151
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Hahn G, Ponce-Alvarez A, Monier C, Benvenuti G, Kumar A, Chavane F, Deco G, Frégnac Y. Spontaneous cortical activity is transiently poised close to criticality. PLoS Comput Biol 2017; 13:e1005543. [PMID: 28542191 PMCID: PMC5464673 DOI: 10.1371/journal.pcbi.1005543] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 06/08/2017] [Accepted: 04/26/2017] [Indexed: 11/19/2022] Open
Abstract
Brain activity displays a large repertoire of dynamics across the sleep-wake cycle and even during anesthesia. It was suggested that criticality could serve as a unifying principle underlying the diversity of dynamics. This view has been supported by the observation of spontaneous bursts of cortical activity with scale-invariant sizes and durations, known as neuronal avalanches, in recordings of mesoscopic cortical signals. However, the existence of neuronal avalanches in spiking activity has been equivocal with studies reporting both its presence and absence. Here, we show that signs of criticality in spiking activity can change between synchronized and desynchronized cortical states. We analyzed the spontaneous activity in the primary visual cortex of the anesthetized cat and the awake monkey, and found that neuronal avalanches and thermodynamic indicators of criticality strongly depend on collective synchrony among neurons, LFP fluctuations, and behavioral state. We found that synchronized states are associated to criticality, large dynamical repertoire and prolonged epochs of eye closure, while desynchronized states are associated to sub-criticality, reduced dynamical repertoire, and eyes open conditions. Our results show that criticality in cortical dynamics is not stationary, but fluctuates during anesthesia and between different vigilance states.
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Affiliation(s)
- Gerald Hahn
- Unité de Neuroscience, Information et Complexité (UNIC), CNRS, Gif-sur-Yvette, France
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Adrian Ponce-Alvarez
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Cyril Monier
- Unité de Neuroscience, Information et Complexité (UNIC), CNRS, Gif-sur-Yvette, France
| | | | - Arvind Kumar
- Bernstein Center for Computational Neuroscience, Freiburg, Germany
- Dept. of Computational Science and Technology, School of Computer Science and Communication, KTH, Royal Institute of Technology, Stockholm, Sweden
| | - Frédéric Chavane
- Institut des Neurosciences de la Timone, CNRS, Marseille, France
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats, Universitat Pompeu Fabra, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Clayton, Victoria, Australia
| | - Yves Frégnac
- Unité de Neuroscience, Information et Complexité (UNIC), CNRS, Gif-sur-Yvette, France
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152
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Baglietto G, Gigante G, Del Giudice P. Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis. PLoS One 2017; 12:e0174918. [PMID: 28369106 PMCID: PMC5378378 DOI: 10.1371/journal.pone.0174918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Accepted: 03/17/2017] [Indexed: 11/18/2022] Open
Abstract
Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the ‘mean-shift’ algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters’ centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network’s state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.
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Affiliation(s)
- Gabriel Baglietto
- INFN-Roma1, Italian National Institute for Nuclear Research (INFN), Rome, Italy
- IFLYSIB Instituto de Física de Líquidos y Sistemas Biológicos (UNLP-CONICET), La Plata, Argentina
- * E-mail:
| | - Guido Gigante
- Italian Institute of Health (ISS), Rome, Italy
- Mperience srl, Rome, Italy
| | - Paolo Del Giudice
- INFN-Roma1, Italian National Institute for Nuclear Research (INFN), Rome, Italy
- Italian Institute of Health (ISS), Rome, Italy
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153
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Itoh M, Leleu T. Modulation of Context-Dependent Spatiotemporal Patterns within Packets of Spiking Activity. Neural Comput 2017; 29:1263-1292. [PMID: 28333586 DOI: 10.1162/neco_a_00952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Recent experiments have shown that stereotypical spatiotemporal patterns occur during brief packets of spiking activity in the cortex, and it has been suggested that top-down inputs can modulate these patterns according to the context. We propose a simple model that may explain important features of these experimental observations and is analytically tractable. The key mechanism underlying this model is that context-dependent top-down inputs can modulate the effective connection strengths between neurons because of short-term synaptic depression. As a result, the degree of synchrony and, in turn, the spatiotemporal patterns of spiking activity that occur during packets are modulated by the top-down inputs. This is shown using an analytical framework, based on avalanche dynamics, that allows calculating the probability that a given neuron spikes during a packet and numerical simulations. Finally, we show that the spatiotemporal patterns that replay previously experienced sequential stimuli and their binding with their corresponding context can be learned because of spike-timing-dependent plasticity.
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Affiliation(s)
- Miho Itoh
- Keio University, Kohoku-ku, Yokohama 223-8521, Japan
| | - Timothée Leleu
- Institute of Industrial Science, University of Tokyo, Meguro-ku, Tokyo 153-8505, Japan
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154
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Karageorgiou E, Vossel KA. Brain rhythm attractor breakdown in Alzheimer's disease: Functional and pathologic implications. Alzheimers Dement 2017; 13:1054-1067. [PMID: 28302453 DOI: 10.1016/j.jalz.2017.02.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2016] [Revised: 01/30/2017] [Accepted: 02/04/2017] [Indexed: 12/11/2022]
Abstract
This perspective binds emerging evidence on the bidirectional relationship between Alzheimer's disease (AD) and sleep disorders through a model of brain rhythm attractor breakdown. This approach explains behavioral-cognitive changes in AD across the sleep-wake cycle and supports a causal association between early brainstem tau pathology and subsequent cortical amyloid β accumulation. Specifically, early tau dysregulation within brainstem-hypothalamic nuclei leads to breakdown of sleep-wake attractor networks, with patients displaying an attenuated range of behavioral and electrophysiological activity patterns, a "twilight zone" of constant activity between deep rest and full alertness. This constant cortical activity promotes activity-dependent amyloid β accumulation in brain areas that modulate their activity across sleep-wake states, especially the medial prefrontal cortex. In addition, the accompanying breakdown of hippocampal-medial prefrontal cortex interplay across sleep stages could explain deficient memory consolidation through dysregulation of synaptic plasticity. Clinical implications include the potential therapeutic benefit of attractor consolidation (e.g., slow-wave sleep enhancers) in delaying AD progression.
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Affiliation(s)
- Elissaios Karageorgiou
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA; Neurological Institute of Athens, Athens, Greece.
| | - Keith A Vossel
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA; Gladstone Institute of Neurological Disease, San Francisco, CA, USA
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155
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Giret N, Edeline JM, Del Negro C. Neural mechanisms of vocal imitation: The role of sleep replay in shaping mirror neurons. Neurosci Biobehav Rev 2017; 77:58-73. [PMID: 28288397 DOI: 10.1016/j.neubiorev.2017.01.051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 01/04/2017] [Accepted: 01/04/2017] [Indexed: 01/19/2023]
Abstract
Learning by imitation involves not only perceiving another individual's action to copy it, but also the formation of a memory trace in order to gradually establish a correspondence between the sensory and motor codes, which represent this action through sensorimotor experience. Memory and sensorimotor processes are closely intertwined. Mirror neurons, which fire both when the same action is performed or perceived, have received considerable attention in the context of imitation. An influential view of memory processes considers that the consolidation of newly acquired information or skills involves an active offline reprocessing of memories during sleep within the neuronal networks that were initially used for encoding. Here, we review the recent advances in the field of mirror neurons and offline processes in the songbird. We further propose a theoretical framework that could establish the neurobiological foundations of sensorimotor learning by imitation. We propose that the reactivation of neuronal assemblies during offline periods contributes to the integration of sensory feedback information and the establishment of sensorimotor mirroring activity at the neuronal level.
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Affiliation(s)
- Nicolas Giret
- Neuroscience Paris-Saclay Institute, CNRS, Université Paris Sud, Université Paris Saclay, Orsay, France.
| | - Jean-Marc Edeline
- Neuroscience Paris-Saclay Institute, CNRS, Université Paris Sud, Université Paris Saclay, Orsay, France.
| | - Catherine Del Negro
- Neuroscience Paris-Saclay Institute, CNRS, Université Paris Sud, Université Paris Saclay, Orsay, France.
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156
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Mitra A, Snyder AZ, Constantino JN, Raichle ME. The Lag Structure of Intrinsic Activity is Focally Altered in High Functioning Adults with Autism. Cereb Cortex 2017; 27:1083-1093. [PMID: 26656726 PMCID: PMC6375249 DOI: 10.1093/cercor/bhv294] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The behaviors that define autism spectrum disorders (ASDs) have been hypothesized to result from disordered communication within brain networks. Several groups have investigated this question using resting-state functional magnetic resonance imaging (RS-fMRI). However, the published findings to date have been inconsistent across laboratories. Prior RS-fMRI studies of ASD have employed conventional analysis techniques based on the assumption that intrinsic brain activity is exactly synchronous over widely separated parts of the brain. By relaxing the assumption of synchronicity and focusing, instead, on lags between time series, we have recently demonstrated highly reproducible patterns of temporally lagged activity in normal human adults. We refer to this analysis technique as resting-state lag analysis (RS-LA). Here, we report RS-LA as well as conventional analyses of RS-fMRI in adults with ASD and demographically matched controls. RS-LA analyses demonstrated significant group differences in rs-fMRI lag structure in frontopolar cortex, occipital cortex, and putamen. Moreover, the degree of abnormality in individuals was highly correlated with behavioral measures relevant to the diagnosis of ASD. In this sample, no significant group differences were observed using conventional RS-fMRI analysis techniques. Our results suggest that altered propagation of intrinsic activity may contribute to abnormal brain function in ASD.
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157
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Russo E, Durstewitz D. Cell assemblies at multiple time scales with arbitrary lag constellations. eLife 2017; 6. [PMID: 28074777 PMCID: PMC5226654 DOI: 10.7554/elife.19428] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 10/27/2016] [Indexed: 12/04/2022] Open
Abstract
Hebb's idea of a cell assembly as the fundamental unit of neural information processing has dominated neuroscience like no other theoretical concept within the past 60 years. A range of different physiological phenomena, from precisely synchronized spiking to broadly simultaneous rate increases, has been subsumed under this term. Yet progress in this area is hampered by the lack of statistical tools that would enable to extract assemblies with arbitrary constellations of time lags, and at multiple temporal scales, partly due to the severe computational burden. Here we present such a unifying methodological and conceptual framework which detects assembly structure at many different time scales, levels of precision, and with arbitrary internal organization. Applying this methodology to multiple single unit recordings from various cortical areas, we find that there is no universal cortical coding scheme, but that assembly structure and precision significantly depends on the brain area recorded and ongoing task demands. DOI:http://dx.doi.org/10.7554/eLife.19428.001
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Affiliation(s)
- Eleonora Russo
- Department of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience, Central Institute for Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience, Central Institute for Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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158
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Gretenkord S, Rees A, Whittington MA, Gartside SE, LeBeau FEN. Dorsal vs. ventral differences in fast Up-state-associated oscillations in the medial prefrontal cortex of the urethane-anesthetized rat. J Neurophysiol 2016; 117:1126-1142. [PMID: 28003411 PMCID: PMC5340880 DOI: 10.1152/jn.00762.2016] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Revised: 12/20/2016] [Accepted: 12/21/2016] [Indexed: 01/08/2023] Open
Abstract
We demonstrate, in the urethane-anesthetized rat, that within the medial prefrontal cortex (mPFC) there are clear subregional differences in the fast network oscillations associated with the slow oscillation Up-state. These differences, particularly between the dorsal and ventral subregions of the mPFC, may reflect the different functions and connectivity of these subregions. Cortical slow oscillations (0.1–1 Hz), which may play a role in memory consolidation, are a hallmark of non-rapid eye movement (NREM) sleep and also occur under anesthesia. During slow oscillations the neuronal network generates faster oscillations on the active Up-states and these nested oscillations are particularly prominent in the PFC. In rodents the medial prefrontal cortex (mPFC) consists of several subregions: anterior cingulate cortex (ACC), prelimbic (PrL), infralimbic (IL), and dorsal peduncular cortices (DP). Although each region has a distinct anatomy and function, it is not known whether slow or fast network oscillations differ between subregions in vivo. We have simultaneously recorded slow and fast network oscillations in all four subregions of the rodent mPFC under urethane anesthesia. Slow oscillations were synchronous between the mPFC subregions, and across the hemispheres, with no consistent amplitude difference between subregions. Delta (2–4 Hz) activity showed only small differences between subregions. However, oscillations in the spindle (6–15 Hz)-, beta (20–30 Hz), gamma (30–80 Hz)-, and high-gamma (80–150 Hz)-frequency bands were consistently larger in the dorsal regions (ACC and PrL) compared with ventral regions (IL and DP). In dorsal regions the peak power of spindle, beta, and gamma activity occurred early after onset of the Up-state. In the ventral regions, especially the DP, the oscillatory power in the spindle-, beta-, and gamma-frequency ranges peaked later in the Up-state. These results suggest variations in fast network oscillations within the mPFC that may reflect the different functions and connectivity of these subregions. NEW & NOTEWORTHY We demonstrate, in the urethane-anesthetized rat, that within the medial prefrontal cortex (mPFC) there are clear subregional differences in the fast network oscillations associated with the slow oscillation Up-state. These differences, particularly between the dorsal and ventral subregions of the mPFC, may reflect the different functions and connectivity of these subregions.
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Affiliation(s)
- Sabine Gretenkord
- Institute of Neuroscience, Newcastle University, Medical School, Newcastle-upon-Tyne, United Kingdom.,Developmental Neurophysiology, Institute of Neuroanatomy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; and
| | - Adrian Rees
- Institute of Neuroscience, Newcastle University, Medical School, Newcastle-upon-Tyne, United Kingdom
| | - Miles A Whittington
- York-Hull Medical School, F1-Department of Biology, York University, Heslington, United Kingdom
| | - Sarah E Gartside
- Institute of Neuroscience, Newcastle University, Medical School, Newcastle-upon-Tyne, United Kingdom
| | - Fiona E N LeBeau
- Institute of Neuroscience, Newcastle University, Medical School, Newcastle-upon-Tyne, United Kingdom;
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159
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Mankin R, Rekker A. Response to a periodic stimulus in a perfect integrate-and-fire neuron model driven by colored noise. Phys Rev E 2016; 94:062103. [PMID: 28085436 DOI: 10.1103/physreve.94.062103] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Indexed: 06/06/2023]
Abstract
The output interspike interval statistics of a stochastic perfect integrate-and-fire neuron model driven by an additive exogenous periodic stimulus is considered. The effect of temporally correlated random activity of synaptic inputs is modeled by an additive symmetric dichotomous noise. Using a first-passage-time formulation, exact expressions for the output interspike interval density and for the serial correlation coefficient are derived in the nonsteady regime, and their dependence on input parameters (e.g., the noise correlation time and amplitude as well as the frequency of an input current) is analyzed. It is shown that an interplay of a periodic forcing and colored noise can cause a variety of nonequilibrium cooperation effects, such as sign reversals of the interspike interval correlations versus noise-switching rate as well as versus the frequency of periodic forcing, a power-law-like decay of oscillations of the serial correlation coefficients in the long-lag limit, amplification of the output signal modulation in the instantaneous firing rate of the neural response, etc. The features of spike statistics in the limits of slow and fast noises are also discussed.
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Affiliation(s)
- Romi Mankin
- School of Natural Sciences and Health, Tallinn University, 29 Narva Road, 10120 Tallinn, Estonia
| | - Astrid Rekker
- School of Natural Sciences and Health, Tallinn University, 29 Narva Road, 10120 Tallinn, Estonia
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160
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Li Y, Sun R, Wang Y, Li H, Zheng X. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment. PLoS One 2016; 11:e0165600. [PMID: 27806074 PMCID: PMC5091833 DOI: 10.1371/journal.pone.0165600] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Accepted: 10/15/2016] [Indexed: 11/19/2022] Open
Abstract
We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.
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Affiliation(s)
- Yongcheng Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
- University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Rong Sun
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, P. R. China
| | - Yuechao Wang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
| | - Hongyi Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
| | - Xiongfei Zheng
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China
- * E-mail:
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161
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Abstract
The cortex connects to the thalamus via extensive corticothalamic (CT) pathways, but their function in vivo is not well understood. We investigated "top-down" signaling from cortex to thalamus via the cortical layer 5B (L5B) to posterior medial nucleus (POm) pathway in the whisker system of the anesthetized mouse. While L5B CT inputs to POm are extremely strong in vitro, ongoing activity of L5 neurons in vivo might tonically depress these inputs and thereby block CT spike transfer. We find robust transfer of spikes from the cortex to the thalamus, mediated by few L5B-POm synapses. However, the gain of this pathway is not constant but instead is controlled by global cortical Up and Down states. We characterized in vivo CT spike transfer by analyzing unitary PSPs and found that a minority of PSPs drove POm spikes when CT gain peaked at the beginning of Up states. CT gain declined sharply during Up states due to frequency-dependent adaptation, resulting in periodic high gain-low gain oscillations. We estimate that POm neurons receive few (2-3) active L5B inputs. Thus, the L5B-POm pathway strongly amplifies the output of a few L5B neurons and locks thalamic POm sub-and suprathreshold activity to cortical L5B spiking.
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Affiliation(s)
- Rebecca A. Mease
- Institute for Neuroscience of the Technische Universität München, 80802 Munich, Germany
- Department of Neurosurgery, Klinikum rechts der Isar, Technische Universität München, 81675 Munich, Germany
| | - Anton Sumser
- Institute for Neuroscience of the Technische Universität München, 80802 Munich, Germany
| | - Bert Sakmann
- Institute for Neuroscience of the Technische Universität München, 80802 Munich, Germany
- Max Planck Institute for Neurobiology, 82152 Martinsried, Germany
| | - Alexander Groh
- Institute for Neuroscience of the Technische Universität München, 80802 Munich, Germany
- Department of Neurosurgery, Klinikum rechts der Isar, Technische Universität München, 81675 Munich, Germany
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162
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Tavoni G, Cocco S, Monasson R. Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings. J Comput Neurosci 2016; 41:269-293. [PMID: 27469424 DOI: 10.1007/s10827-016-0617-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2015] [Revised: 07/07/2016] [Accepted: 07/12/2016] [Indexed: 01/08/2023]
Abstract
We present two graphical model-based approaches to analyse the distribution of neural activities in the prefrontal cortex of behaving rats. The first method aims at identifying cell assemblies, groups of synchronously activating neurons possibly representing the units of neural coding and memory. A graphical (Ising) model distribution of snapshots of the neural activities, with an effective connectivity matrix reproducing the correlation statistics, is inferred from multi-electrode recordings, and then simulated in the presence of a virtual external drive, favoring high activity (multi-neuron) configurations. As the drive increases groups of neurons may activate together, and reveal the existence of cell assemblies. The identified groups are then showed to strongly coactivate in the neural spiking data and to be highly specific of the inferred connectivity network, which offers a sparse representation of the correlation pattern across neural cells. The second method relies on the inference of a Generalized Linear Model, in which spiking events are integrated over time by neurons through an effective connectivity matrix. The functional connectivity matrices inferred with the two approaches are compared. Sampling of the inferred GLM distribution allows us to study the spatio-temporal patterns of activation of neurons within the identified cell assemblies, particularly their activation order: the prevalence of one order with respect to the others is weak and reflects the neuron average firing rates and the strength of the largest effective connections. Other properties of the identified cell assemblies (spatial distribution of coactivation events and firing rates of coactivating neurons) are discussed.
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Affiliation(s)
- G Tavoni
- Laboratoire de Physique Statistique, Ecole Normale Supérieure, CNRS, PSL Research, Sorbonne Université UPMC, Paris, France. .,Laboratoire de Physique Théorique, Ecole Normale Supérieure, CNRS, PSL Research, Sorbonne Université UPMC, Paris, France. .,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - S Cocco
- Laboratoire de Physique Statistique, Ecole Normale Supérieure, CNRS, PSL Research, Sorbonne Université UPMC, Paris, France
| | - R Monasson
- Laboratoire de Physique Théorique, Ecole Normale Supérieure, CNRS, PSL Research, Sorbonne Université UPMC, Paris, France
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163
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Alegre-Cortés J, Soto-Sánchez C, Pizá ÁG, Albarracín AL, Farfán FD, Felice CJ, Fernández E. Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition. J Neurosci Methods 2016; 267:35-44. [PMID: 27044801 DOI: 10.1016/j.jneumeth.2016.03.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 03/21/2016] [Accepted: 03/28/2016] [Indexed: 11/16/2022]
Abstract
BACKGROUND Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. NEW METHOD In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. RESULTS The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. COMPARISON WITH EXISTING METHODS Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. CONCLUSIONS Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods.
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Affiliation(s)
- J Alegre-Cortés
- Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain
| | - C Soto-Sánchez
- Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain; Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Á G Pizá
- Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina
| | - A L Albarracín
- Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina
| | - F D Farfán
- Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina
| | - C J Felice
- Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina
| | - E Fernández
- Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain; Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain.
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164
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Sabri MM, Adibi M, Arabzadeh E. Dynamics of Population Activity in Rat Sensory Cortex: Network Correlations Predict Anatomical Arrangement and Information Content. Front Neural Circuits 2016; 10:49. [PMID: 27458347 PMCID: PMC4933716 DOI: 10.3389/fncir.2016.00049] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 06/22/2016] [Indexed: 12/14/2022] Open
Abstract
To study the spatiotemporal dynamics of neural activity in a cortical population, we implanted a 10 × 10 microelectrode array in the vibrissal cortex of urethane-anesthetized rats. We recorded spontaneous neuronal activity as well as activity evoked in response to sustained and brief sensory stimulation. To quantify the temporal dynamics of activity, we computed the probability distribution function (PDF) of spiking on one electrode given the observation of a spike on another. The spike-triggered PDFs quantified the strength, temporal delay, and temporal precision of correlated activity across electrodes. Nearby cells showed higher levels of correlation at short delays, whereas distant cells showed lower levels of correlation, which tended to occur at longer delays. We found that functional space built based on the strength of pairwise correlations predicted the anatomical arrangement of electrodes. Moreover, the correlation profile of electrode pairs during spontaneous activity predicted the "signal" and "noise" correlations during sensory stimulation. Finally, mutual information analyses revealed that neurons with stronger correlations to the network during spontaneous activity, conveyed higher information about the sensory stimuli in their evoked response. Given the 400-μm-distance between adjacent electrodes, our functional quantifications unravel the spatiotemporal dynamics of activity among nearby and distant cortical columns.
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Affiliation(s)
- Mohammad Mahdi Sabri
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM)Tehran, Iran; Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National UniversityCanberra, ACT, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, The Australian National University NodeCanberra, ACT, Australia
| | - Mehdi Adibi
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National UniversityCanberra, ACT, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, The Australian National University NodeCanberra, ACT, Australia; School of Psychology, University of New South WalesSydney, NSW, Australia
| | - Ehsan Arabzadeh
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National UniversityCanberra, ACT, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, The Australian National University NodeCanberra, ACT, Australia
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165
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Goel A, Buonomano DV. Temporal Interval Learning in Cortical Cultures Is Encoded in Intrinsic Network Dynamics. Neuron 2016; 91:320-7. [PMID: 27346530 DOI: 10.1016/j.neuron.2016.05.042] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 03/22/2016] [Accepted: 05/24/2016] [Indexed: 10/21/2022]
Abstract
Telling time and anticipating when external events will happen is among the most important tasks the brain performs. Yet the neural mechanisms underlying timing remain elusive. One theory proposes that timing is a general and intrinsic computation of cortical circuits. We tested this hypothesis using electrical and optogenetic stimulation to determine if brain slices could "learn" temporal intervals. Presentation of intervals between 100 and 500 ms altered the temporal profile of evoked network activity in an interval and pathway-specific manner-suggesting that the network learned to anticipate an expected stimulus. Recordings performed during training revealed a progressive increase in evoked network activity, followed by subsequent refinement of temporal dynamics, which was related to a time-window-specific increase in the excitatory-inhibitory balance. These results support the hypothesis that subsecond timing is an intrinsic computation and that timing emerges from network-wide, yet pathway-specific, changes in evoked neural dynamics.
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Affiliation(s)
- Anubhuti Goel
- Department of Neurology, University of California, Los Angeles, Reed Neurological Research Ctr-A-145, 710 Westwood Plaza, Los Angeles, CA 90095, USA
| | - Dean V Buonomano
- Departments of Neurobiology and Psychology, Integrative Center for Learning and Memory, University of California, Los Angeles, 695 Young Drive, Los Angeles, CA 90095, USA.
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166
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Translaminar Cortical Membrane Potential Synchrony in Behaving Mice. Cell Rep 2016; 15:2387-99. [PMID: 27264185 PMCID: PMC4914774 DOI: 10.1016/j.celrep.2016.05.026] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 03/24/2016] [Accepted: 05/04/2016] [Indexed: 11/23/2022] Open
Abstract
The synchronized activity of six layers of cortical neurons is critical for sensory perception and the control of voluntary behavior, but little is known about the synaptic mechanisms of cortical synchrony across layers in behaving animals. We made single and dual whole-cell recordings from the primary somatosensory forepaw cortex in awake mice and show that L2/3 and L5 excitatory neurons have layer-specific intrinsic properties and membrane potential dynamics that shape laminar-specific firing rates and subthreshold synchrony. First, while sensory and movement-evoked synaptic input was tightly correlated across layers, spontaneous action potentials and slow spontaneous subthreshold fluctuations had laminar-specific timing; second, longer duration forepaw movement was associated with a decorrelation of subthreshold activity; third, spontaneous and sensory-evoked forepaw movements were signaled more strongly by L5 than L2/3 neurons. Together, our data suggest that the degree of translaminar synchrony is dependent upon the origin (sensory, spontaneous, and movement) of the synaptic input. We made dual whole-cell recordings from L2/3 and L5 cortical neurons in behaving mice Layer-specific membrane properties determine higher mean firing rates of L5 neurons Synchrony of translaminar synaptic activity is determined by the origin of input L5 neurons signal spontaneous and sensory-triggered movements
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167
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Watson BO, Levenstein D, Greene JP, Gelinas JN, Buzsáki G. Network Homeostasis and State Dynamics of Neocortical Sleep. Neuron 2016; 90:839-52. [PMID: 27133462 PMCID: PMC4873379 DOI: 10.1016/j.neuron.2016.03.036] [Citation(s) in RCA: 185] [Impact Index Per Article: 23.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Revised: 02/22/2016] [Accepted: 03/30/2016] [Indexed: 12/23/2022]
Abstract
Sleep exerts many effects on mammalian forebrain networks, including homeostatic effects on both synaptic strengths and firing rates. We used large-scale recordings to examine the activity of neurons in the frontal cortex of rats and first observed that the distribution of pyramidal cell firing rates was wide and strongly skewed toward high firing rates. Moreover, neurons from different parts of that distribution were differentially modulated by sleep substates. Periods of nonREM sleep reduced the activity of high firing rate neurons and tended to upregulate firing of slow-firing neurons. By contrast, the effect of REM was to reduce firing rates across the entire rate spectrum. Microarousals, interspersed within nonREM epochs, increased firing rates of slow-firing neurons. The net result of sleep was to homogenize the firing rate distribution. These findings are at variance with current homeostatic models and provide a novel view of sleep in adjusting network excitability.
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Affiliation(s)
- Brendon O Watson
- New York University Neuroscience Institute, New York University, New York, NY 10016, USA; Department of Psychiatry, Weill Cornell Medical College, New York, NY 10065, USA
| | - Daniel Levenstein
- New York University Neuroscience Institute, New York University, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10016, USA
| | - J Palmer Greene
- New York University Neuroscience Institute, New York University, New York, NY 10016, USA
| | - Jennifer N Gelinas
- New York University Neuroscience Institute, New York University, New York, NY 10016, USA
| | - György Buzsáki
- New York University Neuroscience Institute, New York University, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10016, USA.
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168
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Hippocampo-cortical coupling mediates memory consolidation during sleep. Nat Neurosci 2016; 19:959-64. [PMID: 27182818 DOI: 10.1038/nn.4304] [Citation(s) in RCA: 371] [Impact Index Per Article: 46.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 04/14/2016] [Indexed: 02/08/2023]
Abstract
Memory consolidation is thought to involve a hippocampo-cortical dialog during sleep to stabilize labile memory traces for long-term storage. However, direct evidence supporting this hypothesis is lacking. We dynamically manipulated the temporal coordination between the two structures during sleep following training on a spatial memory task specifically designed to trigger encoding, but not memory consolidation. Reinforcing the endogenous coordination between hippocampal sharp wave-ripples, cortical delta waves and spindles by timed electrical stimulation resulted in a reorganization of prefrontal cortical networks, along with subsequent increased prefrontal responsivity to the task and high recall performance on the next day, contrary to control rats, which performed at chance levels. Our results provide, to the best of our knowledge, the first direct evidence for a causal role of a hippocampo-cortical dialog during sleep in memory consolidation, and indicate that the underlying mechanism involves a fine-tuned coordination between sharp wave-ripples, delta waves and spindles.
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169
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Transient neuronal coactivations embedded in globally propagating waves underlie resting-state functional connectivity. Proc Natl Acad Sci U S A 2016; 113:6556-61. [PMID: 27185944 DOI: 10.1073/pnas.1521299113] [Citation(s) in RCA: 130] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Resting-state functional connectivity (FC), which measures the correlation of spontaneous hemodynamic signals (HemoS) between brain areas, is widely used to study brain networks noninvasively. It is commonly assumed that spatial patterns of HemoS-based FC (Hemo-FC) reflect large-scale dynamics of underlying neuronal activity. To date, studies of spontaneous neuronal activity cataloged heterogeneous types of events ranging from waves of activity spanning the entire neocortex to flash-like activations of a set of anatomically connected cortical areas. However, it remains unclear how these various types of large-scale dynamics are interrelated. More importantly, whether each type of large-scale dynamics contributes to Hemo-FC has not been explored. Here, we addressed these questions by simultaneously monitoring neuronal calcium signals (CaS) and HemoS in the entire neocortex of mice at high spatiotemporal resolution. We found a significant relationship between two seemingly different types of large-scale spontaneous neuronal activity-namely, global waves propagating across the neocortex and transient coactivations among cortical areas sharing high FC. Different sets of cortical areas, sharing high FC within each set, were coactivated at different timings of the propagating global waves, suggesting that spatial information of cortical network characterized by FC was embedded in the phase of the global waves. Furthermore, we confirmed that such transient coactivations in CaS were indeed converted into spatially similar coactivations in HemoS and were necessary to sustain the spatial structure of Hemo-FC. These results explain how global waves of spontaneous neuronal activity propagating across large-scale cortical network contribute to Hemo-FC in the resting state.
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170
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Windels F, Yan S, Stratton PG, Sullivan R, Crane JW, Sah P. Auditory Tones and Foot-Shock Recapitulate Spontaneous Sub-Threshold Activity in Basolateral Amygdala Principal Neurons and Interneurons. PLoS One 2016; 11:e0155192. [PMID: 27171164 PMCID: PMC4865267 DOI: 10.1371/journal.pone.0155192] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Accepted: 04/25/2016] [Indexed: 11/18/2022] Open
Abstract
In quiescent states such as anesthesia and slow wave sleep, cortical networks show slow rhythmic synchronized activity. In sensory cortices this rhythmic activity shows a stereotypical pattern that is recapitulated by stimulation of the appropriate sensory modality. The amygdala receives sensory input from a variety of sources, and in anesthetized animals, neurons in the basolateral amygdala (BLA) show slow rhythmic synchronized activity. Extracellular field potential recordings show that these oscillations are synchronized with sensory cortex and the thalamus, with both the thalamus and cortex leading the BLA. Using whole-cell recording in vivo we show that the membrane potential of principal neurons spontaneously oscillates between up- and down-states. Footshock and auditory stimulation delivered during down-states evokes an up-state that fully recapitulates those occurring spontaneously. These results suggest that neurons in the BLA receive convergent input from networks of cortical neurons with slow oscillatory activity and that somatosensory and auditory stimulation can trigger activity in these same networks.
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Affiliation(s)
- François Windels
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
- Asia Pacific Centre for Neuromodulation, Queensland Brain Institute, Brisbane, Queensland, Australia
- * E-mail:
| | - Shanzhi Yan
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Peter G. Stratton
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
- Asia Pacific Centre for Neuromodulation, Queensland Brain Institute, Brisbane, Queensland, Australia
| | - Robert Sullivan
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - James W. Crane
- School of Biomedical Sciences, Charles Sturt University, Bathurst, New South Wales, Australia
| | - Pankaj Sah
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
- Asia Pacific Centre for Neuromodulation, Queensland Brain Institute, Brisbane, Queensland, Australia
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171
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Frye CG, MacLean JN. Spontaneous activations follow a common developmental course across primary sensory areas in mouse neocortex. J Neurophysiol 2016; 116:431-7. [PMID: 27146981 DOI: 10.1152/jn.00172.2016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Accepted: 05/01/2016] [Indexed: 11/22/2022] Open
Abstract
Spontaneous propagation of spiking within the local neocortical circuits of mature primary sensory areas is highly nonrandom, engaging specific sets of interconnected and functionally related neurons. These spontaneous activations promise insight into neocortical structure and function, but their properties in the first 2 wk of perinatal development are incompletely characterized. Previously, we have found that there is a minimal numerical sample, on the order of 400 cells, necessary to fully capture mature neocortical circuit dynamics. Therefore we maximized our numerical sample by using two-photon calcium imaging to observe spontaneous activity in populations of up to 1,062 neurons spanning multiple columns and layers in 52 acute coronal slices of mouse neocortex at each day from postnatal day (PND) 3 to PND 15. Slices contained either primary auditory cortex (A1) or somatosensory barrel field (S1BF), which allowed us to compare sensory modalities with markedly different developmental timelines. Between PND 3 and PND 8, populations in both areas exhibited activations of anatomically compact subgroups on the order of dozens of cells. Between PND 9 and PND 13, the spatiotemporal structure of the activity diversified to include spatially distributed activations encompassing hundreds of cells. Sparse activations covering the entire field of view dominated in slices taken on or after PND 14. These and other findings demonstrate that the developmental progression of spontaneous activations from active local modules in the first postnatal week to sparse, intermingled groups of neurons at the beginning of the third postnatal week generalizes across primary sensory areas, consistent with an intrinsic developmental trajectory independent of sensory input.
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Affiliation(s)
- Charles G Frye
- Helen Wills Neuroscience Institute, University of California, Berkeley, California; and Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Jason N MacLean
- Department of Neurobiology, University of Chicago, Chicago, Illinois
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172
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Mankin R, Lumi N. Statistics of a leaky integrate-and-fire model of neurons driven by dichotomous noise. Phys Rev E 2016; 93:052143. [PMID: 27300865 DOI: 10.1103/physreve.93.052143] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Indexed: 06/06/2023]
Abstract
The behavior of a stochastic leaky integrate-and-fire model of neurons is considered. The effect of temporally correlated random neuronal input is modeled as a colored two-level (dichotomous) Markovian noise. Relying on the Riemann method, exact expressions for the output interspike interval density and for the serial correlation coefficient are derived, and their dependence on noise parameters (such as correlation time and amplitude) is analyzed. Particularly, noise-induced sign reversal and a resonancelike amplification of the kurtosis of the interspike interval distribution are established. The features of spike statistics, analytically revealed in our study, are compared with recently obtained results for a perfect integrate-and-fire neuron model.
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Affiliation(s)
- Romi Mankin
- School of Natural Sciences and Health, Tallinn University, 29 Narva Road, 10120 Tallinn, Estonia
| | - Neeme Lumi
- School of Natural Sciences and Health, Tallinn University, 29 Narva Road, 10120 Tallinn, Estonia
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173
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Knight JC, Tully PJ, Kaplan BA, Lansner A, Furber SB. Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware. Front Neuroanat 2016; 10:37. [PMID: 27092061 PMCID: PMC4823276 DOI: 10.3389/fnana.2016.00037] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 03/18/2016] [Indexed: 11/17/2022] Open
Abstract
SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.
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Affiliation(s)
- James C Knight
- Advanced Processor Technologies Group, School of Computer Science, University of Manchester Manchester, UK
| | - Philip J Tully
- Department of Computational Biology, Royal Institute of TechnologyStockholm, Sweden; Stockholm Brain Institute, Karolinska InstituteStockholm, Sweden; Institute for Adaptive and Neural Computation, School of Informatics, University of EdinburghEdinburgh, UK
| | - Bernhard A Kaplan
- Department of Visualization and Data Analysis, Zuse Institute Berlin Berlin, Germany
| | - Anders Lansner
- Department of Computational Biology, Royal Institute of TechnologyStockholm, Sweden; Stockholm Brain Institute, Karolinska InstituteStockholm, Sweden; Department of Numerical analysis and Computer Science, Stockholm UniversityStockholm, Sweden
| | - Steve B Furber
- Advanced Processor Technologies Group, School of Computer Science, University of Manchester Manchester, UK
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174
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Yada Y, Kanzaki R, Takahashi H. State-Dependent Propagation of Neuronal Sub-Population in Spontaneous Synchronized Bursts. Front Syst Neurosci 2016; 10:28. [PMID: 27065820 PMCID: PMC4815764 DOI: 10.3389/fnsys.2016.00028] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 03/14/2016] [Indexed: 01/05/2023] Open
Abstract
Repeating stable spatiotemporal patterns emerge in synchronized spontaneous activity in neuronal networks. The repertoire of such patterns can serve as memory, or a reservoir of information, in a neuronal network; moreover, the variety of patterns may represent the network memory capacity. However, a neuronal substrate for producing a repertoire of patterns in synchronization remains elusive. We herein hypothesize that state-dependent propagation of a neuronal sub-population is the key mechanism. By combining high-resolution measurement with a 4096-channel complementary metal-oxide semiconductor (CMOS) microelectrode array (MEA) and dimensionality reduction with non-negative matrix factorization (NMF), we investigated synchronized bursts of dissociated rat cortical neurons at approximately 3 weeks in vitro. We found that bursts had a repertoire of repeating spatiotemporal patterns, and different patterns shared a partially similar sequence of sub-population, supporting the idea of sequential structure of neuronal sub-populations during synchronized activity. We additionally found that similar spatiotemporal patterns tended to appear successively and periodically, suggesting a state-dependent fluctuation of propagation, which has been overlooked in existing literature. Thus, such a state-dependent property within the sequential sub-population structure is a plausible neural substrate for performing a repertoire of stable patterns during synchronized activity.
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Affiliation(s)
- Yuichiro Yada
- Research Center for Advanced Science and Technology, The University of TokyoTokyo, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of TokyoTokyo, Japan; Japan Society for the Promotion of ScienceTokyo, Japan
| | - Ryohei Kanzaki
- Research Center for Advanced Science and Technology, The University of TokyoTokyo, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of TokyoTokyo, Japan
| | - Hirokazu Takahashi
- Research Center for Advanced Science and Technology, The University of TokyoTokyo, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of TokyoTokyo, Japan
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175
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Haider B, Schulz DPA, Häusser M, Carandini M. Millisecond Coupling of Local Field Potentials to Synaptic Currents in the Awake Visual Cortex. Neuron 2016; 90:35-42. [PMID: 27021173 PMCID: PMC4826437 DOI: 10.1016/j.neuron.2016.02.034] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Revised: 10/23/2015] [Accepted: 02/16/2016] [Indexed: 12/31/2022]
Abstract
The cortical local field potential (LFP) is a common measure of population activity, but its relationship to synaptic activity in individual neurons is not fully established. This relationship has been typically studied during anesthesia and is obscured by shared slow fluctuations. Here, we used patch-clamp recordings in visual cortex of anesthetized and awake mice to measure intracellular activity; we then applied a simple method to reveal its coupling to the simultaneously recorded LFP. LFP predicted membrane potential as accurately as synaptic currents, indicating a major role for synaptic currents in the relationship between cortical LFP and intracellular activity. During anesthesia, cortical LFP predicted excitation far better than inhibition; during wakefulness, it predicted them equally well, and visual stimulation further enhanced predictions of inhibition. These findings reveal a central role for synaptic currents, and especially inhibition, in the relationship between the subthreshold activity of individual neurons and the cortical LFP during wakefulness.
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Affiliation(s)
- Bilal Haider
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK.
| | - David P A Schulz
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Michael Häusser
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, UK
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK
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176
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Ribeiro TL, Ribeiro S, Copelli M. Repertoires of Spike Avalanches Are Modulated by Behavior and Novelty. Front Neural Circuits 2016; 10:16. [PMID: 27047341 PMCID: PMC4802163 DOI: 10.3389/fncir.2016.00016] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Accepted: 03/07/2016] [Indexed: 11/13/2022] Open
Abstract
Neuronal avalanches measured as consecutive bouts of thresholded field potentials represent a statistical signature that the brain operates near a critical point. In theory, criticality optimizes stimulus sensitivity, information transmission, computational capability and mnemonic repertoires size. Field potential avalanches recorded via multielectrode arrays from cortical slice cultures are repeatable spatiotemporal activity patterns. It remains unclear whether avalanches of action potentials observed in forebrain regions of freely-behaving rats also form recursive repertoires, and whether these have any behavioral relevance. Here, we show that spike avalanches, recorded from hippocampus (HP) and sensory neocortex of freely-behaving rats, constitute distinct families of recursive spatiotemporal patterns. A significant number of those patterns were specific to a behavioral state. Although avalanches produced during sleep were mostly similar to others that occurred during waking, the repertoire of patterns recruited during sleep differed significantly from that of waking. More importantly, exposure to novel objects increased the rate at which new patterns arose, also leading to changes in post-exposure repertoires, which were significantly different from those before the exposure. A significant number of families occurred exclusively during periods of whisker contact with objects, but few were associated with specific objects. Altogether, the results provide original evidence linking behavior and criticality at the spike level: spike avalanches form repertoires that emerge in waking, recur during sleep, are diversified by novelty and contribute to object representation.
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Affiliation(s)
- Tiago L Ribeiro
- Section on Critical Brain Dynamics, National Institute of Mental Health (NIMH), National Institutes of Health (NIH)Bethesda, MD, USA; Physics Department, Federal University of Pernambuco (UFPE)Recife, PE, Brazil
| | - Sidarta Ribeiro
- Brain Institute, Federal University of Rio Grande do Norte (UFRN) Natal, RN, Brazil
| | - Mauro Copelli
- Physics Department, Federal University of Pernambuco (UFPE) Recife, PE, Brazil
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177
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Rajan K, Harvey CD, Tank DW. Recurrent Network Models of Sequence Generation and Memory. Neuron 2016; 90:128-42. [PMID: 26971945 DOI: 10.1016/j.neuron.2016.02.009] [Citation(s) in RCA: 179] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2015] [Revised: 12/03/2015] [Accepted: 02/02/2016] [Indexed: 12/29/2022]
Abstract
Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here we demonstrate that, starting from random connectivity and modifying a small fraction of connections, a largely disordered recurrent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network Training (PINning), to model and match cellular resolution imaging data from the posterior parietal cortex during a virtual memory-guided two-alternative forced-choice task. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures.
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Affiliation(s)
- Kanaka Rajan
- Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
| | | | - David W Tank
- Department of Molecular Biology and Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.
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178
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Rueckert E, Kappel D, Tanneberg D, Pecevski D, Peters J. Recurrent Spiking Networks Solve Planning Tasks. Sci Rep 2016; 6:21142. [PMID: 26888174 PMCID: PMC4758071 DOI: 10.1038/srep21142] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 12/18/2015] [Indexed: 11/12/2022] Open
Abstract
A recurrent spiking neural network is proposed that implements planning as probabilistic inference for finite and infinite horizon tasks. The architecture splits this problem into two parts: The stochastic transient firing of the network embodies the dynamics of the planning task. With appropriate injected input this dynamics is shaped to generate high-reward state trajectories. A general class of reward-modulated plasticity rules for these afferent synapses is presented. The updates optimize the likelihood of getting a reward through a variant of an Expectation Maximization algorithm and learning is guaranteed to convergence to a local maximum. We find that the network dynamics are qualitatively similar to transient firing patterns during planning and foraging in the hippocampus of awake behaving rats. The model extends classical attractor models and provides a testable prediction on identifying modulating contextual information. In a real robot arm reaching and obstacle avoidance task the ability to represent multiple task solutions is investigated. The neural planning method with its local update rules provides the basis for future neuromorphic hardware implementations with promising potentials like large data processing abilities and early initiation of strategies to avoid dangerous situations in robot co-worker scenarios.
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Affiliation(s)
- Elmar Rueckert
- Intelligent Autonomous Systems Lab, Technische Universität Darmstadt, 64289, Germany
| | - David Kappel
- Institute for Theoretical Computer Science, Technische Universität Graz, 8020, Austria
| | - Daniel Tanneberg
- Intelligent Autonomous Systems Lab, Technische Universität Darmstadt, 64289, Germany
| | - Dejan Pecevski
- Institute for Theoretical Computer Science, Technische Universität Graz, 8020, Austria
| | - Jan Peters
- Intelligent Autonomous Systems Lab, Technische Universität Darmstadt, 64289, Germany.,Robot Learning Group, Max-Planck Institute for Intelligent Systems, Tuebingen, 72076, Germany
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179
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Luongo FJ, Zimmerman CA, Horn ME, Sohal VS. Correlations between prefrontal neurons form a small-world network that optimizes the generation of multineuron sequences of activity. J Neurophysiol 2016; 115:2359-75. [PMID: 26888108 DOI: 10.1152/jn.01043.2015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 02/15/2016] [Indexed: 12/11/2022] Open
Abstract
Sequential patterns of prefrontal activity are believed to mediate important behaviors, e.g., working memory, but it remains unclear exactly how they are generated. In accordance with previous studies of cortical circuits, we found that prefrontal microcircuits in young adult mice spontaneously generate many more stereotyped sequences of activity than expected by chance. However, the key question of whether these sequences depend on a specific functional organization within the cortical microcircuit, or emerge simply as a by-product of random interactions between neurons, remains unanswered. We observed that correlations between prefrontal neurons do follow a specific functional organization-they have a small-world topology. However, until now it has not been possible to directly link small-world topologies to specific circuit functions, e.g., sequence generation. Therefore, we developed a novel analysis to address this issue. Specifically, we constructed surrogate data sets that have identical levels of network activity at every point in time but nevertheless represent various network topologies. We call this method shuffling activity to rearrange correlations (SHARC). We found that only surrogate data sets based on the actual small-world functional organization of prefrontal microcircuits were able to reproduce the levels of sequences observed in actual data. As expected, small-world data sets contained many more sequences than surrogate data sets with randomly arranged correlations. Surprisingly, small-world data sets also outperformed data sets in which correlations were maximally clustered. Thus the small-world functional organization of cortical microcircuits, which effectively balances the random and maximally clustered regimes, is optimal for producing stereotyped sequential patterns of activity.
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Affiliation(s)
- Francisco J Luongo
- Department of Psychiatry, University of California, San Francisco, California; Center for Integrative Neuroscience, University of California, San Francisco, California; Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, California; and Neuroscience Graduate Program, University of California, San Francisco, California
| | - Chris A Zimmerman
- Neuroscience Graduate Program, University of California, San Francisco, California
| | - Meryl E Horn
- Neuroscience Graduate Program, University of California, San Francisco, California
| | - Vikaas S Sohal
- Department of Psychiatry, University of California, San Francisco, California; Center for Integrative Neuroscience, University of California, San Francisco, California; Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, California; and
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180
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Villette V, Malvache A, Tressard T, Dupuy N, Cossart R. Internally Recurring Hippocampal Sequences as a Population Template of Spatiotemporal Information. Neuron 2016; 88:357-66. [PMID: 26494280 PMCID: PMC4622933 DOI: 10.1016/j.neuron.2015.09.052] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Revised: 07/23/2015] [Accepted: 09/16/2015] [Indexed: 12/22/2022]
Abstract
The hippocampus is essential for spatiotemporal cognition. Sequences of neuronal activation provide a substrate for this fundamental function. At the behavioral timescale, these sequences have been shown to occur either in the presence of successive external landmarks or through internal mechanisms within an episodic memory task. In both cases, activity is externally constrained by the organization of the task and by the size of the environment explored. Therefore, it remains unknown whether hippocampal activity can self-organize into a default mode in the absence of any external memory demand or spatiotemporal boundary. Here we show that, in the presence of self-motion cues, a population code integrating distance naturally emerges in the hippocampus in the form of recurring sequences. These internal dynamics clamp spontaneous travel since run distance distributes into integer multiples of the span of these sequences. These sequences may thus guide navigation when external landmarks are reduced. Without external cues, hippocampal dynamics spontaneously display recurring sequences Recurring sequences span across a fixed traveled distance Sequences are an internal cognitive template that shapes mouse behavior Sequences display an internally hardwired functional structure
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Affiliation(s)
- Vincent Villette
- Institut National de la Santé et de la Recherche Médicale Unité 901, 13009 Marseille, France; Aix-Marseille Université, Unité Mixte de Recherche S901, 13009 Marseille, France; Institut de Neurobiologie de la Méditerranée, 13009 Marseille, France
| | - Arnaud Malvache
- Institut National de la Santé et de la Recherche Médicale Unité 901, 13009 Marseille, France; Aix-Marseille Université, Unité Mixte de Recherche S901, 13009 Marseille, France; Institut de Neurobiologie de la Méditerranée, 13009 Marseille, France.
| | - Thomas Tressard
- Institut National de la Santé et de la Recherche Médicale Unité 901, 13009 Marseille, France; Aix-Marseille Université, Unité Mixte de Recherche S901, 13009 Marseille, France; Institut de Neurobiologie de la Méditerranée, 13009 Marseille, France
| | - Nathalie Dupuy
- Institut National de la Santé et de la Recherche Médicale Unité 901, 13009 Marseille, France; Aix-Marseille Université, Unité Mixte de Recherche S901, 13009 Marseille, France; Institut de Neurobiologie de la Méditerranée, 13009 Marseille, France
| | - Rosa Cossart
- Institut National de la Santé et de la Recherche Médicale Unité 901, 13009 Marseille, France; Aix-Marseille Université, Unité Mixte de Recherche S901, 13009 Marseille, France; Institut de Neurobiologie de la Méditerranée, 13009 Marseille, France.
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181
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Krone L, Frase L, Piosczyk H, Selhausen P, Zittel S, Jahn F, Kuhn M, Feige B, Mainberger F, Klöppel S, Riemann D, Spiegelhalder K, Baglioni C, Sterr A, Nissen C. Top-down control of arousal and sleep: Fundamentals and clinical implications. Sleep Med Rev 2016; 31:17-24. [PMID: 26883160 DOI: 10.1016/j.smrv.2015.12.005] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Revised: 12/14/2015] [Accepted: 12/15/2015] [Indexed: 01/07/2023]
Abstract
Mammalian sleep emerges from attenuated activity in the ascending reticular arousal system (ARAS), the main arousal network of the brain. This system originates in the brainstem and activates the thalamus and cortex during wakefulness via a well-characterized 'bottom-up' pathway. Recent studies propose that a less investigated cortico-thalamic 'top-down' pathway also regulates sleep. The present work integrates the current evidence on sleep regulation with a focus on the 'top-down' pathway and explores the potential to translate this information into clinically relevant interventions. Specifically, we elaborate the concept that arousal and sleep continuity in humans can be modulated by non-invasive brain stimulation (NIBS) techniques that increase or decrease cortical excitability. Based on preclinical studies, the modulatory effects of the stimulation are thought to extend to subcortical arousal networks. Further exploration of the 'top-down' regulation of sleep and its modulation through non-invasive brain stimulation techniques may contribute to the development of novel treatments for clinical conditions of disrupted arousal and sleep, which are among the major health problems worldwide.
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Affiliation(s)
- Lukas Krone
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Germany; Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | - Lukas Frase
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Germany; Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | - Hannah Piosczyk
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Germany; Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | - Peter Selhausen
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Germany; Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | - Sulamith Zittel
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Germany; Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | - Friederike Jahn
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Germany; Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | - Marion Kuhn
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Germany; Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | - Bernd Feige
- Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | - Florian Mainberger
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Germany
| | - Stefan Klöppel
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Germany
| | - Dieter Riemann
- Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | - Kai Spiegelhalder
- Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | - Chiara Baglioni
- Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany
| | | | - Christoph Nissen
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Germany; Department of Clinical Psychology and Psychophysiology/ Sleep Medicine, University of Freiburg Medical Center, Germany.
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182
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Neske GT. The Slow Oscillation in Cortical and Thalamic Networks: Mechanisms and Functions. Front Neural Circuits 2016; 9:88. [PMID: 26834569 PMCID: PMC4712264 DOI: 10.3389/fncir.2015.00088] [Citation(s) in RCA: 136] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2015] [Accepted: 12/21/2015] [Indexed: 12/03/2022] Open
Abstract
During even the most quiescent behavioral periods, the cortex and thalamus express rich spontaneous activity in the form of slow (<1 Hz), synchronous network state transitions. Throughout this so-called slow oscillation, cortical and thalamic neurons fluctuate between periods of intense synaptic activity (Up states) and almost complete silence (Down states). The two decades since the original characterization of the slow oscillation in the cortex and thalamus have seen considerable advances in deciphering the cellular and network mechanisms associated with this pervasive phenomenon. There are, nevertheless, many questions regarding the slow oscillation that await more thorough illumination, particularly the mechanisms by which Up states initiate and terminate, the functional role of the rhythmic activity cycles in unconscious or minimally conscious states, and the precise relation between Up states and the activated states associated with waking behavior. Given the substantial advances in multineuronal recording and imaging methods in both in vivo and in vitro preparations, the time is ripe to take stock of our current understanding of the slow oscillation and pave the way for future investigations of its mechanisms and functions. My aim in this Review is to provide a comprehensive account of the mechanisms and functions of the slow oscillation, and to suggest avenues for further exploration.
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Affiliation(s)
- Garrett T Neske
- Department of Neuroscience, Division of Biology and Medicine, Brown UniversityProvidence, RI, USA; Department of Neurobiology, Yale UniversityNew Haven, CT, USA
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183
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Reyes-Puerta V, Yang JW, Siwek ME, Kilb W, Sun JJ, Luhmann HJ. Propagation of spontaneous slow-wave activity across columns and layers of the adult rat barrel cortex in vivo. Brain Struct Funct 2016; 221:4429-4449. [PMID: 26754838 DOI: 10.1007/s00429-015-1173-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Accepted: 12/16/2015] [Indexed: 12/19/2022]
Abstract
During slow-wave sleep, neocortical networks exhibit self-organized activity switching between periods of concurrent spiking (up-states) and periods of network silence (down-states), a phenomenon also occurring under the effects of different anesthetics and in in vitro brain slice preparations. Although this type of ongoing activity has been implicated into important functions such as memory consolidation and learning, the manner in which it propagates across different cortical modules (i.e., columns and layers) has not been fully characterized. In the present study, we investigated this issue by measuring spontaneous activity at large scale in the adult rat barrel cortex under urethane anesthesia by means of voltage-sensitive dye imaging and 128-channel probe recordings. Up to 74 neurons located in all layers of up to four functionally identified barrel-related columns were recorded simultaneously. The spontaneous activity propagated isotropically across the cortical surface with a median speed of ~35 µm/ms. A concomitant radial spread of activation was present from deep to superficial cortical layers. Thus, spontaneous activity occurred rather globally in the barrel cortex, with ≥50 % of the up-states presenting spikes in ≥3 columns and layers. Temporally precise spike sequences, which occurred repeatedly (although sporadically) within the up-states, were typically led by putative excitatory neurons in the infragranular cortical layers. In summary, our data provide for the first time an overall view of the spontaneous slow-wave activity within the barrel cortex circuit, characterizing its propagation across columns and layers at high spatio-temporal resolution.
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Affiliation(s)
- Vicente Reyes-Puerta
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University, Duesbergweg 6, 55128, Mainz, Germany.
| | - Jenq-Wei Yang
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University, Duesbergweg 6, 55128, Mainz, Germany
| | - Magdalena E Siwek
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University, Duesbergweg 6, 55128, Mainz, Germany
| | - Werner Kilb
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University, Duesbergweg 6, 55128, Mainz, Germany
| | - Jyh-Jang Sun
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University, Duesbergweg 6, 55128, Mainz, Germany.
- Neuro-Electronics Research Flanders, Kapeldreef 75, 3001, Louvain, Belgium.
| | - Heiko J Luhmann
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University, Duesbergweg 6, 55128, Mainz, Germany
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184
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Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network. PLoS Comput Biol 2015; 11:e1004640. [PMID: 26714277 PMCID: PMC4694925 DOI: 10.1371/journal.pcbi.1004640] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 11/02/2015] [Indexed: 11/26/2022] Open
Abstract
Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms. Neural recordings seem very noisy. If the exact same stimulus is shown to an animal multiple times, the neural response will vary substantially. In fact, the activity of a single neuron shows many features of a random process. Furthermore, the spontaneous activity occurring in the absence of any sensory stimulus, which is usually considered a kind of background noise, often has a magnitude comparable to the activity evoked by stimulus presentation and interacts with sensory inputs in interesting ways. Here we show that the key features of neural variability and spontaneous activity can all be accounted for by a simple and completely deterministic neural network learning a predictive model of its sensory inputs. The network’s deterministic dynamics give rise to structured but variable responses matching key experimental findings obtained in different mammalian species with different recording techniques. Our results suggest that the notorious variability of neural recordings and the complex features of spontaneous brain activity could reflect the dynamics of a largely deterministic but highly adaptive network learning a predictive model of its sensory environment.
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185
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Brette R. Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain. Front Syst Neurosci 2015; 9:151. [PMID: 26617496 PMCID: PMC4639701 DOI: 10.3389/fnsys.2015.00151] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Accepted: 10/23/2015] [Indexed: 11/16/2022] Open
Abstract
Does the brain use a firing rate code or a spike timing code? Considering this controversial question from an epistemological perspective, I argue that progress has been hampered by its problematic phrasing. It takes the perspective of an external observer looking at whether those two observables vary with stimuli, and thereby misses the relevant question: which one has a causal role in neural activity? When rephrased in a more meaningful way, the rate-based view appears as an ad hoc methodological postulate, one that is practical but with virtually no empirical or theoretical support.
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Affiliation(s)
- Romain Brette
- UMR_S 968, Institut de la Vision, Sorbonne Universités, UPMC University, Paris 06 Paris, France ; INSERM, U968 Paris, France ; CNRS, UMR_7210 Paris, France
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186
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Wanger T, Wetzel W, Scheich H, Ohl FW, Goldschmidt J. Spatial patterns of neuronal activity in rat cerebral cortex during non-rapid eye movement sleep. Brain Struct Funct 2015; 220:3469-84. [PMID: 25113606 PMCID: PMC4575691 DOI: 10.1007/s00429-014-0867-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Accepted: 07/29/2014] [Indexed: 11/06/2022]
Abstract
It is commonly assumed that cortical activity in non-rapid eye movement sleep (NREMS) is spatially homogeneous on the mesoscopic scale. This is partly due to the limited observational scope of common metabolic or imaging methods in sleep. We used the recently developed technique of thallium-autometallography (TlAMG) to visualize mesoscopic patterns of activity in the sleeping cortex with single-cell resolution. We intravenously injected rats with the lipophilic chelate complex thallium diethyldithiocarbamate (TlDDC) during spontaneously occurring periods of NREMS and mapped the patterns of neuronal uptake of the potassium (K+) probe thallium (Tl+). Using this method, we show that cortical activity patterns are not spatially homogeneous during discrete 5-min episodes of NREMS in unrestrained rats-rather, they are complex and spatially diverse. Along with a relative predominance of infragranular layer activation, we find pronounced differences in metabolic activity of neighboring neuronal assemblies, an observation which lends support to the emerging paradigm that sleep is a distributed process with regulation on the local scale.
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Affiliation(s)
- Tim Wanger
- Department Systems Physiology of Learning, Leibniz Institute for Neurobiology (LIN), Brenneckestraße 6, 39118, Magdeburg, Germany.
| | - Wolfram Wetzel
- Department Systems Physiology of Learning, Leibniz Institute for Neurobiology (LIN), Brenneckestraße 6, 39118, Magdeburg, Germany
| | - Henning Scheich
- Emeritus Group Lifelong Learning, Leibniz Institute for Neurobiology (LIN), Brenneckestraße 6, 39118, Magdeburg, Germany
| | - Frank W Ohl
- Department Systems Physiology of Learning, Leibniz Institute for Neurobiology (LIN), Brenneckestraße 6, 39118, Magdeburg, Germany
- Otto-von-Guericke University, 39106, Magdeburg, Germany
- Center for Behavioral Brain Science (CBBS), Magdeburg, Germany
| | - Jürgen Goldschmidt
- Department Systems Physiology of Learning, Leibniz Institute for Neurobiology (LIN), Brenneckestraße 6, 39118, Magdeburg, Germany
- Otto-von-Guericke University, 39106, Magdeburg, Germany
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187
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Luczak A, McNaughton BL, Harris KD. Packet-based communication in the cortex. Nat Rev Neurosci 2015; 16:745-55. [PMID: 26507295 DOI: 10.1038/nrn4026] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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188
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Busche MA, Kekuš M, Adelsberger H, Noda T, Förstl H, Nelken I, Konnerth A. Rescue of long-range circuit dysfunction in Alzheimer's disease models. Nat Neurosci 2015; 18:1623-30. [DOI: 10.1038/nn.4137] [Citation(s) in RCA: 144] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Accepted: 09/09/2015] [Indexed: 02/05/2023]
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189
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Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez CA, Ailamaki A, Alonso-Nanclares L, Antille N, Arsever S, Kahou GAA, Berger TK, Bilgili A, Buncic N, Chalimourda A, Chindemi G, Courcol JD, Delalondre F, Delattre V, Druckmann S, Dumusc R, Dynes J, Eilemann S, Gal E, Gevaert ME, Ghobril JP, Gidon A, Graham JW, Gupta A, Haenel V, Hay E, Heinis T, Hernando JB, Hines M, Kanari L, Keller D, Kenyon J, Khazen G, Kim Y, King JG, Kisvarday Z, Kumbhar P, Lasserre S, Le Bé JV, Magalhães BRC, Merchán-Pérez A, Meystre J, Morrice BR, Muller J, Muñoz-Céspedes A, Muralidhar S, Muthurasa K, Nachbaur D, Newton TH, Nolte M, Ovcharenko A, Palacios J, Pastor L, Perin R, Ranjan R, Riachi I, Rodríguez JR, Riquelme JL, Rössert C, Sfyrakis K, Shi Y, Shillcock JC, Silberberg G, Silva R, Tauheed F, Telefont M, Toledo-Rodriguez M, Tränkler T, Van Geit W, Díaz JV, Walker R, Wang Y, Zaninetta SM, DeFelipe J, Hill SL, Segev I, Schürmann F. Reconstruction and Simulation of Neocortical Microcircuitry. Cell 2015; 163:456-92. [PMID: 26451489 DOI: 10.1016/j.cell.2015.09.029] [Citation(s) in RCA: 747] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 05/04/2015] [Accepted: 09/11/2015] [Indexed: 02/03/2023]
Affiliation(s)
- Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland.
| | - Eilif Muller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Michael W Reimann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Marwan Abdellah
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Carlos Aguado Sanchez
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Anastasia Ailamaki
- Data-Intensive Applications and Systems Lab, EPFL, 1015 Lausanne, Switzerland
| | - Lidia Alonso-Nanclares
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Nicolas Antille
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Selim Arsever
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Guy Antoine Atenekeng Kahou
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Thomas K Berger
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Ahmet Bilgili
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Nenad Buncic
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Athanassia Chalimourda
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Giuseppe Chindemi
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Fabien Delalondre
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Vincent Delattre
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Shaul Druckmann
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel; Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Raphael Dumusc
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - James Dynes
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Stefan Eilemann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Eyal Gal
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Michael Emiel Gevaert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jean-Pierre Ghobril
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Albert Gidon
- Department of Neurobiology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Joe W Graham
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Anirudh Gupta
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Valentin Haenel
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Etay Hay
- Department of Neurobiology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel; The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Thomas Heinis
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Data-Intensive Applications and Systems Lab, EPFL, 1015 Lausanne, Switzerland; Imperial College London, London SW7 2AZ, UK
| | - Juan B Hernando
- CeSViMa, Centro de Supercomputación y Visualización de Madrid, Universidad Politécnica de Madrid, 28223 Madrid, Spain
| | - Michael Hines
- Department of Neurobiology, Yale University, New Haven, CT 06510 USA
| | - Lida Kanari
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Daniel Keller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - John Kenyon
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Georges Khazen
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Yihwa Kim
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - James G King
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Zoltan Kisvarday
- MTA-Debreceni Egyetem, Neuroscience Research Group, 4032 Debrecen, Hungary
| | - Pramod Kumbhar
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Sébastien Lasserre
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratoire d'informatique et de visualisation, EPFL, 1015 Lausanne, Switzerland
| | - Jean-Vincent Le Bé
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Bruno R C Magalhães
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Angel Merchán-Pérez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Julie Meystre
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Benjamin Roy Morrice
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jeffrey Muller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Alberto Muñoz-Céspedes
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Shruti Muralidhar
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Keerthan Muthurasa
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Daniel Nachbaur
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Taylor H Newton
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Max Nolte
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Aleksandr Ovcharenko
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Juan Palacios
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Luis Pastor
- Modeling and Virtual Reality Group, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
| | - Rodrigo Perin
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Rajnish Ranjan
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Imad Riachi
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - José-Rodrigo Rodríguez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Juan Luis Riquelme
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Christian Rössert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Konstantinos Sfyrakis
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Ying Shi
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Julian C Shillcock
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Gilad Silberberg
- Department of Neuroscience, Karolinska Institutet, Stockholm 17177, Sweden
| | - Ricardo Silva
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Farhan Tauheed
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Data-Intensive Applications and Systems Lab, EPFL, 1015 Lausanne, Switzerland
| | - Martin Telefont
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | | | - Thomas Tränkler
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jafet Villafranca Díaz
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Richard Walker
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Yun Wang
- Key Laboratory of Visual Science and National Ministry of Health, School of Optometry and Opthalmology, Wenzhou Medical College, Wenzhou 325003, China; Caritas St. Elizabeth's Medical Center, Genesys Research Institute, Tufts University, Boston, MA 02111, USA
| | - Stefano M Zaninetta
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Sean L Hill
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Idan Segev
- Department of Neurobiology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel; The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Felix Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
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190
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Buzsáki G. Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning. Hippocampus 2015; 25:1073-188. [PMID: 26135716 PMCID: PMC4648295 DOI: 10.1002/hipo.22488] [Citation(s) in RCA: 916] [Impact Index Per Article: 101.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 06/30/2015] [Indexed: 12/23/2022]
Abstract
Sharp wave ripples (SPW-Rs) represent the most synchronous population pattern in the mammalian brain. Their excitatory output affects a wide area of the cortex and several subcortical nuclei. SPW-Rs occur during "off-line" states of the brain, associated with consummatory behaviors and non-REM sleep, and are influenced by numerous neurotransmitters and neuromodulators. They arise from the excitatory recurrent system of the CA3 region and the SPW-induced excitation brings about a fast network oscillation (ripple) in CA1. The spike content of SPW-Rs is temporally and spatially coordinated by a consortium of interneurons to replay fragments of waking neuronal sequences in a compressed format. SPW-Rs assist in transferring this compressed hippocampal representation to distributed circuits to support memory consolidation; selective disruption of SPW-Rs interferes with memory. Recently acquired and pre-existing information are combined during SPW-R replay to influence decisions, plan actions and, potentially, allow for creative thoughts. In addition to the widely studied contribution to memory, SPW-Rs may also affect endocrine function via activation of hypothalamic circuits. Alteration of the physiological mechanisms supporting SPW-Rs leads to their pathological conversion, "p-ripples," which are a marker of epileptogenic tissue and can be observed in rodent models of schizophrenia and Alzheimer's Disease. Mechanisms for SPW-R genesis and function are discussed in this review.
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Affiliation(s)
- György Buzsáki
- The Neuroscience Institute, School of Medicine and Center for Neural Science, New York University, New York, New York
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191
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Ramanathan DS, Gulati T, Ganguly K. Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation. PLoS Biol 2015; 13:e1002263. [PMID: 26382320 PMCID: PMC4575076 DOI: 10.1371/journal.pbio.1002263] [Citation(s) in RCA: 112] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2015] [Accepted: 08/21/2015] [Indexed: 12/22/2022] Open
Abstract
Despite many prior studies demonstrating offline behavioral gains in motor skills after sleep, the underlying neural mechanisms remain poorly understood. To investigate the neurophysiological basis for offline gains, we performed single-unit recordings in motor cortex as rats learned a skilled upper-limb task. We found that sleep improved movement speed with preservation of accuracy. These offline improvements were linked to both replay of task-related ensembles during non-rapid eye movement (NREM) sleep and temporal shifts that more tightly bound motor cortical ensembles to movements; such offline gains and temporal shifts were not evident with sleep restriction. Interestingly, replay was linked to the coincidence of slow-wave events and bursts of spindle activity. Neurons that experienced the most consistent replay also underwent the most significant temporal shift and binding to the motor task. Significantly, replay and the associated performance gains after sleep only occurred when animals first learned the skill; continued practice during later stages of learning (i.e., after motor kinematics had stabilized) did not show evidence of replay. Our results highlight how replay of synchronous neural activity during sleep mediates large-scale neural plasticity and stabilizes kinematics during early motor learning. During non-REM sleep in rats, consolidation and offline improvements of a recently learned motor skill are linked to synchronous reactivation of task-related neural ensembles. Sleep has been shown to help in consolidating learned motor tasks. In other words, sleep can induce “offline” gains in a new motor skill even in the absence of further training. However, how sleep induces this change has not been clearly identified. One hypothesis is that consolidation of memories during sleep occurs by “reactivation” of neurons engaged during learning. In this study, we tested this hypothesis by recording populations of neurons in the motor cortex of rats while they learned a new motor skill and during sleep both before and after the training session. We found that subsets of task-relevant neurons formed highly synchronized ensembles during learning. Interestingly, these same neural ensembles were reactivated during subsequent sleep blocks, and the degree of reactivation was correlated with several metrics of motor memory consolidation. Specifically, after sleep, the speed at which animals performed the task while maintaining accuracy was increased, and the activity of the neuronal assembles were more tightly bound to motor action. Further analyses showed that reactivation events occurred episodically and in conjunction with spindle-oscillations—common bursts of brain activity seen during sleep. This observation is consistent with previous findings in humans that spindle-oscillations correlate with consolidation of learned tasks. Our study thus provides insight into the neuronal network mechanism supporting consolidation of motor memory during sleep and may lead to novel interventions that can enhance skill learning in both healthy and injured nervous systems.
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Affiliation(s)
- Dhakshin S. Ramanathan
- Neurology and Rehabilitation Service, San Francisco VA Medical Center, San Francisco, California, United States of America
- Psychiatry Service, San Francisco VA Medical Center, San Francisco, California, United States of America
- Department of Psychiatry, University of California, San Francisco, San Francisco, California, United States of America
| | - Tanuj Gulati
- Neurology and Rehabilitation Service, San Francisco VA Medical Center, San Francisco, California, United States of America
- Department of Neurology, University of California, San Francisco, San Francisco, California, United States of America
| | - Karunesh Ganguly
- Neurology and Rehabilitation Service, San Francisco VA Medical Center, San Francisco, California, United States of America
- Department of Neurology, University of California, San Francisco, San Francisco, California, United States of America
- * E-mail:
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192
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Abstract
Single-trial analyses of ensemble activity in alert animals demonstrate that cortical circuits dynamics evolve through temporal sequences of metastable states. Metastability has been studied for its potential role in sensory coding, memory, and decision-making. Yet, very little is known about the network mechanisms responsible for its genesis. It is often assumed that the onset of state sequences is triggered by an external stimulus. Here we show that state sequences can be observed also in the absence of overt sensory stimulation. Analysis of multielectrode recordings from the gustatory cortex of alert rats revealed ongoing sequences of states, where single neurons spontaneously attain several firing rates across different states. This single-neuron multistability represents a challenge to existing spiking network models, where typically each neuron is at most bistable. We present a recurrent spiking network model that accounts for both the spontaneous generation of state sequences and the multistability in single-neuron firing rates. Each state results from the activation of neural clusters with potentiated intracluster connections, with the firing rate in each cluster depending on the number of active clusters. Simulations show that the model's ensemble activity hops among the different states, reproducing the ongoing dynamics observed in the data. When probed with external stimuli, the model predicts the quenching of single-neuron multistability into bistability and the reduction of trial-by-trial variability. Both predictions were confirmed in the data. Together, these results provide a theoretical framework that captures both ongoing and evoked network dynamics in a single mechanistic model.
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193
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Abstract
Although the functional properties of individual neurons in primary visual cortex have been studied intensely, little is known about how neuronal groups could encode changing visual stimuli using temporal activity patterns. To explore this, we used in vivo two-photon calcium imaging to record the activity of neuronal populations in primary visual cortex of awake mice in the presence and absence of visual stimulation. Multidimensional analysis of the network activity allowed us to identify neuronal ensembles defined as groups of cells firing in synchrony. These synchronous groups of neurons were themselves activated in sequential temporal patterns, which repeated at much higher proportions than chance and were triggered by specific visual stimuli such as natural visual scenes. Interestingly, sequential patterns were also present in recordings of spontaneous activity without any sensory stimulation and were accompanied by precise firing sequences at the single-cell level. Moreover, intrinsic dynamics could be used to predict the occurrence of future neuronal ensembles. Our data demonstrate that visual stimuli recruit similar sequential patterns to the ones observed spontaneously, consistent with the hypothesis that already existing Hebbian cell assemblies firing in predefined temporal sequences could be the microcircuit substrate that encodes visual percepts changing in time.
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194
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Memory trace replay: the shaping of memory consolidation by neuromodulation. Trends Neurosci 2015; 38:560-70. [PMID: 26275935 PMCID: PMC4712256 DOI: 10.1016/j.tins.2015.07.004] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 07/02/2015] [Accepted: 07/14/2015] [Indexed: 01/24/2023]
Abstract
The consolidation of memories for places and events is thought to rely, at the network level, on the replay of spatially tuned neuronal firing patterns representing discrete places and spatial trajectories. This occurs in the hippocampal-entorhinal circuit during sharp wave ripple events (SWRs) that occur during sleep or rest. Here, we review theoretical models of lingering place cell excitability and behaviorally induced synaptic plasticity within cell assemblies to explain which sequences or places are replayed. We further provide new insights into how fluctuations in cholinergic tone during different behavioral states might shape the direction of replay and how dopaminergic release in response to novelty or reward can modulate which cell assemblies are replayed.
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195
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Vyazovskiy VV, Olcese U, Cirelli C, Tononi G. Prolonged wakefulness alters neuronal responsiveness to local electrical stimulation of the neocortex in awake rats.. J Sleep Res 2015; 22:239-50. [PMID: 23607417 DOI: 10.1111/jsr.12009] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Prolonged wakefulness or a lack of sleep lead to cognitive deficits, but little is known about the underlying cellular mechanisms. We recently found that sleep deprivation affects spontaneous neuronal activity in the neocortex of sleeping and awake rats. While it is well known that synaptic responses are modulated by ongoing cortical activity, it remains unclear whether prolonged waking affects responsiveness of cortical neurons to incoming stimuli. By applying local electrical microstimulation to the frontal area of the neocortex, we found that after a 4 h period of waking the initial neuronal response in the contralateral frontal cortex was stronger and more synchronous, and was followed by a more profound inhibition of neuronal spiking as compared with the control condition. These changes in evoked activity suggest increased neuronal excitability and indicate that, after staying awake, cortical neurons become transiently bistable. We propose that some of the detrimental effects of sleep deprivation may be a result of altered neuronal responsiveness to incoming intrinsic and extrinsic inputs.
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Affiliation(s)
- Vladyslav V Vyazovskiy
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, USA; Department of Biochemistry and Physiology, University of Surrey, Guildford, Surrey, UK
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196
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Local generation of multineuronal spike sequences in the hippocampal CA1 region. Proc Natl Acad Sci U S A 2015; 112:10521-6. [PMID: 26240336 DOI: 10.1073/pnas.1508785112] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Sequential activity of multineuronal spiking can be observed during theta and high-frequency ripple oscillations in the hippocampal CA1 region and is linked to experience, but the mechanisms underlying such sequences are unknown. We compared multineuronal spiking during theta oscillations, spontaneous ripples, and focal optically induced high-frequency oscillations ("synthetic" ripples) in freely moving mice. Firing rates and rate modulations of individual neurons, and multineuronal sequences of pyramidal cell and interneuron spiking, were correlated during theta oscillations, spontaneous ripples, and synthetic ripples. Interneuron spiking was crucial for sequence consistency. These results suggest that participation of single neurons and their sequential order in population events are not strictly determined by extrinsic inputs but also influenced by local-circuit properties, including synapses between local neurons and single-neuron biophysics.
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197
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Sornborger AT, Wang Z, Tao L. A mechanism for graded, dynamically routable current propagation in pulse-gated synfire chains and implications for information coding. J Comput Neurosci 2015; 39:181-95. [PMID: 26227067 DOI: 10.1007/s10827-015-0570-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Revised: 07/13/2015] [Accepted: 07/15/2015] [Indexed: 10/23/2022]
Abstract
Neural oscillations can enhance feature recognition (Azouz and Gray Proceedings of the National Academy of Sciences of the United States of America, 97, 8110-8115 2000), modulate interactions between neurons (Womelsdorf et al. Science, 316, 1609-01612 2007), and improve learning and memory (Markowska et al. The Journal of Neuroscience, 15, 2063-2073 1995). Numerical studies have shown that coherent spiking can give rise to windows in time during which information transfer can be enhanced in neuronal networks (Abeles Israel Journal of Medical Sciences, 18, 83-92 1982; Lisman and Idiart Science, 267, 1512-1515 1995, Salinas and Sejnowski Nature Reviews. Neuroscience, 2, 539-550 2001). Unanswered questions are: 1) What is the transfer mechanism? And 2) how well can a transfer be executed? Here, we present a pulse-based mechanism by which a graded current amplitude may be exactly propagated from one neuronal population to another. The mechanism relies on the downstream gating of mean synaptic current amplitude from one population of neurons to another via a pulse. Because transfer is pulse-based, information may be dynamically routed through a neural circuit with fixed connectivity. We demonstrate the transfer mechanism in a realistic network of spiking neurons and show that it is robust to noise in the form of pulse timing inaccuracies, random synaptic strengths and finite size effects. We also show that the mechanism is structurally robust in that it may be implemented using biologically realistic pulses. The transfer mechanism may be used as a building block for fast, complex information processing in neural circuits. We show that the mechanism naturally leads to a framework wherein neural information coding and processing can be considered as a product of linear maps under the active control of a pulse generator. Distinct control and processing components combine to form the basis for the binding, propagation, and processing of dynamically routed information within neural pathways. Using our framework, we construct example neural circuits to 1) maintain a short-term memory, 2) compute time-windowed Fourier transforms, and 3) perform spatial rotations. We postulate that such circuits, with automatic and stereotyped control and processing of information, are the neural correlates of Crick and Koch's zombie modes.
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Affiliation(s)
| | - Zhuo Wang
- Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing, China.
| | - Louis Tao
- Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, and Center for Quantitative Biology, Peking University, Beijing, China.
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198
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Abstract
For over a century, the neuron doctrine--which states that the neuron is the structural and functional unit of the nervous system--has provided a conceptual foundation for neuroscience. This viewpoint reflects its origins in a time when the use of single-neuron anatomical and physiological techniques was prominent. However, newer multineuronal recording methods have revealed that ensembles of neurons, rather than individual cells, can form physiological units and generate emergent functional properties and states. As a new paradigm for neuroscience, neural network models have the potential to incorporate knowledge acquired with single-neuron approaches to help us understand how emergent functional states generate behaviour, cognition and mental disease.
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Affiliation(s)
- Rafael Yuste
- Neurotechnology Center and Kavli Institute of Brain Sciences, Departments of Biological Sciences and Neuroscience, Columbia University, New York, New York 10027, USA
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199
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A distinct class of slow (~0.2-2 Hz) intrinsically bursting layer 5 pyramidal neurons determines UP/DOWN state dynamics in the neocortex. J Neurosci 2015; 35:5442-58. [PMID: 25855163 DOI: 10.1523/jneurosci.3603-14.2015] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
During sleep and anesthesia, neocortical neurons exhibit rhythmic UP/DOWN membrane potential states. Although UP states are maintained by synaptic activity, the mechanisms that underlie the initiation and robust rhythmicity of UP states are unknown. Using a physiologically validated model of UP/DOWN state generation in mouse neocortical slices whereby the cholinergic tone present in vivo is reinstated, we show that the regular initiation of UP states is driven by an electrophysiologically distinct subset of morphologically identified layer 5 neurons, which exhibit intrinsic rhythmic low-frequency burst firing at ~0.2-2 Hz. This low-frequency bursting is resistant to block of glutamatergic and GABAergic transmission but is absent when slices are maintained in a low Ca(2+) medium (an alternative, widely used model of cortical UP/DOWN states), thus explaining the lack of rhythmic UP states and abnormally prolonged DOWN states in this condition. We also characterized the activity of various other pyramidal and nonpyramidal neurons during UP/DOWN states and found that an electrophysiologically distinct subset of layer 5 regular spiking pyramidal neurons fires earlier during the onset of network oscillations compared with all other types of neurons recorded. This study, therefore, identifies an important role for cell-type-specific neuronal activity in driving neocortical UP states.
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200
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Yang Z, Mei L, Xia F, Luo Q, Fu L, Gong H. Dual-slit confocal light sheet microscopy for in vivo whole-brain imaging of zebrafish. BIOMEDICAL OPTICS EXPRESS 2015; 6:1797-811. [PMID: 26137381 PMCID: PMC4467708 DOI: 10.1364/boe.6.001797] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 04/09/2015] [Accepted: 04/12/2015] [Indexed: 05/08/2023]
Abstract
In vivo functional imaging at single-neuron resolution is an important approach to visualize biological processes in neuroscience. Light sheet microscopy (LSM) is a cutting edge in vivo imaging technique that provides micron-scale spatial resolution at high frame rate. Due to the scattering and absorption of tissue, however, conventional LSM is inadequate to resolve cells because of the attenuated signal to noise ratio (SNR). Using dual-beam illumination and confocal dual-slit detection, here a dual-slit confocal LSM is demonstrated to obtain the SNR enhanced images with frame rate twice as high as line confocal LSM method. Through theoretical calculations and experiments, the correlation between the slit's width and SNR was determined to optimize the image quality. In vivo whole brain structural imaging stacks and the functional imaging sequences of single slice were obtained for analysis of calcium activities at single-cell resolution. A two-fold increase in imaging speed of conventional confocal LSM makes it possible to capture the sequence of the neurons' activities and help reveal the potential functional connections in the whole zebrafish's brain.
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Affiliation(s)
- Zhe Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074,
China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074,
China
| | - Li Mei
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074,
China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074,
China
| | - Fei Xia
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074,
China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074,
China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074,
China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074,
China
| | - Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074,
China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074,
China
- Correspondence:
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074,
China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074,
China
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