Sparse low-order interaction network underlies a highly correlated and learnable neural population code
about
Information theoretic approaches to understanding circuit functionOptogenetic activation of an inhibitory network enhances feedforward functional connectivity in auditory cortex.Gibbs distribution analysis of temporal correlations structure in retina ganglion cells.Thermodynamics and signatures of criticality in a network of neurons.On criticality in high-dimensional data.Learning maximum entropy models from finite-size data sets: A fast data-driven algorithm allows sampling from the posterior distribution.The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data.Modeling higher-order correlations within cortical microcolumns.Network neuroscience.Temporal precision in population-but not individual neuron-dynamics reveals rapid experience-dependent plasticity in the rat barrel cortex.Stimulus-dependent maximum entropy models of neural population codes.Searching for collective behavior in a large network of sensory neuronsThe sign rule and beyond: boundary effects, flexibility, and noise correlations in neural population codes.Natural grouping of neural responses reveals spatially segregated clusters in prearcuate cortex.Improved estimation and interpretation of correlations in neural circuitsSimultaneous silence organizes structured higher-order interactions in neural populationsDecoding thalamic afferent input using microcircuit spiking activity.Triplet correlations among similarly tuned cells impact population codingError-Robust Modes of the Retinal Population Code.Synergy from silence in a combinatorial neural code.Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations.Population rate dynamics and multineuron firing patterns in sensory cortexA pairwise maximum entropy model accurately describes resting-state human brain networks.Missing mass approximations for the partition function of stimulus driven Ising modelsHigh-order social interactions in groups of mice.A Tractable Method for Describing Complex Couplings between Neurons and Population Rate.High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia.Hyper-connectivity of functional networks for brain disease diagnosis.Small-World Brain Networks Revisited.Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity.Analysis of Neuronal Spike Trains, Deconstructed.Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings.The relevance of network micro-structure for neural dynamicsPRANAS: A New Platform for Retinal Analysis and Simulation.A generative spike train model with time-structured higher order correlations.Energy landscapes of resting-state brain networks.When do microcircuits produce beyond-pairwise correlations?Sparsity and compressed coding in sensory systems.A thesaurus for a neural population code.Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks.
P2860
Q26860936-03621EE0-31C7-486C-A55A-BE2F6E0192BEQ30425712-B0F68321-F468-4EC1-B74C-D3C127951F83Q30524323-74D63174-015B-4D7A-B3BC-FD2E9A4C77CAQ30665056-4B1371BE-67ED-4DEC-B74A-25C533B22F1FQ30795285-0C7C5939-BCF3-4D9F-A37C-CBCC95DE31E8Q31130714-807A7297-6242-4BE3-ADA9-DE1EFF559738Q31143726-961D73C3-5FAA-4331-AD07-9313855EBFC9Q31172256-09D5039D-09F9-413A-91B2-7D8B8C627476Q33837327-DA879832-29DB-4A55-A910-067074FF8918Q34569602-55EF8B8C-3109-4143-8D6F-81561E4D1BE8Q34629073-1447E108-0D0F-4AC9-A309-93B94F663F18Q35082161-8264FABD-349F-425E-8583-40EC1151AA32Q35105564-AC16D187-65D7-4A0A-A962-81A4346806B1Q35197680-4F3F73DF-DE23-418D-8709-4394F067329FQ35235172-83074CC2-71CB-4E6B-85EC-9E21B5FBC3F2Q35545766-A735692B-4AF0-4196-9653-624ECF364B48Q35560359-2DCFF4F4-016C-4C27-8646-FE36A114C96EQ35617609-C4DE3542-C2AA-40C1-A9B9-FCA42227C0BFQ36194956-74A99BFE-EAAF-4B17-AC5D-D9971366870DQ36246815-79B720E0-D6BA-400D-A6D4-899ECA2BD3B4Q36251187-2F5A9589-B742-41DD-9678-4CC5ADF90613Q36460663-DD502B87-85AB-4065-B6DB-8CF932E0107BQ36864556-7973BD88-136C-408F-8241-CAD14A94FB4DQ37039665-80D83800-6EC7-46BE-8818-01DF170A3B5AQ37145703-379A08CF-FEDF-42D2-B865-12FBD48C6615Q37184555-93E9E2D8-0C94-435F-9315-C6C7C0EC8B74Q37496256-21DC4523-7ED6-4E3F-A7EA-70074F30006AQ37676900-B1A4A2E3-8C87-4946-9E3E-0C0032ED1265Q38818877-7A8A7D70-DDA6-40DE-9ACE-C123E76ABE68Q38856061-173197B1-8837-4519-8E30-11500BD0B7E2Q38914176-6186B49F-D91B-44F3-8983-956CC5ECC80BQ39548838-2CEF3B1B-5254-4897-B4D4-9A46ADB032C2Q41461989-AF016789-AC85-473A-BC53-926121D15D3CQ41622505-4DE8849B-FC3B-4260-94F5-584D92F6469CQ41886150-5B5AE502-462C-43E9-8617-5AD555ED2ED7Q41912571-D010E351-4CAD-4783-B975-2EE56AAFC270Q41921342-0C1E0670-A30A-4047-AFC9-F2D14F525034Q41976489-F9D69D1E-BBBA-4601-A46B-6A14C6A92B24Q42043985-F169EBA2-F360-48DE-81A8-629F77FE42BEQ42107777-DB9B0796-68BF-430E-91C0-76C8F368A280
P2860
Sparse low-order interaction network underlies a highly correlated and learnable neural population code
description
2011 nî lūn-bûn
@nan
2011 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Sparse low-order interaction n ...... arnable neural population code
@ast
Sparse low-order interaction n ...... arnable neural population code
@en
type
label
Sparse low-order interaction n ...... arnable neural population code
@ast
Sparse low-order interaction n ...... arnable neural population code
@en
prefLabel
Sparse low-order interaction n ...... arnable neural population code
@ast
Sparse low-order interaction n ...... arnable neural population code
@en
P2860
P356
P1476
Sparse low-order interaction n ...... arnable neural population code
@en
P2093
Elad Ganmor
P2860
P304
P356
10.1073/PNAS.1019641108
P407
P577
2011-05-20T00:00:00Z