s)
- Shoutik Mukherjee
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
- Institute for Systems Research, University of Maryland, College Park, Maryland, United States of America
| | - Behtash Babadi
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
- Institute for Systems Research, University of Maryland, College Park, Maryland, United States of America
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3
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Chen S, He M, Brown RE, Eden UT, Prerau MJ. Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581592. [PMID: 38464146 PMCID: PMC10925076 DOI: 10.1101/2024.02.22.581592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Sleep spindles are critical for memory consolidation and strongly linked to neurological disease and aging. Despite their significance, the relative influences of factors like sleep depth, cortical up/down states, and spindle temporal patterns on individual spindle production remain poorly understood. Moreover, spindle temporal patterns are typically ignored in favor of an average spindle rate. Here, we analyze spindle dynamics in 1008 participants from the Multi-Ethnic Study of Atherosclerosis using a point process framework. Results reveal fingerprint-like temporal patterns, characterized by a refractory period followed by a period of increased spindle activity, which are highly individualized yet consistent night-to-night. We observe increased timing variability with age and distinct gender/age differences. Strikingly, and in contrast to the prevailing notion, individualized spindle patterns are the dominant determinant of spindle timing, accounting for over 70% of the statistical deviance explained by all of the factors we assessed, surpassing the contribution of slow oscillation (SO) phase (~14%) and sleep depth (~16%). Furthermore, we show spindle/SO coupling dynamics with sleep depth are preserved across age, with a global negative shift towards the SO rising slope. These findings offer novel mechanistic insights into spindle dynamics with direct experimental implications and applications to individualized electroencephalography biomarker identification.
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Affiliation(s)
- Shuqiang Chen
- Graduate Program for Neuroscience, Boston University, Boston, MA, USA
| | - Mingjian He
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ritchie E. Brown
- VA Boston Healthcare System and Harvard Medical School, Department of Psychiatry, West Roxbury, MA, USA
| | - Uri T. Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Michael J. Prerau
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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Lai C, Tanaka S, Harris TD, Lee AK. Volitional activation of remote place representations with a hippocampal brain-machine interface. Science 2023; 382:566-573. [PMID: 37917713 PMCID: PMC10683874 DOI: 10.1126/science.adh5206] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 09/22/2023] [Indexed: 11/04/2023]
Abstract
The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including maplike representations of familiar environments. However, whether representations in such "cognitive maps" can be volitionally accessed is unknown. We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner. We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.
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Affiliation(s)
- Chongxi Lai
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Shinsuke Tanaka
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Timothy D. Harris
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Albert K. Lee
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
- Howard Hughes Medical Institute and Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
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5
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Mukherjee S, Babadi B. Adaptive modeling and inference of higher-order coordination in neuronal assemblies: a dynamic greedy estimation approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.16.562647. [PMID: 37905104 PMCID: PMC10614874 DOI: 10.1101/2023.10.16.562647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states.
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Affiliation(s)
- Shoutik Mukherjee
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
- Institute for Systems Research, University of Maryland, College Park, MD, USA
| | - Behtash Babadi
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
- Institute for Systems Research, University of Maryland, College Park, MD, USA
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6
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Rezaei MR, Jeoung H, Gharamani A, Saha U, Bhat V, Popovic MR, Yousefi A, Chen R, Lankarany M. Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model. J Neural Eng 2023; 20:056016. [PMID: 37473753 DOI: 10.1088/1741-2552/ace932] [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: 01/19/2023] [Accepted: 07/20/2023] [Indexed: 07/22/2023]
Abstract
Objective. The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g. the response time), remains to be identified.Approach. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals' response time) recorded in ten Parkinson's disease (PD) patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy populations.Main results. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies.Significance. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems.
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Affiliation(s)
- Mohammad R Rezaei
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Haseul Jeoung
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Ayda Gharamani
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- Worcester Polytechnic Institute, MA, United States of America
| | - Utpal Saha
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Venkat Bhat
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Ali Yousefi
- Worcester Polytechnic Institute, MA, United States of America
| | - Robert Chen
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Milad Lankarany
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
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7
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Chen Z, Blair GJ, Guo C, Zhou J, Romero-Sosa JL, Izquierdo A, Golshani P, Cong J, Aharoni D, Blair HT. A hardware system for real-time decoding of in vivo calcium imaging data. eLife 2023; 12:78344. [PMID: 36692269 PMCID: PMC9908073 DOI: 10.7554/elife.78344] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 01/23/2023] [Indexed: 01/25/2023] Open
Abstract
Epifluorescence miniature microscopes ('miniscopes') are widely used for in vivo calcium imaging of neural population activity. Imaging data are typically collected during a behavioral task and stored for later offline analysis, but emerging techniques for online imaging can support novel closed-loop experiments in which neural population activity is decoded in real time to trigger neurostimulation or sensory feedback. To achieve short feedback latencies, online imaging systems must be optimally designed to maximize computational speed and efficiency while minimizing errors in population decoding. Here we introduce DeCalciOn, an open-source device for real-time imaging and population decoding of in vivo calcium signals that is hardware compatible with all miniscopes that use the UCLA Data Acquisition (DAQ) interface. DeCalciOn performs online motion stabilization, neural enhancement, calcium trace extraction, and decoding of up to 1024 traces per frame at latencies of <50 ms after fluorescence photons arrive at the miniscope image sensor. We show that DeCalciOn can accurately decode the position of rats (n = 12) running on a linear track from calcium fluorescence in the hippocampal CA1 layer, and can categorically classify behaviors performed by rats (n = 2) during an instrumental task from calcium fluorescence in orbitofrontal cortex. DeCalciOn achieves high decoding accuracy at short latencies using innovations such as field-programmable gate array hardware for real-time image processing and contour-free methods to efficiently extract calcium traces from sensor images. In summary, our system offers an affordable plug-and-play solution for real-time calcium imaging experiments in behaving animals.
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Affiliation(s)
- Zhe Chen
- Department of Electrical and Computer Engineering, University of California, Los AngelesLos AngelesUnited States
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Garrett J Blair
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Changliang Guo
- David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Jim Zhou
- Department of Electrical and Computer Engineering, University of California, Los AngelesLos AngelesUnited States
| | - Juan-Luis Romero-Sosa
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Alicia Izquierdo
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
- Integrative Center for Learning and Memory, University of California, Los AngelesLos AngelesUnited States
| | - Peyman Golshani
- David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Integrative Center for Learning and Memory, University of California, Los AngelesLos AngelesUnited States
| | - Jason Cong
- Department of Electrical and Computer Engineering, University of California, Los AngelesLos AngelesUnited States
| | - Daniel Aharoni
- David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Integrative Center for Learning and Memory, University of California, Los AngelesLos AngelesUnited States
| | - Hugh T Blair
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
- Integrative Center for Learning and Memory, University of California, Los AngelesLos AngelesUnited States
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8
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Comrie AE, Frank LM, Kay K. Imagination as a fundamental function of the hippocampus. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210336. [PMID: 36314152 PMCID: PMC9620759 DOI: 10.1098/rstb.2021.0336] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 04/20/2022] [Indexed: 08/25/2023] Open
Abstract
Imagination is a biological function that is vital to human experience and advanced cognition. Despite this importance, it remains unknown how imagination is realized in the brain. Substantial research focusing on the hippocampus, a brain structure traditionally linked to memory, indicates that firing patterns in spatially tuned neurons can represent previous and upcoming paths in space. This work has generally been interpreted under standard views that the hippocampus implements cognitive abilities primarily related to actual experience, whether in the past (e.g. recollection, consolidation), present (e.g. spatial mapping) or future (e.g. planning). However, relatively recent findings in rodents identify robust patterns of hippocampal firing corresponding to a variety of alternatives to actual experience, in many cases without overt reference to the past, present or future. Given these findings, and others on hippocampal contributions to human imagination, we suggest that a fundamental function of the hippocampus is to generate a wealth of hypothetical experiences and thoughts. Under this view, traditional accounts of hippocampal function in episodic memory and spatial navigation can be understood as particular applications of a more general system for imagination. This view also suggests that the hippocampus contributes to a wider range of cognitive abilities than previously thought. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.
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Affiliation(s)
- Alison E. Comrie
- Neuroscience Graduate Program, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA
- Center for Integrative Neuroscience, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA
- Departments of Physiology and Psychiatry, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA
| | - Loren M. Frank
- Kavli Institute for Fundamental Neuroscience, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA
- Center for Integrative Neuroscience, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA
- Departments of Physiology and Psychiatry, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA
- Howard Hughes Medical Institute, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA
| | - Kenneth Kay
- Zuckerman Institute, Center for Theoretical Neuroscience, Columbia University, 3227 Broadway, New York, NY 10027, USA
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9
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Xiao P, Liu X. Nonlinear point-process estimation of neural spiking activity based on variational Bayesian inference. J Neural Eng 2022; 19. [PMID: 35947962 DOI: 10.1088/1741-2552/ac88a0] [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: 05/16/2022] [Accepted: 08/10/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Understanding neural encoding and decoding processes are crucial to the development of brain-machine interfaces (BMI). Higher decoding speed of neural signals is required for the large-scale neural data and the extremely low detection delay of closed-loop feedback experiment. APPROACH To achieve higher neural decoding speed, we proposed a novel adaptive higher-order nonlinear point-process filter based on the Variational Bayesian Inference (VBI) framework, called the HON-VBI. This algorithm avoids the complex Monte Carlo random sampling in the traditional method. Using the VBI method, it can quickly implement inferences of state posterior distribution and the tuning parameters. MAIN RESULTS Our result demonstrates the effectiveness and advantages of the HON-VBI by application for decoding the multichannel neural spike trains of the simulation data and real data. Compared with traditional methods, the HON-VBI greatly reduces the decoding time of large-scale neural spike trains. Through capturing the nonlinear evolution of system state and accurate estimating of time-varying tuning parameters, the decoding accuracy is improved. SIGNIFICANCE Our work can be applied to rapidly decode large-scale multichannel neural spike trains in brain-machine interfaces.
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Affiliation(s)
- Ping Xiao
- State Key Laboratory of Mechanics and Control of Mechanical Structures, Institute of Nano Science and Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing, Jiangsu, 210016, CHINA
| | - Xinsheng Liu
- State Key Laboratory of Mechanics and Control of Mechanical Structures, Institute of Nano Science and Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing, Jiangsu, 210016, CHINA
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10
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Deng X, Chen S, Sosa M, Karlsson MP, Wei XX, Frank LM. A Variable Clock Underlies Internally Generated Hippocampal Sequences. J Neurosci 2022; 42:3797-3810. [PMID: 35351831 PMCID: PMC9087812 DOI: 10.1523/jneurosci.1120-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/23/2021] [Accepted: 01/01/2022] [Indexed: 11/21/2022] Open
Abstract
Humans have the ability to store and retrieve memories with various degrees of specificity, and recent advances in reinforcement learning have identified benefits to learning when past experience is represented at different levels of temporal abstraction. How this flexibility might be implemented in the brain remains unclear. We analyzed the temporal organization of male rat hippocampal population spiking to identify potential substrates for temporally flexible representations. We examined activity both during locomotion and during memory-associated population events known as sharp-wave ripples (SWRs). We found that spiking during SWRs is rhythmically organized with higher event-to-event variability than spiking during locomotion-associated population events. Decoding analyses using clusterless methods further indicate that a similar spatial experience can be replayed in multiple SWRs, each time with a different rhythmic structure whose periodicity is sampled from a log-normal distribution. This variability increases with experience despite the decline in SWR rates that occurs as environments become more familiar. We hypothesize that the variability in temporal organization of hippocampal spiking provides a mechanism for storing experiences with various degrees of specificity.SIGNIFICANCE STATEMENT One of the most remarkable properties of memory is its flexibility: the brain can retrieve stored representations at varying levels of detail where, for example, we can begin with a memory of an entire extended event and then zoom in on a particular episode. The neural mechanisms that support this flexibility are not understood. Here we show that hippocampal sharp-wave ripples, which mark the times of memory replay and are important for memory storage, have a highly variable temporal structure that is well suited to support the storage of memories at different levels of detail.
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Affiliation(s)
- Xinyi Deng
- Department of Data Science, Beijing University of Technology, Beijing 100124, People's Republic of China
| | - Shizhe Chen
- Department of Statistics, University of California, Davis, Davis, California 95616
| | - Marielena Sosa
- Center for Integrative Neuroscience and Department of Physiology, University of California, San Francisco, San Francisco, California 94158
| | - Mattias P Karlsson
- Center for Integrative Neuroscience and Department of Physiology, University of California, San Francisco, San Francisco, California 94158
| | - Xue-Xin Wei
- Department of Neuroscience, University of Texas at Austin, Austin, Texas 78751
| | - Loren M Frank
- Center for Integrative Neuroscience and Department of Physiology, University of California, San Francisco, San Francisco, California 94158
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, California 94158
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, California 94158
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11
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Rezaei MR, Hadjinicolaou AE, Cash SS, Eden UT, Yousefi A. Direct Discriminative Decoder Models for Analysis of High-Dimensional Dynamical Neural Data. Neural Comput 2022; 34:1100-1135. [PMID: 35344988 DOI: 10.1162/neco_a_01491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/08/2022] [Indexed: 11/04/2022]
Abstract
With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationally tractable. Latent process models have shown promising results in estimating the dynamics of cognitive processes using individual models for each neuron's receptive field. However, scaling these models to work on high-dimensional neural recordings remains challenging. Not only is it impractical to build receptive field models for individual neurons of a large neural population, but most neural data analyses based on individual receptive field models discard the local history of neural activity, which has been shown to be critical in the accurate inference of the underlying cognitive processes. Here, we propose a novel, scalable latent process model that can directly estimate cognitive process dynamics without requiring precise receptive field models of individual neurons or brain nodes. We call this the direct discriminative decoder (DDD) model. The DDD model consists of (1) a discriminative process that characterizes the conditional distribution of the signal to be estimated, or state, as a function of both the current neural activity and its local history, and (2) a state transition model that characterizes the evolution of the state over a longer time period. While this modeling framework inherits advantages of existing latent process modeling methods, its computational cost is tractable. More important, the solution can incorporate any information from the history of neural activity at any timescale in computing the estimate of the state process. There are many choices in building the discriminative process, including deep neural networks or gaussian processes, which adds to the flexibility of the framework. We argue that these attributes of the proposed methodology, along with its applicability to different modalities of neural data, make it a powerful tool for high-dimensional neural data analysis. We also introduce an extension of these methods, called the discriminative-generative decoder (DGD). The DGD includes both discriminative and generative processes in characterizing observed data. As a result, we can combine physiological correlates like behavior with neural data to better estimate underlying cognitive processes. We illustrate the methods, including steps for inference and model identification, and demonstrate applications to multiple data analysis problems with high-dimensional neural recordings. The modeling results demonstrate the computational and modeling advantages of the DDD and DGD methods.
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Affiliation(s)
- Mohammad R Rezaei
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9.,Krembil Research Institute, University Health Network, Toronto, ON M5T 2S8.,KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada
| | - Alex E Hadjinicolaou
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114.,Harvard Medical School, Boston, MA 02115, U.S.A.
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114.,Harvard Medical School, Boston, MA 02115, U.S.A.
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, U.S.A.
| | - Ali Yousefi
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, U.S.A.
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12
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Krause EL, Drugowitsch J. A large majority of awake hippocampal sharp-wave ripples feature spatial trajectories with momentum. Neuron 2021; 110:722-733.e8. [PMID: 34863366 DOI: 10.1016/j.neuron.2021.11.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 10/06/2021] [Accepted: 11/12/2021] [Indexed: 01/02/2023]
Abstract
During periods of rest, hippocampal place cells feature bursts of activity called sharp-wave ripples (SWRs). Heuristic approaches have revealed that a small fraction of SWRs appear to "simulate" trajectories through the environment, called awake hippocampal replay. However, the functional role of a majority of these SWRs remains unclear. We find, using Bayesian model comparison of state-space models to characterize the spatiotemporal dynamics embedded in SWRs, that almost all SWRs of foraging rodents simulate such trajectories. Furthermore, these trajectories feature momentum, or inertia in their velocities, that mirrors the animals' natural movement, in contrast to replay events during sleep, which lack such momentum. Last, we show that past analyses of replayed trajectories for navigational planning were biased by the heuristic SWR sub-selection. Our findings thus identify the dominant function of awake SWRs as simulating trajectories with momentum and provide a principled foundation for future work on their computational function.
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Affiliation(s)
- Emma L Krause
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA.
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13
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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.
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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
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14
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Hermiz J, Joseph E, Hyun Lee K, Baldacci IA, Chung JE, Frank LM, Bouchard KE, Denes P. The impact of reducing signal acquisition specifications on neuronal spike sorting. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5914-5918. [PMID: 34892465 DOI: 10.1109/embc46164.2021.9630669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Measuring electrical potentials in the extracellular space of the brain is a popular technique because it can detect action potentials from putative individual neurons. Electrophysiology is undergoing a transformation where the number of recording channels, and thus number of neurons detected, is growing at a dramatic rate. This rapid scaling is paving the way for both new discoveries and commercial applications; however, as the number of channels increases there will be an increasing need to make these systems more power efficient. One area ripe for optimization are the signal acquisition specifications needed to detect and sort action potentials (i.e., "spikes") to putative single neuron sources. In this work, we take existing recordings collected using Intan hardware and modify them in a way that corresponds to reduced recording performance. The accuracy of these degraded recordings to spike sort using MountainSort4 is evaluated by comparing against expert labels. We show that despite reducing signal specifications by a factor of 2 or more, spike sorting accuracy does not change substantially. Specifically, reducing both sample rate and bit depth from 30 kHz and 16 bits to 12 kHz and 12 bits resulted in a 3% drop in spike sorting accuracy. Our results suggest that current neural acquisition systems are over-specified. These results may inform the design of next generation neural acquisition systems enabling higher channel count systems.
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15
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Gillespie AK, Astudillo Maya DA, Denovellis EL, Liu DF, Kastner DB, Coulter ME, Roumis DK, Eden UT, Frank LM. Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice. Neuron 2021; 109:3149-3163.e6. [PMID: 34450026 DOI: 10.1016/j.neuron.2021.07.029] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/21/2021] [Accepted: 07/29/2021] [Indexed: 01/06/2023]
Abstract
Executing memory-guided behavior requires storage of information about experience and later recall of that information to inform choices. Awake hippocampal replay, when hippocampal neural ensembles briefly reactivate a representation related to prior experience, has been proposed to critically contribute to these memory-related processes. However, it remains unclear whether awake replay contributes to memory function by promoting the storage of past experiences, facilitating planning based on evaluation of those experiences, or both. We designed a dynamic spatial task that promotes replay before a memory-based choice and assessed how the content of replay related to past and future behavior. We found that replay content was decoupled from subsequent choice and instead was enriched for representations of previously rewarded locations and places that had not been visited recently, indicating a role in memory storage rather than in directly guiding subsequent behavior.
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Affiliation(s)
- Anna K Gillespie
- Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, CA 94158, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Daniela A Astudillo Maya
- Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, CA 94158, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Eric L Denovellis
- Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, CA 94158, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Daniel F Liu
- Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, CA 94158, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David B Kastner
- Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, CA 94158, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Michael E Coulter
- Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, CA 94158, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Demetris K Roumis
- Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, CA 94158, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Loren M Frank
- Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, CA 94158, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
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16
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Denovellis EL, Gillespie AK, Coulter ME, Sosa M, Chung JE, Eden UT, Frank LM. Hippocampal replay of experience at real-world speeds. eLife 2021; 10:64505. [PMID: 34570699 PMCID: PMC8476125 DOI: 10.7554/elife.64505] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 09/08/2021] [Indexed: 01/12/2023] Open
Abstract
Representations related to past experiences play a critical role in memory and decision-making processes. The rat hippocampus expresses these types of representations during sharp-wave ripple (SWR) events, and previous work identified a minority of SWRs that contain ‘replay’ of spatial trajectories at ∼20x the movement speed of the animal. Efforts to understand replay typically make multiple assumptions about which events to examine and what sorts of representations constitute replay. We therefore lack a clear understanding of both the prevalence and the range of representational dynamics associated with replay. Here, we develop a state space model that uses a combination of movement dynamics of different speeds to capture the spatial content and time evolution of replay during SWRs. Using this model, we find that the large majority of replay events contain spatially coherent, interpretable content. Furthermore, many events progress at real-world, rather than accelerated, movement speeds, consistent with actual experiences.
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Affiliation(s)
- Eric L Denovellis
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States.,Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, United States.,Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, United States
| | - Anna K Gillespie
- Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, United States.,Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, United States
| | - Michael E Coulter
- Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, United States.,Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, United States
| | - Marielena Sosa
- Department of Neurobiology, Stanford University School of Medicine, Stanford, United States
| | - Jason E Chung
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, United States
| | - Loren M Frank
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States.,Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, United States.,Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, United States
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17
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Subramanian S, Purdon PL, Barbieri R, Brown EN. A Model-Based Framework for Assessing the Physiologic Structure of Electrodermal Activity. IEEE Trans Biomed Eng 2021; 68:2833-2845. [PMID: 33822719 PMCID: PMC8425954 DOI: 10.1109/tbme.2021.3071366] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Objective: We present a statistical model for extracting physiologic
characteristics from electrodermal activity (EDA) data in observational
settings. Methods: We based our model on the integrate-and-fire physiology of sweat
gland bursts, which predicts inverse Gaussian (IG) inter-pulse interval
structure. At the core of our model-based paradigm is a subject-specific
amplitude threshold selection process for EDA pulses based on the
statistical properties of four right-skewed models including the IG. By
performing a sensitivity analysis across thresholds and fitting all four
models, we selected for IG-like structure and verified the pulse selection
with a goodness-of-fit analysis, maximizing capture of physiology at the
time scale of EDA responses. Results: We tested the model-based paradigm on simulated EDA time series and
data from two different experimental cohorts recorded during different
experimental conditions, using different equipment. In both the simulated
and experimental data, our model-based method robustly recovered pulses that
captured the IG-like structure predicted by physiology, despite large
differences in noise level. In contrast, established EDA analysis tools,
which attempted to estimate neural activity from slower EDA responses, did
not provide physiological validation and were susceptible to noise. Conclusion: We present a computationally efficient, statistically rigorous, and
physiology-informed paradigm for pulse selection from EDA data that is
robust across individuals and experimental conditions, yet adaptable to
varying noise level. Significance: The robustness of the model-based paradigm and its physiological
basis provide empirical support for the use of EDA as a clinical marker for
sympathetic activity in conditions such as pain, anxiety, depression, and
sleep states.
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18
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Frey M, Tanni S, Perrodin C, O'Leary A, Nau M, Kelly J, Banino A, Bendor D, Lefort J, Doeller CF, Barry C. Interpreting wide-band neural activity using convolutional neural networks. eLife 2021; 10:e66551. [PMID: 34338632 PMCID: PMC8328518 DOI: 10.7554/elife.66551] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 07/13/2021] [Indexed: 11/29/2022] Open
Abstract
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors - including a novel representation of head direction - from raw neural activity.
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Affiliation(s)
- Markus Frey
- Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, NTNU, Norwegian University of Science and TechnologyTrondheimNorway
- Max-Planck-Insitute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Sander Tanni
- Cell & Developmental Biology, UCLLondonUnited Kingdom
| | | | - Alice O'Leary
- Cell & Developmental Biology, UCLLondonUnited Kingdom
| | - Matthias Nau
- Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, NTNU, Norwegian University of Science and TechnologyTrondheimNorway
- Max-Planck-Insitute for Human Cognitive and Brain SciencesLeipzigGermany
| | | | | | - Daniel Bendor
- Institute of Behavioural Neuroscience, UCLLondonUnited Kingdom
| | - Julie Lefort
- Cell & Developmental Biology, UCLLondonUnited Kingdom
| | - Christian F Doeller
- Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, NTNU, Norwegian University of Science and TechnologyTrondheimNorway
- Max-Planck-Insitute for Human Cognitive and Brain SciencesLeipzigGermany
- Institute of Psychology, Leipzig UniversityLeipzigGermany
| | - Caswell Barry
- Cell & Developmental Biology, UCLLondonUnited Kingdom
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19
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Kim YJ, Brackbill N, Batty E, Lee J, Mitelut C, Tong W, Chichilnisky EJ, Paninski L. Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings. Neural Comput 2021; 33:1719-1750. [PMID: 34411268 DOI: 10.1162/neco_a_01395] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 01/25/2021] [Indexed: 11/04/2022]
Abstract
Decoding sensory stimuli from neural activity can provide insight into how the nervous system might interpret the physical environment, and facilitates the development of brain-machine interfaces. Nevertheless, the neural decoding problem remains a significant open challenge. Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs). Our approach uses neural networks to improve on existing decoders in both accuracy and scalability. Trained and validated on real retinal spike data from more than 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of the fine structures of visual stimuli. Specifically, high-pass spatial features of natural images can only be decoded using nonlinear techniques, while low-pass features can be extracted equally well by linear and nonlinear methods. Together, these results advance the state of the art in decoding natural stimuli from large populations of neurons.
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20
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Williams AH, Degleris A, Wang Y, Linderman SW. Point process models for sequence detection in high-dimensional neural spike trains. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 2020; 33:14350-14361. [PMID: 35002191 PMCID: PMC8734964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Sparse sequences of neural spikes are posited to underlie aspects of working memory [1], motor production [2], and learning [3, 4]. Discovering these sequences in an unsupervised manner is a longstanding problem in statistical neuroscience [5-7]. Promising recent work [4, 8] utilized a convolutive nonnegative matrix factorization model [9] to tackle this challenge. However, this model requires spike times to be discretized, utilizes a sub-optimal least-squares criterion, and does not provide uncertainty estimates for model predictions or estimated parameters. We address each of these shortcomings by developing a point process model that characterizes fine-scale sequences at the level of individual spikes and represents sequence occurrences as a small number of marked events in continuous time. This ultra-sparse representation of sequence events opens new possibilities for spike train modeling. For example, we introduce learnable time warping parameters to model sequences of varying duration, which have been experimentally observed in neural circuits [10]. We demonstrate these advantages on experimental recordings from songbird higher vocal center and rodent hippocampus.
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Affiliation(s)
- Alex H Williams
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Anthony Degleris
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305
| | - Yixin Wang
- Department of Statistics, Columbia University, New York NY 10027
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21
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Rezaei MR, Arai K, Frank LM, Eden UT, Yousefi A. Real-Time Point Process Filter for Multidimensional Decoding Problems Using Mixture Models. J Neurosci Methods 2020; 348:109006. [PMID: 33232686 DOI: 10.1016/j.jneumeth.2020.109006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 11/16/2020] [Accepted: 11/17/2020] [Indexed: 10/22/2022]
Abstract
There is an increasing demand for a computationally efficient and accurate point process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the point process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general point-process filter problem when the conditional intensity of a cell's spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of point process observation called marked point-process, which encompasses both clustered - mainly, called sorted - and clusterless - generally called unsorted or raw- spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional point-process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rat's position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% highest probability coverage area (HPD) performance metrics. Though the marked-point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.
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Affiliation(s)
- Mohammad Reza Rezaei
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | - Kensuke Arai
- Department of Mathematics and Statistics, Boston University, Boston, MA, 02215, United States
| | - Loren M Frank
- Department of Physiology, University of California, San Francisco, San Francisco, CA, 94158, United States
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA, 02215, United States
| | - Ali Yousefi
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, 01609, United States.
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22
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Luan L, Robinson JT, Aazhang B, Chi T, Yang K, Li X, Rathore H, Singer A, Yellapantula S, Fan Y, Yu Z, Xie C. Recent Advances in Electrical Neural Interface Engineering: Minimal Invasiveness, Longevity, and Scalability. Neuron 2020; 108:302-321. [PMID: 33120025 PMCID: PMC7646678 DOI: 10.1016/j.neuron.2020.10.011] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 10/03/2020] [Accepted: 10/08/2020] [Indexed: 12/16/2022]
Abstract
Electrical neural interfaces serve as direct communication pathways that connect the nervous system with the external world. Technological advances in this domain are providing increasingly more powerful tools to study, restore, and augment neural functions. Yet, the complexities of the nervous system give rise to substantial challenges in the design, fabrication, and system-level integration of these functional devices. In this review, we present snapshots of the latest progresses in electrical neural interfaces, with an emphasis on advances that expand the spatiotemporal resolution and extent of mapping and manipulating brain circuits. We include discussions of large-scale, long-lasting neural recording; wireless, miniaturized implants; signal transmission, amplification, and processing; as well as the integration of interfaces with optical modalities. We outline the background and rationale of these developments and share insights into the future directions and new opportunities they enable.
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Affiliation(s)
- Lan Luan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; Department of Bioengineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA
| | - Jacob T Robinson
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; Department of Bioengineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA
| | - Taiyun Chi
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Kaiyuan Yang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Xue Li
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA
| | - Haad Rathore
- NeuroEngineering Initiative, Rice University, Houston, TX, USA; Applied Physics Graduate Program, Rice University, Houston, TX, USA
| | - Amanda Singer
- NeuroEngineering Initiative, Rice University, Houston, TX, USA; Applied Physics Graduate Program, Rice University, Houston, TX, USA
| | - Sudha Yellapantula
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA
| | - Yingying Fan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Zhanghao Yu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Chong Xie
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; Department of Bioengineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA.
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23
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Yousefi A, Amidi Y, Nazari B, Eden UT. Assessing Goodness-of-Fit in Marked Point Process Models of Neural Population Coding via Time and Rate Rescaling. Neural Comput 2020; 32:2145-2186. [PMID: 32946712 DOI: 10.1162/neco_a_01321] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Marked point process models have recently been used to capture the coding properties of neural populations from multiunit electrophysiological recordings without spike sorting. These clusterless models have been shown in some instances to better describe the firing properties of neural populations than collections of receptive field models for sorted neurons and to lead to better decoding results. To assess their quality, we previously proposed a goodness-of-fit technique for marked point process models based on time rescaling, which for a correct model produces a set of uniform samples over a random region of space. However, assessing uniformity over such a region can be challenging, especially in high dimensions. Here, we propose a set of new transformations in both time and the space of spike waveform features, which generate events that are uniformly distributed in the new mark and time spaces. These transformations are scalable to multidimensional mark spaces and provide uniformly distributed samples in hypercubes, which are well suited for uniformity tests. We discuss the properties of these transformations and demonstrate aspects of model fit captured by each transformation. We also compare multiple uniformity tests to determine their power to identify lack-of-fit in the rescaled data. We demonstrate an application of these transformations and uniformity tests in a simulation study. Proofs for each transformation are provided in the appendix.
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Affiliation(s)
- Ali Yousefi
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, U.S.A.
| | - Yalda Amidi
- Department of Neurological Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, U.S.A., and Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Behzad Nazari
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, U.S.A.
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24
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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.
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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.
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25
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Shibue R, Komaki F. Deconvolution of calcium imaging data using marked point processes. PLoS Comput Biol 2020; 16:e1007650. [PMID: 32163407 PMCID: PMC7093033 DOI: 10.1371/journal.pcbi.1007650] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 03/24/2020] [Accepted: 01/10/2020] [Indexed: 11/19/2022] Open
Abstract
Calcium imaging has been widely used for measuring spiking activities of neurons. When using calcium imaging, we need to extract summarized information from the raw movie beforehand. Recent studies have used matrix deconvolution for this preprocessing. However, such an approach can neither directly estimate the generative mechanism of spike trains nor use stimulus information that has a strong influence on neural activities. Here, we propose a new deconvolution method for calcium imaging using marked point processes. We consider that the observed movie is generated from a probabilistic model with marked point processes as hidden variables, and we calculate the posterior of these variables using a variational inference approach. Our method can simultaneously estimate various kinds of information, such as cell shape, spike occurrence time, and tuning curve. We apply our method to simulated and experimental data to verify its performance. Calcium imaging is a promising technique that enables the observation of the activities of large neural populations as a movie. Since the measured movie is a large-scale dataset containing a considerable amount of information, we need to apply a preprocessing procedure to extract crucial information from the raw movie for the analysis that follows. Recent studies have adopted matrix decomposition to decompose the observed movie into the product of two matrices: one consisting of cell shapes and the other consisting of calcium florescent time series. This approach can estimate cell locations and activities simultaneously; however, it cannot express some aspects of neural population codes. For instance, this approach cannot incorporate other covariates that may affect the neural population activities. In this paper, we propose a new statistical model for calcium imaging movies and an estimation procedure for this model. To express the random occurrence of spikes occurring in the movie, our model adopts a marked point process, which is used to express sequences of events to which certain characteristic values are attached. Our model can estimate cell shapes, spikes, and tuning curves of cells directly without any additional preprocessing procedure, and it also improves the estimation accuracy compared to the conventional approach.
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Affiliation(s)
- Ryohei Shibue
- NTT Communication Science Laboratories, Atsugi-shi, Japan
| | - Fumiyasu Komaki
- Department of Mathematical Informatics, The University of Tokyo, Tokyo, Japan
- RIKEN Center for Brain Science, Wako-shi, Japan
- International Research Center for Neurointelligence (IRCN), The University of Tokyo, Tokyo, Japan
- * E-mail:
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26
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Kay K, Chung JE, Sosa M, Schor JS, Karlsson MP, Larkin MC, Liu DF, Frank LM. Constant Sub-second Cycling between Representations of Possible Futures in the Hippocampus. Cell 2020; 180:552-567.e25. [PMID: 32004462 DOI: 10.1016/j.cell.2020.01.014] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 11/17/2019] [Accepted: 01/09/2020] [Indexed: 02/07/2023]
Abstract
Cognitive faculties such as imagination, planning, and decision-making entail the ability to represent hypothetical experience. Crucially, animal behavior in natural settings implies that the brain can represent hypothetical future experience not only quickly but also constantly over time, as external events continually unfold. To determine how this is possible, we recorded neural activity in the hippocampus of rats navigating a maze with multiple spatial paths. We found neural activity encoding two possible future scenarios (two upcoming maze paths) in constant alternation at 8 Hz: one scenario per ∼125-ms cycle. Further, we found that the underlying dynamics of cycling (both inter- and intra-cycle dynamics) generalized across qualitatively different representational correlates (location and direction). Notably, cycling occurred across moving behaviors, including during running. These findings identify a general dynamic process capable of quickly and continually representing hypothetical experience, including that of multiple possible futures.
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Affiliation(s)
- Kenneth Kay
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Jason E Chung
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Marielena Sosa
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jonathan S Schor
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Mattias P Karlsson
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Margaret C Larkin
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Daniel F Liu
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Loren M Frank
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
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27
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Yousefi A, Gillespie AK, Guidera JA, Karlsson M, Frank LM, Eden UT. Efficient Decoding of Multi-Dimensional Signals From Population Spiking Activity Using a Gaussian Mixture Particle Filter. IEEE Trans Biomed Eng 2019; 66:3486-3498. [PMID: 30932819 DOI: 10.1109/tbme.2019.2906640] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
New recording technologies and the potential for closed-loop experiments have led to an increasing demand for computationally efficient and accurate algorithms to decode population spiking activity in multi-dimensional spaces. Exact point process filters can accurately decode low-dimensional signals, but are computationally intractable for high-dimensional signals. Approximate Gaussian filters are computationally efficient, but are inaccurate when the signals have complex distributions and nonlinear dynamics. Even particle filter methods tend to become inefficient and inaccurate when the filter distribution has multiple peaks. Here, we develop a new point process filter algorithm that combines the computational efficiency of approximate Gaussian methods with a numerical accuracy that exceeds standard particle filters. We use a mixture of Gaussian model for the posterior at each time step, allowing for an analytic solution to the computationally expensive filter integration step. During non-spike intervals, the filter needs only to update the mean, covariance, and mixture weight of each component. At spike times, a sampling procedure is used to update the filtering distribution and find the number of Gaussian mixture components necessary to maintain an accurate approximation. We illustrate the application of this algorithm to the problem of decoding a rat's position and velocity in a maze from hippocampal place cell data using both 2-D and 4-D decoders.
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28
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Dai J, Zhang P, Sun H, Qiao X, Zhao Y, Ma J, Li S, Zhou J, Wang C. Reliability of motor and sensory neural decoding by threshold crossings for intracortical brain-machine interface. J Neural Eng 2019; 16:036011. [PMID: 30822756 DOI: 10.1088/1741-2552/ab0bfb] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVE For intracortical neurophysiological studies, spike sorting is an important procedure to isolate single units for analyzing specific functions. However, whether spike sorting is necessary or not for neural decoding applications is controversial. Several studies showed that using threshold crossings (TC) instead of spike sorting could also achieve a similar satisfactory performance. However, such studies were limited in similar behavioral tasks, and the neural signal source mainly focused on the motor-related cortical regions. It is not certain if this conclusion is applicable to other situations. Therefore, we compared the performance of TC and spike sorting in neural decoding with more comprehensive paradigms and parameters. APPROACH Two rhesus macaques implanted with Utah or floating microelectrode arrays (FMAs) in motor or sensory-related cortical regions were trained to perform a motor or a sensory task. Data from each monkey were preprocessed with three different schemes: TC, automatic sorting (AS), and manual sorting (MS). A support vector machine was used as the decoder, and the decoding accuracy was used for evaluating the performance of three preprocessing methods. Different neural signal sources, different decoders, and related parameters and decoding stability were further tested to systematically compare three preprocessing methods. MAIN RESULTS TC could achieve a similar (-4.5 RMS threshold) or better (-3.0 RMS threshold) decoding performance compared to the other two sorting methods in the motor or sensory tasks even if the neural signal sources or decoder-related parameters were changed. Moreover, TC was much more stable in neural decoding across sessions and robust to changes of threshold. SIGNIFICANCE Our results indicated that spike-firing patterns could be stably extracted through TC from multiple cortices in both motor and sensory neural decoding applications. Considering the stability of TC, it might be more suitable for neural decoding compared to sorting methods.
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Affiliation(s)
- Jun Dai
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, 27 Taiping Rd, Beijing 100850, People's Republic of China
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29
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Paninski L, Cunningham JP. Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience. Curr Opin Neurobiol 2019; 50:232-241. [PMID: 29738986 DOI: 10.1016/j.conb.2018.04.007] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 03/12/2018] [Accepted: 04/06/2018] [Indexed: 01/01/2023]
Abstract
Modern large-scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single-neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control-developed in lockstep with advances in experimental neurotechnology-promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time-series data with single-neuronal precision.
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Affiliation(s)
- L Paninski
- Department of Statistics, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States; Department of Neuroscience, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States.
| | - J P Cunningham
- Department of Statistics, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States
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30
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Yousefi A, Paulk AC, Basu I, Mirsky JL, Dougherty DD, Eskandar EN, Eden UT, Widge AS. COMPASS: An Open-Source, General-Purpose Software Toolkit for Computational Psychiatry. Front Neurosci 2019; 12:957. [PMID: 30686965 PMCID: PMC6336923 DOI: 10.3389/fnins.2018.00957] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 11/30/2018] [Indexed: 01/03/2023] Open
Abstract
Mathematical modeling of behavior during a psychophysical task, referred to as "computational psychiatry," could greatly improve our understanding of mental disorders. One barrier to the broader adoption of computational methods, is that they often require advanced statistical modeling and mathematical skills. Biological and behavioral signals often show skewed or non-Gaussian distributions, and very few toolboxes and analytical platforms are capable of processing such signal categories. We developed the Computational Psychiatry Adaptive State-Space (COMPASS) toolbox, an open-source MATLAB-based software package. This toolbox is easy to use and capable of integrating signals with a variety of distributions. COMPASS has the tools to process signals with continuous-valued and binary measurements, or signals with incomplete-missing or censored-measurements, which makes it well-suited for processing those signals captured during a psychophysical task. After specifying a few parameters in a small set of user-friendly functions, COMPASS allows users to efficiently apply a wide range of computational behavioral models. The model output can be analyzed as an experimental outcome or used as a regressor for neural data and can also be tested using the goodness-of-fit measurement. Here, we demonstrate that COMPASS can replicate two computational behavioral analyses from different groups. COMPASS replicates and can slightly improve on the original modeling results. We also demonstrate the use of COMPASS application in a censored-data problem and compare its performance result with naïve estimation methods. This flexible, general-purpose toolkit should accelerate the use of computational modeling in psychiatric neuroscience.
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Affiliation(s)
- Ali Yousefi
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.,Department of Mathematics and Statistics, Boston University, Boston, MA, United States
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Ishita Basu
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Jonathan L Mirsky
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Emad N Eskandar
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA, United States
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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31
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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.
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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.
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32
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Tao L, Weber KE, Arai K, Eden UT. A common goodness-of-fit framework for neural population models using marked point process time-rescaling. J Comput Neurosci 2018; 45:147-162. [PMID: 30298220 PMCID: PMC6208891 DOI: 10.1007/s10827-018-0698-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 09/16/2018] [Accepted: 09/26/2018] [Indexed: 11/27/2022]
Abstract
A critical component of any statistical modeling procedure is the ability to assess the goodness-of-fit between a model and observed data. For spike train models of individual neurons, many goodness-of-fit measures rely on the time-rescaling theorem and assess model quality using rescaled spike times. Recently, there has been increasing interest in statistical models that describe the simultaneous spiking activity of neuron populations, either in a single brain region or across brain regions. Classically, such models have used spike sorted data to describe relationships between the identified neurons, but more recently clusterless modeling methods have been used to describe population activity using a single model. Here we develop a generalization of the time-rescaling theorem that enables comprehensive goodness-of-fit analysis for either of these classes of population models. We use the theory of marked point processes to model population spiking activity, and show that under the correct model, each spike can be rescaled individually to generate a uniformly distributed set of events in time and the space of spike marks. After rescaling, multiple well-established goodness-of-fit procedures and statistical tests are available. We demonstrate the application of these methods both to simulated data and real population spiking in rat hippocampus. We have made the MATLAB and Python code used for the analyses in this paper publicly available through our Github repository at https://github.com/Eden-Kramer-Lab/popTRT .
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Affiliation(s)
- Long Tao
- Boston University College of Arts and Sciences, 111 Cummington Mall, Boston, MA, 02215, USA.
| | - Karoline E Weber
- Boston University College of Arts and Sciences, 111 Cummington Mall, Boston, MA, 02215, USA
| | - Kensuke Arai
- Boston University College of Arts and Sciences, 111 Cummington Mall, Boston, MA, 02215, USA
| | - Uri T Eden
- Boston University College of Arts and Sciences, 111 Cummington Mall, Boston, MA, 02215, USA
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33
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Zielinski MC, Tang W, Jadhav SP. The role of replay and theta sequences in mediating hippocampal-prefrontal interactions for memory and cognition. Hippocampus 2018; 30:60-72. [PMID: 29251801 DOI: 10.1002/hipo.22821] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 12/03/2017] [Accepted: 12/10/2017] [Indexed: 11/05/2022]
Abstract
Sequential activity is seen in the hippocampus during multiple network patterns, prominently as replay activity during both awake and sleep sharp-wave ripples (SWRs), and as theta sequences during active exploration. Although various mnemonic and cognitive functions have been ascribed to these hippocampal sequences, evidence for these proposed functions remains primarily phenomenological. Here, we briefly review current knowledge about replay events and theta sequences in spatial memory tasks. We reason that in order to gain a mechanistic and causal understanding of how these patterns influence memory and cognitive processing, it is important to consider how these sequences influence activity in other regions, and in particular, the prefrontal cortex, which is crucial for memory-guided behavior. For spatial memory tasks, we posit that hippocampal-prefrontal interactions mediated by replay and theta sequences play complementary and overlapping roles at different stages in learning, supporting memory encoding and retrieval, deliberative decision making, planning, and guiding future actions. This framework offers testable predictions for future physiology and closed-loop feedback inactivation experiments for specifically targeting hippocampal sequences as well as coordinated prefrontal activity in different network states, with the potential to reveal their causal roles in memory-guided behavior.
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Affiliation(s)
- Mark C Zielinski
- Graduate Program in Neuroscience, Brandeis University, Waltham, Massachusetts, 02453
| | - Wenbo Tang
- Graduate Program in Neuroscience, Brandeis University, Waltham, Massachusetts, 02453
| | - Shantanu P Jadhav
- Neuroscience Program, Department of Psychology and Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts, 02453
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34
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Shibue R, Komaki F. Firing rate estimation using infinite mixture models and its application to neural decoding. J Neurophysiol 2017; 118:2902-2913. [PMID: 28794199 DOI: 10.1152/jn.00818.2016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 07/12/2017] [Accepted: 08/06/2017] [Indexed: 11/22/2022] Open
Abstract
Neural decoding is a framework for reconstructing external stimuli from spike trains recorded by various neural recordings. Kloosterman et al. proposed a new decoding method using marked point processes (Kloosterman F, Layton SP, Chen Z, Wilson MA. J Neurophysiol 111: 217-227, 2014). This method does not require spike sorting and thereby improves decoding accuracy dramatically. In this method, they used kernel density estimation to estimate intensity functions of marked point processes. However, the use of kernel density estimation causes problems such as low decoding accuracy and high computational costs. To overcome these problems, we propose a new decoding method using infinite mixture models to estimate intensity. The proposed method improves decoding performance in terms of accuracy and computational speed. We apply the proposed method to simulation and experimental data to verify its performance.NEW & NOTEWORTHY We propose a new neural decoding method using infinite mixture models and nonparametric Bayesian statistics. The proposed method improves decoding performance in terms of accuracy and computation speed. We have successfully applied the proposed method to position decoding from spike trains recorded in a rat hippocampus.
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Affiliation(s)
- Ryohei Shibue
- Department of Mathematical Informatics, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; and
| | - Fumiyasu Komaki
- Department of Mathematical Informatics, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; and .,RIKEN Brain Science Institute, Wako City, Saitama, Japan
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35
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Chung JE, Magland JF, Barnett AH, Tolosa VM, Tooker AC, Lee KY, Shah KG, Felix SH, Frank LM, Greengard LF. A Fully Automated Approach to Spike Sorting. Neuron 2017; 95:1381-1394.e6. [PMID: 28910621 PMCID: PMC5743236 DOI: 10.1016/j.neuron.2017.08.030] [Citation(s) in RCA: 218] [Impact Index Per Article: 31.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Revised: 07/06/2017] [Accepted: 08/16/2017] [Indexed: 10/18/2022]
Abstract
Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible.
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Affiliation(s)
- Jason E Chung
- Neuroscience Graduate Program, Kavli Institute for Fundamental Neuroscience and Department of Physiology, University of California San Francisco, CA 94158, USA
| | - Jeremy F Magland
- Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA
| | - Alex H Barnett
- Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA; Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA
| | - Vanessa M Tolosa
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Angela C Tooker
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Kye Y Lee
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Kedar G Shah
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Sarah H Felix
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Loren M Frank
- Neuroscience Graduate Program, Kavli Institute for Fundamental Neuroscience and Department of Physiology, University of California San Francisco, CA 94158, USA; Howard Hughes Medical Institute.
| | - Leslie F Greengard
- Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA; Courant Institute, NYU, New York, NY 10012, USA
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36
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Li J, Li Z. Sums of Spike Waveform Features for Motor Decoding. Front Neurosci 2017; 11:406. [PMID: 28769745 PMCID: PMC5513987 DOI: 10.3389/fnins.2017.00406] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 06/30/2017] [Indexed: 11/23/2022] Open
Abstract
Traditionally, the key step before decoding motor intentions from cortical recordings is spike sorting, the process of identifying which neuron was responsible for an action potential. Recently, researchers have started investigating approaches to decoding which omit the spike sorting step, by directly using information about action potentials' waveform shapes in the decoder, though this approach is not yet widespread. Particularly, one recent approach involves computing the moments of waveform features and using these moment values as inputs to decoders. This computationally inexpensive approach was shown to be comparable in accuracy to traditional spike sorting. In this study, we use offline data recorded from two Rhesus monkeys to further validate this approach. We also modify this approach by using sums of exponentiated features of spikes, rather than moments. Our results show that using waveform feature sums facilitates significantly higher hand movement reconstruction accuracy than using waveform feature moments, though the magnitudes of differences are small. We find that using the sums of one simple feature, the spike amplitude, allows better offline decoding accuracy than traditional spike sorting by expert (correlation of 0.767, 0.785 vs. 0.744, 0.738, respectively, for two monkeys, average 16% reduction in mean-squared-error), as well as unsorted threshold crossings (0.746, 0.776; average 9% reduction in mean-squared-error). Our results suggest that the sums-of-features framework has potential as an alternative to both spike sorting and using unsorted threshold crossings, if developed further. Also, we present data comparing sorted vs. unsorted spike counts in terms of offline decoding accuracy. Traditional sorted spike counts do not include waveforms that do not match any template (“hash”), but threshold crossing counts do include this hash. On our data and in previous work, hash contributes to decoding accuracy. Thus, using the comparison between sorted spike counts and threshold crossing counts to evaluate the benefit of sorting is confounded by the presence of hash. We find that when the comparison is controlled for hash, performing sorting is better than not. These results offer a new perspective on the question of to sort or not to sort.
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Affiliation(s)
- Jie Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal UniversityBeijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China
| | - Zheng Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal UniversityBeijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China
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37
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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.
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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
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Truccolo W. From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining. JOURNAL OF PHYSIOLOGY, PARIS 2016; 110:336-347. [PMID: 28336305 PMCID: PMC5610574 DOI: 10.1016/j.jphysparis.2017.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 02/20/2017] [Accepted: 02/26/2017] [Indexed: 01/15/2023]
Abstract
This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles.
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Affiliation(s)
- Wilson Truccolo
- Department of Neuroscience and Institute for Brain Science, Brown University, Providence, USA; Center for Neurorestoration and Neurotechnology, U.S. Department of Veterans Affairs, Providence, USA.
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39
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Deng X, Liu DF, Karlsson MP, Frank LM, Eden UT. Rapid classification of hippocampal replay content for real-time applications. J Neurophysiol 2016; 116:2221-2235. [PMID: 27535369 DOI: 10.1152/jn.00151.2016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 08/17/2016] [Indexed: 11/22/2022] Open
Abstract
Sharp-wave ripple (SWR) events in the hippocampus replay millisecond-timescale patterns of place cell activity related to the past experience of an animal. Interrupting SWR events leads to learning and memory impairments, but how the specific patterns of place cell spiking seen during SWRs contribute to learning and memory remains unclear. A deeper understanding of this issue will require the ability to manipulate SWR events based on their content. Accurate real-time decoding of SWR replay events requires new algorithms that are able to estimate replay content and the associated uncertainty, along with software and hardware that can execute these algorithms for biological interventions on a millisecond timescale. Here we develop an efficient estimation algorithm to categorize the content of replay from multiunit spiking activity. Specifically, we apply real-time decoding methods to each SWR event and then compute the posterior probability of the replay feature. We illustrate this approach by classifying SWR events from data recorded in the hippocampus of a rat performing a spatial memory task into four categories: whether they represent outbound or inbound trajectories and whether the activity is replayed forward or backward in time. We show that our algorithm can classify the majority of SWR events in a recording epoch within 20 ms of the replay onset with high certainty, which makes the algorithm suitable for a real-time implementation with short latencies to incorporate into content-based feedback experiments.
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Affiliation(s)
- Xinyi Deng
- Department of Statistics, Columbia University, New York, New York; .,Grossman Center for the Statistics of Mind, Columbia University, New York, New York
| | - Daniel F Liu
- UC Berkeley-UCSF Graduate Program in Bioengineering, UCSF, San Francisco, California
| | | | - Loren M Frank
- Department of Physiology, UCSF, San Francisco, California.,Kavli Institute for Fundamental Neuroscience, UCSF, San Francisco, California.,Howard Hughes Medical Institute, UCSF, San Francisco, California; and
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts
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Oby ER, Perel S, Sadtler PT, Ruff DA, Mischel JL, Montez DF, Cohen MR, Batista AP, Chase SM. Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters. J Neural Eng 2016; 13:036009. [PMID: 27097901 DOI: 10.1088/1741-2560/13/3/036009] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain-computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation. However, the optimal threshold for single-neuron identification is not necessarily the optimal threshold for information extraction. Here we introduce a procedure to determine the best threshold for extracting information from extracellular recordings. We apply this procedure in two distinct contexts: the encoding of kinematic parameters from neural activity in primary motor cortex (M1), and visual stimulus parameters from neural activity in primary visual cortex (V1). APPROACH We record extracellularly from multi-electrode arrays implanted in M1 or V1 in monkeys. Then, we systematically sweep the voltage detection threshold and quantify the information conveyed by the corresponding threshold crossings. MAIN RESULTS The optimal threshold depends on the desired information. In M1, velocity is optimally encoded at higher thresholds than speed; in both cases the optimal thresholds are lower than are typically used in BCI applications. In V1, information about the orientation of a visual stimulus is optimally encoded at higher thresholds than is visual contrast. A conceptual model explains these results as a consequence of cortical topography. SIGNIFICANCE How neural signals are processed impacts the information that can be extracted from them. Both the type and quality of information contained in threshold crossings depend on the threshold setting. There is more information available in these signals than is typically extracted. Adjusting the detection threshold to the parameter of interest in a BCI context should improve our ability to decode motor intent, and thus enhance BCI control. Further, by sweeping the detection threshold, one can gain insights into the topographic organization of the nearby neural tissue.
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Affiliation(s)
- Emily R Oby
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213, USA. Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
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