Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity
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[1] J. Knott. The organization of behavior: A neuropsychological theory , 1951 .
[2] D. Perkel,et al. Simultaneously Recorded Trains of Action Potentials: Analysis and Functional Interpretation , 1969, Science.
[3] Bruce L. McNaughton,et al. The stereotrode: A new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records , 1983, Journal of Neuroscience Methods.
[4] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[5] M K Habib,et al. Dynamics of neuronal firing correlation: modulation of "effective connectivity". , 1989, Journal of neurophysiology.
[6] T. Bliss,et al. A synaptic model of memory: long-term potentiation in the hippocampus , 1993, Nature.
[7] B. McNaughton,et al. Reactivation of hippocampal ensemble memories during sleep. , 1994, Science.
[8] Markus Meister,et al. Multi-neuronal signals from the retina: acquisition and analysis , 1994, Journal of Neuroscience Methods.
[9] M. Wilson,et al. Coordinated Interactions between Hippocampal Ripples and Cortical Spindles during Slow-Wave Sleep , 1998, Neuron.
[10] E N Brown,et al. A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells , 1998, The Journal of Neuroscience.
[11] Miguel A. L. Nicolelis,et al. Methods for Neural Ensemble Recordings , 1998 .
[12] V. Brown,et al. Medial Frontal Cortex Mediates Perceptual Attentional Set Shifting in the Rat , 2000, The Journal of Neuroscience.
[13] Albert K. Lee,et al. Memory of Sequential Experience in the Hippocampus during Slow Wave Sleep , 2002, Neuron.
[14] J. Csicsvari,et al. Organization of cell assemblies in the hippocampus , 2003, Nature.
[15] Uri T Eden,et al. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. , 2005, Journal of neurophysiology.
[16] Michael J. Berry,et al. Weak pairwise correlations imply strongly correlated network states in a neural population , 2005, Nature.
[17] David J. Foster,et al. Reverse replay of behavioural sequences in hippocampal place cells during the awake state , 2006, Nature.
[18] G. Tononi,et al. Sleep function and synaptic homeostasis. , 2006, Sleep medicine reviews.
[19] G. Buzsáki,et al. Forward and reverse hippocampal place-cell sequences during ripples , 2007, Nature Neuroscience.
[20] Adam Johnson,et al. Neural Ensembles in CA3 Transiently Encode Paths Forward of the Animal at a Decision Point , 2007, The Journal of Neuroscience.
[21] Eero P. Simoncelli,et al. Spatio-temporal correlations and visual signalling in a complete neuronal population , 2008, Nature.
[22] G. Buzsáki,et al. Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex , 2008, Nature Neuroscience.
[23] Robert E. Schapire,et al. Faster solutions of the inverse pairwise Ising problem , 2008 .
[24] Lee E Miller,et al. Inferring functional connections between neurons , 2008, Current Opinion in Neurobiology.
[25] M. Khamassi,et al. Replay of rule-learning related neural patterns in the prefrontal cortex during sleep , 2009, Nature Neuroscience.
[26] Mehdi Khamassi,et al. Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution , 2009, Journal of Computational Neuroscience.
[27] G. Buzsáki,et al. Selective suppression of hippocampal ripples impairs spatial memory , 2009, Nature Neuroscience.
[28] Santo Fortunato,et al. Community detection in graphs , 2009, ArXiv.
[29] Sami El Boustani,et al. Prediction of spatiotemporal patterns of neural activity from pairwise correlations. , 2009, Physical review letters.
[30] S. Leibler,et al. Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods , 2009, Proceedings of the National Academy of Sciences.
[31] Liam Paninski,et al. Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models , 2010, Journal of Computational Neuroscience.
[32] Mehdi Khamassi,et al. Coherent Theta Oscillations and Reorganization of Spike Timing in the Hippocampal- Prefrontal Network upon Learning , 2010, Neuron.
[33] J. O’Neill,et al. Play it again: reactivation of waking experience and memory , 2010, Trends in Neurosciences.
[34] Simona Cocco,et al. Fast inference of interactions in assemblies of stochastic integrate-and-fire neurons from spike recordings , 2011, Journal of Computational Neuroscience.
[35] György Buzsáki,et al. Neural Syntax: Cell Assemblies, Synapsembles, and Readers , 2010, Neuron.
[36] Karl J. Friston. Functional and Effective Connectivity: A Review , 2011, Brain Connect..
[37] J. Hertz,et al. Mean field theory for nonequilibrium network reconstruction. , 2010, Physical review letters.
[38] Simona Cocco,et al. Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data , 2011, Physical review letters.
[39] Jascha Sohl-Dickstein,et al. A new method for parameter estimation in probabilistic models: Minimum probability flow , 2011, Physical review letters.
[40] Margaret F. Carr,et al. Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval , 2011, Nature Neuroscience.
[41] H. Sebastian Seung,et al. Neuroscience: Towards functional connectomics , 2011, Nature.
[42] R. Monasson,et al. Adaptive Cluster Expansion for the Inverse Ising Problem: Convergence, Algorithm and Tests , 2011, 1110.5416.
[43] S. Rumpel,et al. Discrete Neocortical Dynamics Predict Behavioral Categorization of Sounds , 2012, Neuron.
[44] P. Castillo. Presynaptic LTP and LTD of excitatory and inhibitory synapses. , 2012, Cold Spring Harbor perspectives in biology.
[45] E. Aurell,et al. Inverse Ising inference using all the data. , 2011, Physical review letters.
[46] Brad E. Pfeiffer,et al. Hippocampal place cell sequences depict future paths to remembered goals , 2013, Nature.
[47] Inbar Brosh,et al. Holographic optogenetic stimulation of patterned neuronal activity for vision restoration , 2013, Nature Communications.
[48] Vítor Lopes-dos-Santos,et al. Detecting cell assemblies in large neuronal populations , 2013, Journal of Neuroscience Methods.
[49] Margaret F. Carr,et al. Hippocampal SWR Activity Predicts Correct Decisions during the Initial Learning of an Alternation Task , 2013, Neuron.
[50] S. Cocco,et al. Ising models for neural activity inferred via selective cluster expansion: structural and coding properties , 2013 .
[51] Marijn C. W. Kroes,et al. Light sleep versus slow wave sleep in memory consolidation: a question of global versus local processes? , 2014, Trends in Neurosciences.
[52] Mauricio Barahona,et al. Revealing cell assemblies at multiple levels of granularity , 2014, Journal of Neuroscience Methods.
[53] Long-Term Recordings Improve the Detection of Weak Excitatory–Excitatory Connections in Rat Prefrontal Cortex , 2014, The Journal of Neuroscience.
[54] Kenneth D. Harris,et al. High-Dimensional Cluster Analysis with the Masked EM Algorithm , 2013, Neural Computation.
[55] Bruno A. Olshausen,et al. Modeling Higher-Order Correlations within Cortical Microcolumns , 2014, PLoS Comput. Biol..
[56] Philipp J. Keller,et al. Whole-animal functional and developmental imaging with isotropic spatial resolution , 2015, Nature Methods.
[57] G. Buzsáki. Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning , 2015, Hippocampus.
[58] Joshua W Shaevitz,et al. Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans , 2015, Proceedings of the National Academy of Sciences.
[59] Willy Supatto,et al. Whole-brain functional imaging with two-photon light-sheet microscopy , 2015, Nature Methods.
[60] Simona Cocco,et al. Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings , 2016, Journal of Computational Neuroscience.
[61] Simona Cocco,et al. ACE: adaptive cluster expansion for maximum entropy graphical model inference , 2016, bioRxiv.
[62] Ulisse Ferrari. Learning maximum entropy models from finite-size data sets: A fast data-driven algorithm allows sampling from the posterior distribution. , 2016, Physical review. E.
[63] Thierry Mora,et al. Random versus maximum entropy models of neural population activity. , 2017, Physical review. E.
[64] Simona Cocco,et al. Functional connectivity models for decoding of spatial representations from hippocampal CA1 recordings , 2016, Journal of Computational Neuroscience.