Cell assemblies at multiple time scales with arbitrary lag constellations

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

[1]  Hans C. van Houwelingen,et al.  The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, New York, 2001. No. of pages: xvi+533. ISBN 0‐387‐95284‐5 , 2004 .

[2]  G. Wahba,et al.  Multivariate Bernoulli distribution , 2012, 1206.1874.

[3]  J. Teugels Some representations of the multivariate Bernoulli and binomial distributions , 1990 .

[4]  A Gordon,et al.  Classification, 2nd Edition , 1999 .

[5]  Adriano B. L. Tort,et al.  Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis , 2011, PloS one.

[6]  Daniel Durstewitz,et al.  Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex , 2008, Proceedings of the National Academy of Sciences.

[7]  Peter Smith,et al.  Probability of Repeating Patterns in Simultaneous Neural Data , 2010, Neural Computation.

[8]  J. Csicsvari,et al.  Organization of cell assemblies in the hippocampus , 2003, Nature.

[9]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[10]  Yuji Ikegaya,et al.  Synfire Chains and Cortical Songs: Temporal Modules of Cortical Activity , 2004, Science.

[11]  David J. Foster,et al.  Reverse replay of behavioural sequences in hippocampal place cells during the awake state , 2006, Nature.

[12]  Sonja Grün,et al.  Unitary Events in Multiple Single-Neuron Spiking Activity: I. Detection and Significance , 2002, Neural Computation.

[13]  Sonja Grün,et al.  Effect of cross-trial nonstationarity on joint-spike events , 2003, Biological Cybernetics.

[14]  G L Gerstein,et al.  Detecting spatiotemporal firing patterns among simultaneously recorded single neurons. , 1988, Journal of neurophysiology.

[15]  G. Buzsáki,et al.  Theta Oscillations Provide Temporal Windows for Local Circuit Computation in the Entorhinal-Hippocampal Loop , 2009, Neuron.

[16]  W. Singer,et al.  Visuomotor integration is associated with zero time-lag synchronization among cortical areas , 1997, Nature.

[17]  Matthew A Wilson,et al.  A combinatorial method for analyzing sequential firing patterns involving an arbitrary number of neurons based on relative time order. , 2004, Journal of neurophysiology.

[18]  Wolf Singer,et al.  Neuronal Synchrony: A Versatile Code for the Definition of Relations? , 1999, Neuron.

[19]  Sonja Grün,et al.  Unitary Events in Multiple Single-Neuron Spiking Activity: II. Nonstationary Data , 2002, Neural Computation.

[20]  Mauricio Barahona,et al.  Revealing cell assemblies at multiple levels of granularity , 2014, Journal of Neuroscience Methods.

[21]  M. London,et al.  Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex , 2010, Nature.

[22]  N. Wermuth,et al.  Nonlinear Time Series: Nonparametric and Parametric Methods , 2005 .

[23]  Gordon Pipa,et al.  NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events , 2009, Journal of Computational Neuroscience.

[24]  A D Redish,et al.  Prediction, sequences and the hippocampus , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[25]  Stefan Rotter,et al.  Higher-order correlations in non-stationary parallel spike trains : statistical modeling and inference , 2022 .

[26]  Carlos D. Brody,et al.  Correlations Without Synchrony , 1999, Neural Computation.

[27]  Emery N. Brown,et al.  State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data , 2012, PLoS Comput. Biol..

[28]  Michael N. Shadlen,et al.  Synchrony Unbound A Critical Evaluation of the Temporal Binding Hypothesis , 1999, Neuron.

[29]  W. Newsome,et al.  The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding , 1998, The Journal of Neuroscience.

[30]  H. Eichenbaum Time cells in the hippocampus: a new dimension for mapping memories , 2014, Nature Reviews Neuroscience.

[31]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[32]  T. Sejnowski,et al.  Neurocomputational models of working memory , 2000, Nature Neuroscience.

[33]  Igor V. Tetko,et al.  A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings , 2001, Journal of Neuroscience Methods.

[34]  Camille Roth,et al.  Natural Scales in Geographical Patterns , 2017, Scientific Reports.

[35]  Emery N. Brown,et al.  Estimating a State-space Model from Point Process Observations Emery N. Brown , 2022 .

[36]  Leonie Welberg,et al.  Epigenetics: A lingering smell? , 2013, Nature Reviews Neuroscience.

[37]  John P. Cunningham,et al.  Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity , 2008, NIPS.

[38]  G. Buzsáki,et al.  Forward and reverse hippocampal place-cell sequences during ripples , 2007, Nature Neuroscience.

[39]  L. Nadel,et al.  The Hippocampus as a Cognitive Map , 1978 .

[40]  R. Passingham The hippocampus as a cognitive map J. O'Keefe & L. Nadel, Oxford University Press, Oxford (1978). 570 pp., £25.00 , 1979, Neuroscience.

[41]  Ohad Ben-Shahar,et al.  Stochastic Emergence of Repeating Cortical Motifs in Spontaneous Membrane Potential Fluctuations In Vivo , 2007, Neuron.

[42]  Ad Aertsen,et al.  Stable propagation of synchronous spiking in cortical neural networks , 1999, Nature.

[43]  Julie A. Wall,et al.  A Spiking Neural Network Model of the Medial Superior Olive Using Spike Timing Dependent Plasticity for Sound Localization , 2010, Frontiers in computational neuroscience.

[44]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[45]  Mehdi Khamassi,et al.  Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution , 2009, Journal of Computational Neuroscience.

[46]  James R. Schott,et al.  Principles of Multivariate Analysis: A User's Perspective , 2002 .

[47]  Sonja Grün,et al.  Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task , 2016, The Journal of Neuroscience.

[48]  A. Lansner,et al.  The cortex as a central pattern generator , 2005, Nature Reviews Neuroscience.

[49]  Stefan Rotter,et al.  Statistical Significance of Coincident Spikes: Count-Based Versus Rate-Based Statistics , 2002, Neural Computation.

[50]  D. R. Euston,et al.  Fast-Forward Playback of Recent Memory Sequences in Prefrontal Cortex During Sleep , 2007, Science.

[51]  Christian Borgelt,et al.  Finding neural assemblies with frequent item set mining , 2013, Front. Neuroinform..

[52]  Mahesan Niranjan,et al.  Estimating a State-Space Model from Point Process Observations: A Note on Convergence , 2010, Neural Computation.

[53]  Sonja Grün,et al.  Data-driven significance estimation for precise spike correlation. , 2009, Journal of neurophysiology.

[54]  D. Durstewitz,et al.  Contextual encoding by ensembles of medial prefrontal cortex neurons , 2012, Proceedings of the National Academy of Sciences.

[55]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[56]  Shun-ichi Amari,et al.  Information-Geometric Measure for Neural Spikes , 2002, Neural Computation.

[57]  M. Jung,et al.  Dynamics of Population Code for Working Memory in the Prefrontal Cortex , 2003, Neuron.

[58]  Sonja Grün,et al.  ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains , 2016, PLoS Comput. Biol..

[59]  W. Singer,et al.  The gamma cycle , 2007, Trends in Neurosciences.

[60]  H. Markram,et al.  Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. , 1997, The Journal of physiology.

[61]  Asohan Amarasingham,et al.  Internally Generated Cell Assembly Sequences in the Rat Hippocampus , 2008, Science.

[62]  B. McNaughton,et al.  Reactivation of hippocampal ensemble memories during sleep. , 1994, Science.

[63]  G. Buzsáki Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning , 2015, Hippocampus.

[64]  M. Khamassi,et al.  Replay of rule-learning related neural patterns in the prefrontal cortex during sleep , 2009, Nature Neuroscience.

[65]  Eldon Emberly,et al.  Action and outcome activity state patterns in the anterior cingulate cortex. , 2013, Cerebral cortex.

[66]  George L. Gerstein,et al.  Identification of functionally related neural assemblies , 1978, Brain Research.

[67]  Pieter R. Roelfsema,et al.  How Precise is Neuronal Synchronization? , 1995, Neural Computation.

[68]  Albert K. Lee,et al.  Memory of Sequential Experience in the Hippocampus during Slow Wave Sleep , 2002, Neuron.

[69]  M. Abeles,et al.  Detecting precise firing sequences in experimental data , 2001, Journal of Neuroscience Methods.

[70]  J. Fuster Unit activity in prefrontal cortex during delayed-response performance: neuronal correlates of transient memory. , 1973, Journal of neurophysiology.

[71]  B. McNaughton,et al.  Replay of Neuronal Firing Sequences in Rat Hippocampus During Sleep Following Spatial Experience , 1996, Science.

[72]  Christian Borgelt,et al.  Statistical evaluation of synchronous spike patterns extracted by frequent item set mining , 2013, Front. Comput. Neurosci..

[73]  P. Goldman-Rakic Cellular basis of working memory , 1995, Neuron.

[74]  G. Buzsáki,et al.  Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex , 2008, Nature Neuroscience.

[75]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[76]  Igor V. Tetko,et al.  A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns , 2001, Journal of Neuroscience Methods.

[77]  Hagai Bergman,et al.  Temporal Convergence of Dynamic Cell Assemblies in the Striato-Pallidal Network , 2012, The Journal of Neuroscience.

[78]  Daniel Durstewitz,et al.  Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation. , 2013, Journal of neurophysiology.

[79]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[80]  Debashis Kushary,et al.  Bootstrap Methods and Their Application , 2000, Technometrics.

[81]  G. Buzsáki,et al.  Sequential structure of neocortical spontaneous activity in vivo , 2007, Proceedings of the National Academy of Sciences.

[82]  D. Durstewitz,et al.  Abrupt Transitions between Prefrontal Neural Ensemble States Accompany Behavioral Transitions during Rule Learning , 2010, Neuron.

[83]  Wolf Singer,et al.  Detecting Multineuronal Temporal Patterns in Parallel Spike Trains , 2012, Front. Neuroinform..

[84]  Mark D Humphries,et al.  Spike-Train Communities: Finding Groups of Similar Spike Trains , 2011, The Journal of Neuroscience.

[85]  Vítor Lopes-dos-Santos,et al.  Detecting cell assemblies in large neuronal populations , 2013, Journal of Neuroscience Methods.

[86]  R. Yuste,et al.  Visual stimuli recruit intrinsically generated cortical ensembles , 2014, Proceedings of the National Academy of Sciences.

[87]  Angelo Arleo,et al.  Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex , 2015, PLoS biology.

[88]  K. Harris Neural signatures of cell assembly organization , 2005, Nature Reviews Neuroscience.

[89]  Paul Miller,et al.  Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles , 2007, Proceedings of the National Academy of Sciences.

[90]  G. Laurent,et al.  Multiplexing using synchrony in the zebrafish olfactory bulb , 2004, Nature Neuroscience.

[91]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[92]  John M. Beggs,et al.  Neuronal Avalanches in Neocortical Circuits , 2003, The Journal of Neuroscience.

[93]  T. Hunter,et al.  Organization of cell assemblies in the hippocampus , 2003 .

[94]  P. S. Sastry,et al.  Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains , 2008, Neural Computation.

[95]  A. Aertsen,et al.  Spike synchronization and rate modulation differentially involved in motor cortical function. , 1997, Science.

[96]  Peter Smith,et al.  A Set Probability Technique for Detecting Relative Time Order Across Multiple Neurons , 2006, Neural Computation.

[97]  Sonja Grün,et al.  Detecting synfire chains in parallel spike data , 2012, Journal of Neuroscience Methods.

[98]  E. Seidemann,et al.  Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[99]  Sonja Grün,et al.  CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains , 2009, Journal of Computational Neuroscience.

[100]  Moshe Abeles,et al.  Corticonics: Neural Circuits of Cerebral Cortex , 1991 .