Models that contain the Model Concept : Learning

(The ability of a neural network to change over time, or trials, its output in response to a set of, or a repetition of inputs.)
Re-display model names with descriptions
    Models
1. 3D model of the olfactory bulb (Migliore et al. 2014)
2. 3D olfactory bulb: operators (Migliore et al, 2015)
3. A 1000 cell network model for Lateral Amygdala (Kim et al. 2013)
4. A large-scale model of the functioning brain (spaun) (Eliasmith et al. 2012)
5. A model of antennal lobe of bee (Chen JY et al. 2015)
6. A Model of Selection between Stimulus and Place Strategy in a Hawkmoth (Balkenius et al. 2004)
7. A neurocomputational model of classical conditioning phenomena (Moustafa et al. 2009)
8. A reinforcement learning example (Sutton and Barto 1998)
9. A simple model of neuromodulatory state-dependent synaptic plasticity (Pedrosa and Clopath, 2016)
10. A spiking neural network model of model-free reinforcement learning (Nakano et al 2015)
11. Adaptation of Short-Term Plasticity parameters (Esposito et al. 2015)
12. Adaptive robotic control driven by a versatile spiking cerebellar network (Casellato et al. 2014)
13. Alternative time representation in dopamine models (Rivest et al. 2009)
14. Behavioral time scale synaptic plasticity underlies CA1 place fields (Bittner et al. 2017)
15. CA1 pyramidal neurons: binding properties and the magical number 7 (Migliore et al. 2008)
16. Calcium response prediction in the striatal spines depending on input timing (Nakano et al. 2013)
17. Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)
18. Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012)
19. Cerebellar memory consolidation model (Yamazaki et al. 2015)
20. Cognitive and motor cortico-basal ganglia interactions during decision making (Guthrie et al 2013)
21. Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)
22. Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)
23. Cortico-striatal plasticity in medium spiny neurons (Gurney et al 2015)
24. Democratic population decisions result in robust policy-gradient learning (Richmond et al. 2011)
25. Development of modular activity of grid cells (Urdapilleta et al 2017)
26. Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)
27. Dynamic dopamine modulation in the basal ganglia: Learning in Parkinson (Frank et al 2004,2005)
28. Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)
29. Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)
30. Fixed point attractor (Hasselmo et al 1995)
31. FRAT: An amygdala-centered model of fear conditioning (Krasne et al. 2011)
32. Functional balanced networks with synaptic plasticity (Sadeh et al, 2015)
33. Hebbian learning in a random network for PFC modeling (Lindsay, et al. 2017)
34. Hippocampal context-dependent retrieval (Hasselmo and Eichenbaum 2005)
35. Large scale model of the olfactory bulb (Yu et al., 2013)
36. Learning spatial transformations through STDP (Davison, Frégnac 2006)
37. Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007)
38. Long time windows from theta modulated inhib. in entorhinal–hippo. loop (Cutsuridis & Poirazi 2015)
39. Mapping function onto neuronal morphology (Stiefel and Sejnowski 2007)
40. Model of cerebellar parallel fiber-Purkinje cell LTD and LTP (Gallimore et al 2018)
41. Model of DARPP-32 phosphorylation in striatal medium spiny neurons (Lindskog et al. 2006)
42. Modeling hebbian and homeostatic plasticity (Toyoizumi et al. 2014)
43. Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017)
44. Multimodal stimuli learning in hawkmoths (Balkenius et al. 2008)
45. Neuronify: An Educational Simulator for Neural Circuits (Dragly et al 2017)
46. Odor supported place cell model and goal navigation in rodents (Kulvicius et al. 2008)
47. Olfactory bulb mitral and granule cell column formation (Migliore et al. 2007)
48. Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)
49. Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
50. Prefrontal cortical mechanisms for goal-directed behavior (Hasselmo 2005)
51. Reinforcement learning of targeted movement (Chadderdon et al. 2012)
52. Reinforcement Learning with Forgetting: Linking Sustained Dopamine to Motivation (Kato Morita 2016)
53. Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
54. Reward modulated STDP (Legenstein et al. 2008)
55. Robust Reservoir Generation by Correlation-Based Learning (Yamazaki & Tanaka 2008)
56. Role for short term plasticity and OLM cells in containing spread of excitation (Hummos et al 2014)
57. Roles of subthalamic nucleus and DBS in reinforcement conflict-based decision making (Frank 2006)
58. Scaling self-organizing maps to model large cortical networks (Bednar et al 2004)
59. Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)
60. Sequential neuromodulation of Hebbian plasticity in reward-based navigation (Brzosko et al 2017)
61. Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
62. Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)
63. Spatial structure from diffusive synaptic plasticity (Sweeney and Clopath, 2016)
64. Speed/accuracy trade-off between the habitual and the goal-directed processes (Kermati et al. 2011)
65. Spike-timing dependent inhibitory plasticity for gating bAPs (Wilmes et al 2017)
66. Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
67. STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)
68. Striatal dopamine ramping: an explanation by reinforcement learning with decay (Morita & Kato, 2014)
69. Supervised learning with predictive coding (Whittington & Bogacz 2017)
70. Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013)
71. Theta phase precession in a model CA3 place cell (Baker and Olds 2007)

Re-display model names with descriptions