| | Models |
| 1. |
A 1000 cell network model for Lateral Amygdala (Kim et al. 2013)
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| 2. |
A basal ganglia model of aberrant learning (Ursino et al. 2018)
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| 3. |
A single-cell spiking model for the origin of grid-cell patterns (D'Albis & Kempter 2017)
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| 4. |
Acetylcholine-modulated plasticity in reward-driven navigation (Zannone et al 2018)
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| 5. |
Adaptive robotic control driven by a versatile spiking cerebellar network (Casellato et al. 2014)
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| 6. |
Biologically-plausible models for spatial navigation (Cannon et al 2003)
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| 7. |
Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)
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| 8. |
Cerebellar memory consolidation model (Yamazaki et al. 2015)
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| 9. |
Computing with neural synchrony (Brette 2012)
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| 10. |
Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)
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| 11. |
Development and Binocular Matching of Orientation Selectivity in Visual Cortex (Xu et al 2020)
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| 12. |
Diffusive homeostasis in a spiking network model (Sweeney et al. 2015)
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| 13. |
Distributed cerebellar plasticity implements adaptable gain control (Garrido et al., 2013)
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| 14. |
Distributed synaptic plasticity and spike timing (Garrido et al. 2013)
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| 15. |
Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)
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| 16. |
Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011)
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| 17. |
Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014)
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| 18. |
Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)
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| 19. |
Fast convergence of cerebellar learning (Luque et al. 2015)
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| 20. |
First-Spike-Based Visual Categorization Using Reward-Modulated STDP (Mozafari et al. 2018)
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| 21. |
FNS spiking neural simulator; LIFL neuron model, event-driven simulation (Susi et al 2021)
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| 22. |
Formation of synfire chains (Jun and Jin 2007)
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| 23. |
Functional balanced networks with synaptic plasticity (Sadeh et al, 2015)
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| 24. |
Hebbian STDP for modelling the emergence of disparity selectivity (Chauhan et al 2018)
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| 25. |
Learning spatial transformations through STDP (Davison, Frégnac 2006)
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| 26. |
Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007)
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| 27. |
Microsaccades and synchrony coding in the retina (Masquelier et al. 2016)
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| 28. |
Modeling dendritic spikes and plasticity (Bono and Clopath 2017)
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| 29. |
Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017)
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| 30. |
Neural model of two-interval discrimination (Machens et al 2005)
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| 31. |
Olfactory bulb mitral and granule cell column formation (Migliore et al. 2007)
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| 32. |
Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
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| 33. |
Parallel odor processing by mitral and middle tufted cells in the OB (Cavarretta et al 2016, 2018)
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| 34. |
Polychronization: Computation With Spikes (Izhikevich 2005)
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| 35. |
Realistic barrel cortical column - Matlab (Huang et al., 2022)
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| 36. |
Realistic barrel cortical column - NetPyNE (Huang et al., 2022)
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| 37. |
Reinforcement learning of targeted movement (Chadderdon et al. 2012)
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| 38. |
Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
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| 39. |
Reward modulated STDP (Legenstein et al. 2008)
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| 40. |
Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)
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| 41. |
Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
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| 42. |
Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)
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| 43. |
Spikes,synchrony,and attentive learning by laminar thalamocort. circuits (Grossberg & Versace 2007)
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| 44. |
Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
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| 45. |
Stability of complex spike timing-dependent plasticity in cerebellar learning (Roberts 2007)
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| 46. |
STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)
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| 47. |
STDP promotes synchrony of inhibitory networks in the presence of heterogeneity (Talathi et al 2008)
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| 48. |
Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013)
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