These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

202 related articles for article (PubMed ID: 31632258)

  • 1. Response Dynamics in an Olivocerebellar Spiking Neural Network With Non-linear Neuron Properties.
    Geminiani A; Pedrocchi A; D'Angelo E; Casellato C
    Front Comput Neurosci; 2019; 13():68. PubMed ID: 31632258
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Complex Electroresponsive Dynamics in Olivocerebellar Neurons Represented With Extended-Generalized Leaky Integrate and Fire Models.
    Geminiani A; Casellato C; D'Angelo E; Pedrocchi A
    Front Comput Neurosci; 2019; 13():35. PubMed ID: 31244635
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network.
    Casali S; Marenzi E; Medini C; Casellato C; D'Angelo E
    Front Neuroinform; 2019; 13():37. PubMed ID: 31156416
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness.
    Geminiani A; Casellato C; Locatelli F; Prestori F; Pedrocchi A; D'Angelo E
    Front Neuroinform; 2018; 12():88. PubMed ID: 30559658
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Adaptive robotic control driven by a versatile spiking cerebellar network.
    Casellato C; Antonietti A; Garrido JA; Carrillo RR; Luque NR; Ros E; Pedrocchi A; D'Angelo E
    PLoS One; 2014; 9(11):e112265. PubMed ID: 25390365
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A Multiple-Plasticity Spiking Neural Network Embedded in a Closed-Loop Control System to Model Cerebellar Pathologies.
    Geminiani A; Casellato C; Antonietti A; D'Angelo E; Pedrocchi A
    Int J Neural Syst; 2018 Jun; 28(5):1750017. PubMed ID: 28264639
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell.
    Marín M; Sáez-Lara MJ; Ros E; Garrido JA
    Front Cell Neurosci; 2020; 14():161. PubMed ID: 32765220
    [TBL] [Abstract][Full Text] [Related]  

  • 8. FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency.
    Susi G; Garcés P; Paracone E; Cristini A; Salerno M; Maestú F; Pereda E
    Sci Rep; 2021 Jun; 11(1):12160. PubMed ID: 34108523
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Cerebellum Involvement in Dystonia During Associative Motor Learning: Insights From a Data-Driven Spiking Network Model.
    Geminiani A; Mockevičius A; D'Angelo E; Casellato C
    Front Syst Neurosci; 2022; 16():919761. PubMed ID: 35782305
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Dynamic Redistribution of Plasticity in a Cerebellar Spiking Neural Network Reproducing an Associative Learning Task Perturbed by TMS.
    Antonietti A; Monaco J; D'Angelo E; Pedrocchi A; Casellato C
    Int J Neural Syst; 2018 Nov; 28(9):1850020. PubMed ID: 29914314
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures.
    Lee C; Sarwar SS; Panda P; Srinivasan G; Roy K
    Front Neurosci; 2020; 14():119. PubMed ID: 32180697
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Model-Driven Analysis of Eyeblink Classical Conditioning Reveals the Underlying Structure of Cerebellar Plasticity and Neuronal Activity.
    Antonietti A; Casellato C; D'Angelo E; Pedrocchi A
    IEEE Trans Neural Netw Learn Syst; 2017 Nov; 28(11):2748-2762. PubMed ID: 27608482
    [TBL] [Abstract][Full Text] [Related]  

  • 13. An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.
    Stromatias E; Soto M; Serrano-Gotarredona T; Linares-Barranco B
    Front Neurosci; 2017; 11():350. PubMed ID: 28701911
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Corrigendum: Complex Electroresponsive Dynamics in Olivocerebellar Neurons Represented With Extended-Generalized Leaky Integrate and Fire Models.
    Geminiani A; Casellato C; D'Angelo E; Pedrocchi A
    Front Comput Neurosci; 2019; 13():48. PubMed ID: 31379546
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Interrelated modification of excitatory and inhibitory connections in the olivocerebellar neural network.
    Sil'kis IG
    Neurosci Behav Physiol; 2001; 31(6):573-81. PubMed ID: 11766893
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.
    Xu T; Xiao N; Zhai X; Kwan Chan P; Tin C
    J Neural Eng; 2018 Feb; 15(1):016021. PubMed ID: 29115280
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Leaky Integrate-and-Fire Neuron Circuit Based on Floating-Gate Integrator.
    Kornijcuk V; Lim H; Seok JY; Kim G; Kim SK; Kim I; Choi BJ; Jeong DS
    Front Neurosci; 2016; 10():212. PubMed ID: 27242416
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Olivocerebellar climbing fibers in the granuloprival cerebellum: morphological study of individual axonal projections in the X-irradiated rat.
    Sugihara I; Bailly Y; Mariani J
    J Neurosci; 2000 May; 20(10):3745-60. PubMed ID: 10804216
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Analog Complementary Metal-Oxide-Semiconductor Integrate-and-Fire Neuron Circuit for Overflow Retaining in Hardware Spiking Neural Networks.
    Hwang S; Lee JJ; Kwon MW; Baek MH; Jang T; Chang J; Lee JH; Park BG
    J Nanosci Nanotechnol; 2020 May; 20(5):3117-3122. PubMed ID: 31635655
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Anterograde tracing of the rat olivocerebellar system with Phaseolus vulgaris leucoagglutinin (PHA-L). Demonstration of climbing fiber collateral innervation of the cerebellar nuclei.
    Van der Want JJ; Wiklund L; Guegan M; Ruigrok T; Voogd J
    J Comp Neurol; 1989 Oct; 288(1):1-18. PubMed ID: 2794133
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.