Detecting cell assemblies in large neuronal populations

Recent progress in the technology for single unit recordings has given the neuroscientific community the opportunity to record the spiking activity of large neuronal populations. At the same pace, statistical and mathematical tools were developed to deal with high-dimensional datasets typical of such recordings. A major line of research investigates the functional role of subsets of neurons with significant co-firing behavior: the Hebbian cell assemblies. Here we review three linear methods for the detection of cell assemblies in large neuronal populations that rely on principal and independent component analysis. Based on their performance in spike train simulations, we propose a modified framework that incorporates multiple features of these previous methods. We apply the new framework to actual single unit recordings and show the existence of cell assemblies in the rat hippocampus, which typically oscillate at theta frequencies and couple to different phases of the underlying field rhythm.

[1]  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.

[2]  M. Nicolelis,et al.  Sensorimotor encoding by synchronous neural ensemble activity at multiple levels of the somatosensory system. , 1995, Science.

[3]  P. Seba,et al.  Random matrix analysis of human EEG data. , 2003, Physical review letters.

[4]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

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

[6]  M. Nicolelis,et al.  Principal component analysis of neuronal ensemble activity reveals multidimensional somatosensory representations , 1999, Journal of Neuroscience Methods.

[7]  Christian Borgelt,et al.  Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains , 2009, Comput. Intell. Neurosci..

[8]  M. Laubach,et al.  Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task , 2022 .

[9]  J. O'Keefe,et al.  The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. , 1971, Brain research.

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

[11]  W. Singer,et al.  Synchronization of neuronal responses in primary visual cortex of monkeys viewing natural images. , 2008, Journal of neurophysiology.

[12]  M. Lavine,et al.  Long-Lasting Novelty-Induced Neuronal Reverberation during Slow-Wave Sleep in Multiple Forebrain Areas , 2004, PLoS biology.

[13]  G. Biroli,et al.  On the top eigenvalue of heavy-tailed random matrices , 2006, cond-mat/0609070.

[14]  Sonja Grün,et al.  Higher-Order Correlations in Non-Stationary Parallel Spike Trains: Statistical Modeling and Inference , 2009, Front. Comput. Neurosci..

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

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

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

[18]  Konrad P Kording,et al.  How advances in neural recording affect data analysis , 2011, Nature Neuroscience.

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

[20]  Christian Borgelt,et al.  Complexity distribution as a measure for assembly size and temporal precision , 2010, Neural Networks.

[21]  V. Plerou,et al.  Random matrix approach to cross correlations in financial data. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.

[23]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

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

[25]  Yoram Ben-Shaul,et al.  Temporally precise cortical firing patterns are associated with distinct action segments. , 2006, Journal of neurophysiology.

[26]  V. Marčenko,et al.  DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES , 1967 .

[27]  G. Buzsáki.  Large-scale recording of neuronal ensembles , 2004, Nature Neuroscience.

[28]  R. Quiroga,et al.  Extracting information from neuronal populations : information theory and decoding approaches , 2022 .

[29]  D. Perrett,et al.  Visual neurones responsive to faces in the monkey temporal cortex , 2004, Experimental Brain Research.

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

[31]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[32]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

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

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

[35]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

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

[37]  M. Diamond,et al.  Whisker Vibration Information Carried by Rat Barrel Cortex Neurons , 2004, The Journal of Neuroscience.

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

[39]  M. Laubach,et al.  Independent component analyses for quantifying neuronal ensemble interactions , 1999, Journal of Neuroscience Methods.

[40]  R. C. Tees.  Review of The organization of behavior: A neuropsychological theory. , 2003 .

[41]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[42]  E. Adrian,et al.  The impulses produced by sensory nerve‐endings , 1926 .

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

[44]  Mehdi Khamassi,et al.  Coherent Theta Oscillations and Reorganization of Spike Timing in the Hippocampal- Prefrontal Network upon Learning , 2010, Neuron.

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

[46]  M. Wilson,et al.  Temporally Structured Replay of Awake Hippocampal Ensemble Activity during Rapid Eye Movement Sleep , 2001, Neuron.

[47]  Evgueniy V. Lubenov,et al.  Prefrontal Phase Locking to Hippocampal Theta Oscillations , 2005, Neuron.