| | Models |
| 1. |
A computational model of oxytocin modulation of olfactory recognition memory (Linster & Kelsch 2019)
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| 2. |
A computational model of systems memory consolidation and reconsolidation (Helfer & Shultz 2019)
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| 3. |
A detailed data-driven network model of prefrontal cortex (Hass et al 2016)
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| 4. |
A dynamical model of the basal ganglia (Leblois et al 2006)
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| 5. |
A model of antennal lobe of bee (Chen JY et al. 2015)
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| 6. |
A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011)
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| 7. |
A NN with synaptic depression for testing the effects of connectivity on dynamics (Jacob et al 2019)
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| 8. |
A unified thalamic model of multiple distinct oscillations (Li, Henriquez and Fröhlich 2017)
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| 9. |
An oscillatory neural model of multiple object tracking (Kazanovich and Borisyuk 2006)
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| 10. |
Basis for temporal filters in the cerebellar granular layer (Roessert et al. 2015)
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| 11. |
Biologically Constrained Basal Ganglia model (BCBG model) (Lienard, Girard 2014)
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| 12. |
Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)
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| 13. |
Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012)
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| 14. |
Cerebellar memory consolidation model (Yamazaki et al. 2015)
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| 15. |
Cerebellar Model for the Optokinetic Response (Kim and Lim 2021)
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| 16. |
Coding of stimulus frequency by latency in thalamic networks (Golomb et al 2005)
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| 17. |
Competition model of pheromone ratio detection (Zavada et al. 2011)
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| 18. |
Conductance-based model of Layer-4 in the barrel cortex (Argaman et Golomb 2017)
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| 19. |
Connection-set Algebra (CSA) for the representation of connectivity in NN models (Djurfeldt 2012)
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| 20. |
COREM: configurable retina simulator (Martínez-Cañada et al., 2016)
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| 21. |
Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)
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| 22. |
Default mode network model (Matsui et al 2014)
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| 23. |
Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)
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| 24. |
Distributed cerebellar plasticity implements adaptable gain control (Garrido et al., 2013)
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| 25. |
Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009)
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| 26. |
Dynamics in random NNs with multiple neuron subtypes (Pena et al 2018, Tomov et al 2014, 2016)
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| 27. |
Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014)
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| 28. |
Formation of synfire chains (Jun and Jin 2007)
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| 29. |
Generating oscillatory bursts from a network of regular spiking neurons (Shao et al. 2009)
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| 30. |
Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018)
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| 31. |
Huntington`s disease model (Gambazzi et al. 2010)
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| 32. |
Inhibition and glial-K+ interaction leads to diverse seizure transition modes (Ho & Truccolo 2016)
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| 33. |
L4 cortical barrel NN model receiving thalamic input during whisking or touch (Gutnisky et al. 2017)
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| 34. |
Large cortex model with map-based neurons (Rulkov et al 2004)
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| 35. |
Linking dynamics of the inhibitory network to the input structure (Komarov & Bazhenov 2016)
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| 36. |
Mechanisms of very fast oscillations in axon networks coupled by gap junctions (Munro, Borgers 2010)
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| 37. |
Microsaccades and synchrony coding in the retina (Masquelier et al. 2016)
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| 38. |
Model of memory linking through memory allocation (Kastellakis et al. 2016)
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| 39. |
Models for cortical UP-DOWN states in a bistable inhibitory-stabilized network (Jercog et al 2017)
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| 40. |
NETMORPH: creates NNs with realistic neuron morphologies (Koene et al. 2009, van Ooyen et al. 2014)
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| 41. |
Network model with neocortical architecture (Anderson et al 2007,2012; Azhar et al 2012)
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| 42. |
Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)
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| 43. |
Neural mass model based on single cell dynamics to model pathophysiology (Zandt et al 2014)
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| 44. |
Neural modeling of an internal clock (Yamazaki and Tanaka 2008)
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| 45. |
NMDAR & GABAB/KIR Give Bistable Dendrites: Working Memory & Sequence Readout (Sanders et al., 2013)
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| 46. |
Norns - Neural Network Studio (Visser & Van Gils 2014)
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| 47. |
Numerical Integration of Izhikevich and HH model neurons (Stewart and Bair 2009)
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| 48. |
Olfactory bulb network model of gamma oscillations (Bathellier et al. 2006; Lagier et al. 2007)
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| 49. |
Optimal deep brain stimulation of the subthalamic nucleus-a computational study (Feng et al. 2007)
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| 50. |
Perceptual judgments via sensory-motor interaction assisted by cortical GABA (Hoshino et al 2018)
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| 51. |
Persistent synchronized bursting activity in cortical tissues (Golomb et al 2005)
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| 52. |
Perturbation sensitivity implies high noise and suggests rate coding in cortex (London et al. 2010)
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| 53. |
Rate model of a cortical RS-FS-LTS network (Hayut et al. 2011)
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| 54. |
Reducing variability in motor cortex activity by GABA (Hoshino et al. 2019)
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| 55. |
Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
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| 56. |
Robust Reservoir Generation by Correlation-Based Learning (Yamazaki & Tanaka 2008)
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| 57. |
Sleep-wake transitions in corticothalamic system (Bazhenov et al 2002)
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| 58. |
Spinal circuits controlling limb coordination and gaits in quadrupeds (Danner et al 2017)
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| 59. |
STDP promotes synchrony of inhibitory networks in the presence of heterogeneity (Talathi et al 2008)
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| 60. |
Stochastic and periodic inputs tune ongoing oscillations (Hutt et al. 2016)
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| 61. |
Studies of stimulus parameters for seizure disruption using NN simulations (Anderson et al. 2007)
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| 62. |
Temporal integration by stochastic recurrent network (Okamoto et al. 2007)
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