| | Models |
| 1. |
3D model of the olfactory bulb (Migliore et al. 2014)
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| 2. |
3D olfactory bulb: operators (Migliore et al, 2015)
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| 3. |
A 1000 cell network model for Lateral Amygdala (Kim et al. 2013)
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| 4. |
A computational model of systems memory consolidation and reconsolidation (Helfer & Shultz 2019)
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| 5. |
A large-scale model of the functioning brain (spaun) (Eliasmith et al. 2012)
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| 6. |
A model of antennal lobe of bee (Chen JY et al. 2015)
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| 7. |
A Model of Selection between Stimulus and Place Strategy in a Hawkmoth (Balkenius et al. 2004)
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| 8. |
A neurocomputational model of classical conditioning phenomena (Moustafa et al. 2009)
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| 9. |
A reinforcement learning example (Sutton and Barto 1998)
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| 10. |
A simple model of neuromodulatory state-dependent synaptic plasticity (Pedrosa and Clopath, 2016)
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| 11. |
A single-cell spiking model for the origin of grid-cell patterns (D'Albis & Kempter 2017)
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| 12. |
A spiking neural network model of model-free reinforcement learning (Nakano et al 2015)
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| 13. |
Acetylcholine-modulated plasticity in reward-driven navigation (Zannone et al 2018)
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| 14. |
Adaptation of Short-Term Plasticity parameters (Esposito et al. 2015)
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| 15. |
Adaptive robotic control driven by a versatile spiking cerebellar network (Casellato et al. 2014)
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| 16. |
Alleviating catastrophic forgetting: context gating and synaptic stabilization (Masse et al 2018)
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| 17. |
Alternative time representation in dopamine models (Rivest et al. 2009)
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| 18. |
An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)
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| 19. |
Aplysia LTF model (Liu et al, 2020; Zhang et al, 2021; Liu et al 2022)
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| 20. |
Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020)
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| 21. |
Behavioral time scale synaptic plasticity underlies CA1 place fields (Bittner et al. 2017)
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| 22. |
CA1 pyramidal neurons: binding properties and the magical number 7 (Migliore et al. 2008)
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| 23. |
Calcium response prediction in the striatal spines depending on input timing (Nakano et al. 2013)
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| 24. |
Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)
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| 25. |
Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012)
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| 26. |
Cerebellar memory consolidation model (Yamazaki et al. 2015)
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| 27. |
Cerebellar Model for the Optokinetic Response (Kim and Lim 2021)
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| 28. |
Coding explains development of binocular vision and its failure in Amblyopia (Eckmann et al 2020)
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| 29. |
Cognitive and motor cortico-basal ganglia interactions during decision making (Guthrie et al 2013)
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| 30. |
Combining modeling, deep learning for MEA neuron localization, classification (Buccino et al 2018)
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| 31. |
Computational endophenotypes in addiction (Fiore et al 2018)
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| 32. |
Computational model of the distributed representation of operant reward memory (Costa et al. 2020)
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| 33. |
Conditions for synaptic specificity in maintenance phase of synaptic plasticity (Huertas et al, '22)
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| 34. |
Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)
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| 35. |
Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)
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| 36. |
Cortico - Basal Ganglia Loop (Mulcahy et al 2020)
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| 37. |
Cortico-striatal plasticity in medium spiny neurons (Gurney et al 2015)
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| 38. |
Democratic population decisions result in robust policy-gradient learning (Richmond et al. 2011)
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| 39. |
Development of modular activity of grid cells (Urdapilleta et al 2017)
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| 40. |
Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)
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| 41. |
Dynamic dopamine modulation in the basal ganglia: Learning in Parkinson (Frank et al 2004,2005)
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| 42. |
Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)
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| 43. |
ELL Medium Ganglion Cell (Mormyrid fish) (Muller et al, accepted)
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| 44. |
Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)
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| 45. |
First-Spike-Based Visual Categorization Using Reward-Modulated STDP (Mozafari et al. 2018)
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| 46. |
Fixed point attractor (Hasselmo et al 1995)
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| 47. |
FRAT: An amygdala-centered model of fear conditioning (Krasne et al. 2011)
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| 48. |
Functional balanced networks with synaptic plasticity (Sadeh et al, 2015)
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| 49. |
Generation of stable heading representations in diverse visual scenes (Kim et al 2019)
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| 50. |
Hebbian learning in a random network for PFC modeling (Lindsay, et al. 2017)
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| 51. |
Hebbian STDP for modelling the emergence of disparity selectivity (Chauhan et al 2018)
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| 52. |
Hierarchical anti-Hebbian network model for the formation of spatial cells in 3D (Soman et al 2019)
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| 53. |
Hippocampal context-dependent retrieval (Hasselmo and Eichenbaum 2005)
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| 54. |
Large scale model of the olfactory bulb (Yu et al., 2013)
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| 55. |
Learning spatial transformations through STDP (Davison, Frégnac 2006)
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| 56. |
Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007)
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| 57. |
Locus Coeruleus blocking model (Chowdhury et al.)
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| 58. |
Logarithmic distributions prove that intrinsic learning is Hebbian (Scheler 2017)
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| 59. |
Long time windows from theta modulated inhib. in entorhinal–hippo. loop (Cutsuridis & Poirazi 2015)
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| 60. |
Mapping function onto neuronal morphology (Stiefel and Sejnowski 2007)
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| 61. |
Mathematical model of behavioral time scale plasticity (BTSP) of place fields (Shouval & Cone 2021)
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| 62. |
Model of cerebellar parallel fiber-Purkinje cell LTD and LTP (Gallimore et al 2018)
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| 63. |
Model of DARPP-32 phosphorylation in striatal medium spiny neurons (Lindskog et al. 2006)
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| 64. |
Modeling hebbian and homeostatic plasticity (Toyoizumi et al. 2014)
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| 65. |
Modelling gain modulation in stability-optimised circuits (Stroud et al 2018)
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| 66. |
Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017)
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| 67. |
Multimodal stimuli learning in hawkmoths (Balkenius et al. 2008)
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| 68. |
Neurogenesis in the olfactory bulb controlled by top-down input (Adams et al 2018)
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| 69. |
Neuronify: An Educational Simulator for Neural Circuits (Dragly et al 2017)
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| 70. |
Odor supported place cell model and goal navigation in rodents (Kulvicius et al. 2008)
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| 71. |
Olfactory bulb mitral and granule cell column formation (Migliore et al. 2007)
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| 72. |
Online learning model of olfactory bulb external plexiform layer network (Imam & Cleland 2020)
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| 73. |
Optimal Localist and Distributed Coding Through STDP (Masquelier & Kheradpisheh 2018)
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| 74. |
Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)
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| 75. |
Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
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| 76. |
Phasic dopamine changes, Hebbian mechs during reversal learning in striatum (Schirru et al in press)
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| 77. |
Prefrontal cortical mechanisms for goal-directed behavior (Hasselmo 2005)
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| 78. |
Reinforcement learning of targeted movement (Chadderdon et al. 2012)
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| 79. |
Reinforcement Learning with Forgetting: Linking Sustained Dopamine to Motivation (Kato Morita 2016)
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| 80. |
Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
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| 81. |
Reward modulated STDP (Legenstein et al. 2008)
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| 82. |
Robust Reservoir Generation by Correlation-Based Learning (Yamazaki & Tanaka 2008)
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| 83. |
Role for short term plasticity and OLM cells in containing spread of excitation (Hummos et al 2014)
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| 84. |
Roles of subthalamic nucleus and DBS in reinforcement conflict-based decision making (Frank 2006)
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| 85. |
Scaling self-organizing maps to model large cortical networks (Bednar et al 2004)
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| 86. |
Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)
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| 87. |
Sequence learning via biophysically realistic learning rules (Cone and Shouval 2021)
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| 88. |
Sequential neuromodulation of Hebbian plasticity in reward-based navigation (Brzosko et al 2017)
|
| 89. |
SHOT-CA3, RO-CA1 Training, & Simulation CODE in models of hippocampal replay (Nicola & Clopath 2019)
|
| 90. |
Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
|
| 91. |
Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)
|
| 92. |
Somatodendritic consistency check for temporal feature segmentation (Asabuki & Fukai 2020)
|
| 93. |
Spatial structure from diffusive synaptic plasticity (Sweeney and Clopath, 2016)
|
| 94. |
Speed/accuracy trade-off between the habitual and the goal-directed processes (Kermati et al. 2011)
|
| 95. |
Spike-timing dependent inhibitory plasticity for gating bAPs (Wilmes et al 2017)
|
| 96. |
Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
|
| 97. |
Spiny Projection Neuron Ca2+ based plasticity is robust to in vivo spike train (Dorman&Blackwell)
|
| 98. |
STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)
|
| 99. |
Striatal dopamine ramping: an explanation by reinforcement learning with decay (Morita & Kato, 2014)
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| 100. |
Supervised learning with predictive coding (Whittington & Bogacz 2017)
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| 101. |
Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013)
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| 102. |
The APP in C-terminal domain alters CA1 neuron firing (Pousinha et al 2019)
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| 103. |
Theta phase precession in a model CA3 place cell (Baker and Olds 2007)
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| 104. |
Tonic activation of extrasynaptic NMDA-R promotes bistability (Gall & Dupont 2020)
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| 105. |
Towards a biologically plausible model of LGN-V1 pathways (Lian et al 2019)
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