| | Models |
| 1. |
2 Distinct Classes of L2 and L3 Pyramidal Neurons in Human Temporal Cortex (Deitcher et al 2017)
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| 2. |
A computational model of systems memory consolidation and reconsolidation (Helfer & Shultz 2019)
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| 3. |
A fast model of voltage-dependent NMDA Receptors (Moradi et al. 2013)
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| 4. |
A Fast Rhythmic Bursting Cell: in vivo cell modeling (Lee 2007)
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| 5. |
A full-scale cortical microcircuit spiking network model (Shimoura et al 2018)
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| 6. |
A microcircuit model of the frontal eye fields (Heinzle et al. 2007)
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| 7. |
A Model Circuit of Thalamocortical Convergence (Behuret et al. 2013)
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| 8. |
A multilayer cortical model to study seizure propagation across microdomains (Basu et al. 2015)
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| 9. |
A Neural mass computational model of the Thalamocorticothalamic circuitry (Bhattacharya et al. 2011)
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| 10. |
A neural mass model for critical assessment of brain connectivity (Ursino et al 2020)
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| 11. |
A neural mass model of cross frequency coupling (Chehelcheraghi et al 2017)
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| 12. |
A neurocomputational model of classical conditioning phenomena (Moustafa et al. 2009)
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| 13. |
A single column thalamocortical network model (Traub et al 2005)
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| 14. |
A spiking model of cortical broadcast and competition (Shanahan 2008)
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| 15. |
A spiking NN for amplification of feature-selectivity with specific connectivity (Sadeh et al 2015)
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| 16. |
Accurate and fast simulation of channel noise in conductance-based model neurons (Linaro et al 2011)
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| 17. |
Action potential-evoked Na+ influx are similar in axon and soma (Fleidervish et al. 2010)
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| 18. |
Action potential-evoked Na+ influx similar in axon and soma (Fleidervish et al. 2010) (Python)
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| 19. |
Allen Institute: Gad2-IRES-Cre VISp layer 5 472447460
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| 20. |
Allen Institute: Gad2-IRES-Cre VISp layer 5 473561729
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| 21. |
Allen Institute: Htr3a-Cre VISp layer 2/3 472352327
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| 22. |
Allen Institute: Htr3a-Cre VISp layer 2/3 472421285
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| 23. |
Allen Institute: Nr5a1-Cre VISp layer 2/3 473862496
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| 24. |
Allen Institute: Nr5a1-Cre VISp layer 4 329322394
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| 25. |
Allen Institute: Nr5a1-Cre VISp layer 4 472306544
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| 26. |
Allen Institute: Nr5a1-Cre VISp layer 4 472442377
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| 27. |
Allen Institute: Nr5a1-Cre VISp layer 4 472451419
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| 28. |
Allen Institute: Nr5a1-Cre VISp layer 4 472915634
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| 29. |
Allen Institute: Nr5a1-Cre VISp layer 4 473834758
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| 30. |
Allen Institute: Nr5a1-Cre VISp layer 4 473863035
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| 31. |
Allen Institute: Nr5a1-Cre VISp layer 4 473871429
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| 32. |
Allen Institute: Ntsr1-Cre VISp layer 4 472430904
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| 33. |
Allen Institute: Pvalb-IRES-Cre VISp layer 2/3 472306616
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| 34. |
Allen Institute: Pvalb-IRES-Cre VISp layer 5 471085845
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| 35. |
Allen Institute: Pvalb-IRES-Cre VISp layer 5 472349114
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| 36. |
Allen Institute: Pvalb-IRES-Cre VISp layer 5 472912177
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| 37. |
Allen Institute: Pvalb-IRES-Cre VISp layer 5 473465774
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| 38. |
Allen Institute: Pvalb-IRES-Cre VISp layer 5 473862421
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| 39. |
Allen Institute: Pvalb-IRES-Cre VISp layer 6a 471081668
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| 40. |
Allen Institute: Pvalb-IRES-Cre VISp layer 6a 472301074
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| 41. |
Allen Institute: Pvalb-IRES-Cre VISp layer 6a 473860269
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| 42. |
Allen Institute: Rbp4-Cre VISp layer 5 472424854
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| 43. |
Allen Institute: Rbp4-Cre VISp layer 6a 473871592
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| 44. |
Allen Institute: Rorb-IRES2-Cre-D VISp layer 2/3 472299294
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| 45. |
Allen Institute: Rorb-IRES2-Cre-D VISp layer 2/3 472434498
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| 46. |
Allen Institute: Rorb-IRES2-Cre-D VISp layer 4 473863510
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| 47. |
Allen Institute: Rorb-IRES2-Cre-D VISp layer 5 471087975
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| 48. |
Allen Institute: Rorb-IRES2-Cre-D VISp layer 5 473561660
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| 49. |
Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 472300877
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| 50. |
Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 472427533
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| 51. |
Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 472912107
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| 52. |
Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 473465456
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| 53. |
Allen Institute: Scnn1a-Tg2-Cre VISp layer 5 472306460
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| 54. |
Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 329321704
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| 55. |
Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 472363762
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| 56. |
Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 473862845
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| 57. |
Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 473872986
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| 58. |
Allen Institute: Scnn1a-Tg3-Cre VISp layer 5 472455509
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| 59. |
Allen Institute: Scnn1a-Tg3-Cre VISp layer 5 473863578
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| 60. |
Allen Institute: Scnn1a-Tg3-Cre VISp layer 5 473871773
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| 61. |
Allen Institute: Sst-IRES-Cre VISp layer 2/3 471086533
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| 62. |
Allen Institute: Sst-IRES-Cre VISp layer 2/3 472304676
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| 63. |
Allen Institute: Sst-IRES-Cre VISp layer 4 472304539
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| 64. |
Allen Institute: Sst-IRES-Cre VISp layer 5 472299363
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| 65. |
Allen Institute: Sst-IRES-Cre VISp layer 5 472450023
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| 66. |
Allen Institute: Sst-IRES-Cre VISp layer 5 473835796
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| 67. |
Allen Institute: Sst-IRES-Cre VISp layer 6a 472440759
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| 68. |
Alpha rhythm in vitro visual cortex (Traub et al 2020)
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| 69. |
An agent-based computational model for cortical layer formation (Bauer et al 2021)
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| 70. |
AP back-prop. explains threshold variability and rapid rise (McCormick et al. 2007, Yu et al. 2008)
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| 71. |
Apical Length Governs Computational Diversity of Layer 5 Pyramidal Neurons (Galloni et al 2020)
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| 72. |
Asynchronous irregular and up/down states in excitatory and inhibitory NNs (Destexhe 2009)
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| 73. |
Ave. neuron model for slow-wave sleep in cortex Tatsuki 2016 Yoshida 2018 Rasmussen 2017 (all et al)
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| 74. |
Axonal Projection and Interneuron Types (Helmstaedter et al. 2008)
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| 75. |
Basal ganglia-corticothalamic (BGCT) network (Chen et al., 2014)
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| 76. |
Biochemically detailed model of LTP and LTD in a cortical spine (Maki-Marttunen et al 2020)
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| 77. |
Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009)
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| 78. |
Biophysically realistic neuron models for simulation of cortical stimulation (Aberra et al. 2018)
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| 79. |
Ca+/HCN channel-dependent persistent activity in multiscale model of neocortex (Neymotin et al 2016)
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| 80. |
Collection of simulated data from a thalamocortical network model (Glabska, Chintaluri, Wojcik 2017)
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| 81. |
Combining modeling, deep learning for MEA neuron localization, classification (Buccino et al 2018)
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| 82. |
Compartmentalization of GABAergic inhibition by dendritic spines (Chiu et al. 2013)
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| 83. |
Composite spiking network/neural field model of Parkinsons (Kerr et al 2013)
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| 84. |
Comprehensive models of human cortical pyramidal neurons (Eyal et al 2018)
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| 85. |
Computational aspects of feedback in neural circuits (Maass et al 2006)
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| 86. |
Computational Surgery (Lytton et al. 2011)
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| 87. |
Computer models of corticospinal neurons replicate in vitro dynamics (Neymotin et al. 2017)
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| 88. |
CONFIGR: a vision-based model for long-range figure completion (Carpenter et al. 2007)
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| 89. |
Cortex-Basal Ganglia-Thalamus network model (Kumaravelu et al. 2016)
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| 90. |
Cortical Basal Ganglia Network Model during Closed-loop DBS (Fleming et al 2020)
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| 91. |
Cortical Layer 5b pyr. cell with [Na+]i mechanisms, from Hay et al 2011 (Zylbertal et al 2017)
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| 92. |
Cortical network model of posttraumatic epileptogenesis (Bush et al 1999)
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| 93. |
Current Dipole in Laminar Neocortex (Lee et al. 2013)
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| 94. |
Deconstruction of cortical evoked potentials generated by subthalamic DBS (Kumaravelu et al 2018)
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| 95. |
Dendritic action potentials and computation in human layer 2/3 cortical neurons (Gidon et al 2020)
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| 96. |
Dendritic action potentials and computation in human layer 2/3 cortical neurons (Gidon et al 2020)
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| 97. |
Dendritic Discrimination of Temporal Input Sequences (Branco et al. 2010)
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| 98. |
Dendritic Na+ spike initiation and backpropagation of APs in active dendrites (Nevian et al. 2007)
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| 99. |
Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)
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| 100. |
Distal inhibitory control of sensory-evoked excitation (Egger, Schmitt et al. 2015)
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| 101. |
Distinct integration properties of noisy inputs in active dendritic subunits (Poleg-Polsky 2019)
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| 102. |
Dynamics in random NNs with multiple neuron subtypes (Pena et al 2018, Tomov et al 2014, 2016)
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| 103. |
Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011)
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| 104. |
Electrodecrements in in vitro model of infantile spasms (Traub et al 2020)
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| 105. |
Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014)
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| 106. |
Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014)
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| 107. |
Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011)
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| 108. |
Emergence of spatiotemporal sequences in spiking neuronal networks (Spreizer et al 2019)
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| 109. |
Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010)
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| 110. |
Entrainment and divisive inhibition in a neocortical neural mass model (Papasavvas et al 2020)
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| 111. |
Event-related simulation of neural processing in complex visual scenes (Mihalas et al. 2011)
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| 112. |
Excitability of PFC Basal Dendrites (Acker and Antic 2009)
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| 113. |
Extraction and classification of three cortical neuron types (Mensi et al. 2012)
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| 114. |
Firing neocortical layer V pyramidal neuron (Reetz et al. 2014; Stadler et al. 2014)
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| 115. |
Fitting predictive coding to the neurophysiological data (Spratling 2019)
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| 116. |
Four cortical interneuron subtypes (Kubota et al. 2011)
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| 117. |
Fronto-parietal visuospatial WM model with HH cells (Edin et al 2007)
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| 118. |
Functional consequences of cortical circuit abnormalities on gamma in schizophrenia (Spencer 2009)
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| 119. |
Glutamate mediated dendritic and somatic plateau potentials in cortical L5 pyr cells (Gao et al '20)
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| 120. |
Hierarchical network model of perceptual decision making (Wimmer et al 2015)
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| 121. |
High dimensional dynamics and low dimensional readouts in neural microcircuits (Haeusler et al 2006)
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| 122. |
Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008)
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| 123. |
Human L2/3 pyramidal cells with low Cm values (Eyal et al. 2016)
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| 124. |
Huntington`s disease model (Gambazzi et al. 2010)
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| 125. |
Hyperconnectivity, slow synapses in PFC mental retardation and autism model (Testa-Silva et al 2011)
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| 126. |
I&F recurrent networks with current- or conductance-based synapses (Cavallari et al. 2014)
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| 127. |
Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010)
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| 128. |
Information-processing in lamina-specific cortical microcircuits (Haeusler and Maass 2006)
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| 129. |
Inhibition and glial-K+ interaction leads to diverse seizure transition modes (Ho & Truccolo 2016)
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| 130. |
Inhibition of bAPs and Ca2+ spikes in a multi-compartment pyramidal neuron model (Wilmes et al 2016)
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| 131. |
Inhibitory control by an integral feedback signal in prefrontal cortex (Miller and Wang 2006)
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| 132. |
Inhibitory plasticity balances excitation and inhibition (Vogels et al. 2011)
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| 133. |
Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009)
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| 134. |
Irregular spiking in NMDA-driven prefrontal cortex neurons (Durstewitz and Gabriel 2006)
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| 135. |
Kernel method to calculate LFPs from networks of point neurons (Telenczuk et al 2020)
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| 136. |
Knox implementation of Destexhe 1998 spike and wave oscillation model (Knox et al 2018)
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| 137. |
L5 cortical neurons with recreated synaptic inputs in vitro correlation transfer (Linaro et al 2019)
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| 138. |
L5 PFC microcircuit used to study persistent activity (Papoutsi et al. 2014, 2013)
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| 139. |
L5 pyr. cell spiking control by oscillatory inhibition in distal apical dendrites (Li et al 2013)
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| 140. |
L5 pyramidal neuron myelination increases analog-digital facilitation extent (Zbili & Debanne 2020)
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| 141. |
L5b PC model constrained for BAC firing and perisomatic current step firing (Hay et al., 2011)
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| 142. |
Large cortex model with map-based neurons (Rulkov et al 2004)
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| 143. |
Large scale neocortical model for PGENESIS (Crone et al 2019)
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| 144. |
Large-scale laminar model of macaque cortex (Mejias et al 2016)
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| 145. |
Large-scale model of neocortical slice in vitro exhibiting persistent gamma (Tomsett et al. 2014)
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| 146. |
Layer V pyramidal cell functions and schizophrenia genetics (Mäki-Marttunen et al 2019)
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| 147. |
Layer V pyramidal cell model with reduced morphology (Mäki-Marttunen et al 2018)
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| 148. |
LFP signature of monosynaptic thalamocortical connection (Hagen et al 2017)
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| 149. |
Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007)
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| 150. |
Maximum entropy model to predict spatiotemporal spike patterns (Marre et al. 2009)
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| 151. |
Mechanisms for stable, robust, and adaptive development of orientation maps (Stevens et al. 2013)
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| 152. |
Memory savings through unified pre- and postsynaptic STDP (Costa et al 2015)
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| 153. |
Mice Somatosensory L2/3 Pyramidal cells (Iascone et al 2020)
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| 154. |
Microcircuits of L5 thick tufted pyramidal cells (Hay & Segev 2015)
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| 155. |
Mirror Neuron (Antunes et al 2017)
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| 156. |
Models for cortical UP-DOWN states in a bistable inhibitory-stabilized network (Jercog et al 2017)
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| 157. |
Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014)
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| 158. |
Multi-area layer-resolved spiking network model of resting-state dynamics in macaque visual cortex
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| 159. |
Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016)
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| 160. |
Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012)
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| 161. |
Network topologies for producing limited sustained activation (Kaiser and Hilgetag 2010)
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| 162. |
Neural mass model of the neocortex under sleep regulation (Costa et al 2016)
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| 163. |
Neural mass model of the sleeping cortex (Weigenand et al 2014)
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| 164. |
Neural mass model of the sleeping thalamocortical system (Schellenberger Costa et al 2016)
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| 165. |
NN activity impact on neocortical pyr. neurons integrative properties in vivo (Destexhe & Pare 1999)
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| 166. |
NN for proto-object based contour integration and figure-ground segregation (Hu & Niebur 2017)
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| 167. |
On the structural connectivity of large-scale models of brain networks (Giacopelli et al 2021)
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| 168. |
Orientation preference in L23 V1 pyramidal neurons (Park et al 2019)
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| 169. |
Orientation selectivity in inhibition-dominated recurrent networks (Sadeh and Rotter, 2015)
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| 170. |
Persistent synchronized bursting activity in cortical tissues (Golomb et al 2005)
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| 171. |
Perturbation sensitivity implies high noise and suggests rate coding in cortex (London et al. 2010)
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| 172. |
Pipette and membrane patch geometry effects on GABAa currents patch-clamp exps (Moroni et al. 2011)
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| 173. |
Polychronization: Computation With Spikes (Izhikevich 2005)
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| 174. |
Prefrontal–striatal Parkinsons comp. model of multicue category learning (Moustafa and Gluck 2011)
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| 175. |
Preserving axosomatic spiking features despite diverse dendritic morphology (Hay et al., 2013)
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| 176. |
Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012)
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| 177. |
Reducing variability in motor cortex activity by GABA (Hoshino et al. 2019)
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| 178. |
Reinforcement learning of targeted movement (Chadderdon et al. 2012)
|
| 179. |
Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008)
|
| 180. |
Reverse-time correlation analysis for idealized orientation tuning dynamics (Kovacic et al. 2008)
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| 181. |
Reward modulated STDP (Legenstein et al. 2008)
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| 182. |
Rhesus Monkey Layer 3 Pyramidal Neurons: V1 vs PFC (Amatrudo, Weaver et al. 2012)
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| 183. |
SCZ-associated variant effects on L5 pyr cell NN activity and delta osc. (Maki-Marttunen et al 2018)
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| 184. |
Selective control of cortical axonal spikes by a slowly inactivating K+ current (Shu et al. 2007)
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| 185. |
Sensory-evoked responses of L5 pyramidal tract neurons (Egger et al 2020)
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| 186. |
Shaping NMDA spikes by timed synaptic inhibition on L5PC (Doron et al. 2017)
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| 187. |
Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
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| 188. |
Spike propagation in dendrites with stochastic ion channels (Diba et al. 2006)
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| 189. |
Spontaneous weakly correlated excitation and inhibition (Tan et al. 2013)
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| 190. |
Stable propagation of synchronous spiking in cortical neural networks (Diesmann et al 1999)
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| 191. |
State and location dependence of action potential metabolic cost (Hallermann et al., 2012)
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| 192. |
Stochastic layer V pyramidal neuron: interpulse interval coding and noise (Singh & Levy 2017)
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| 193. |
Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010)
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| 194. |
Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013)
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| 195. |
Systematic integration of data into multi-scale models of mouse primary V1 (Billeh et al 2020)
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| 196. |
Temporal integration by stochastic recurrent network (Okamoto et al. 2007)
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| 197. |
The origin of different spike and wave-like events (Hall et al 2017)
|
| 198. |
The role of glutamate in neuronal ion homeostasis: spreading depolarization (Hubel et al 2017)
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| 199. |
Theoretical principles of DBS induced synaptic suppression (Farokhniaee & McIntyre 2019)
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| 200. |
Theory of sequence memory in neocortex (Hawkins & Ahmad 2016)
|
| 201. |
Towards a biologically plausible model of LGN-V1 pathways (Lian et al 2019)
|
| 202. |
Two populations of excitatory neurons in the superficial retrosplenial cortex (Brennan et al 2020)
|
| 203. |
Unbalanced peptidergic inhibition in superficial cortex underlies seizure activity (Hall et al 2015)
|
| 204. |
V1 and AL spiking neural network for visual contrast response in mouse (Meijer et al. 2020)
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| 205. |
Visual physiology of the layer 4 cortical circuit in silico (Arkhipov et al 2018)
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