Simulation, visualization and analysis tools for pattern recognition assessment with spiking neuronal networks
暂无分享,去创建一个
Eduardo Ros | Pablo Toharia | Luis Pastor | Jesús Alberto Garrido | Sergio E. Galindo | Oscar David Robles | L. Pastor | E. Ros | J. Garrido | Pablo Toharia | O. D. Robles | S. Galindo
[1] Arne Møller,et al. Total numbers of various cell types in rat cerebellar cortex estimated using an unbiased stereological method , 1993, Brain Research.
[2] M. Zoli,et al. Heterosynaptic GABAergic plasticity bidirectionally driven by the activity of pre- and postsynaptic NMDA receptors , 2016, Proceedings of the National Academy of Sciences.
[3] Timothée Masquelier,et al. Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity , 2007, PLoS Comput. Biol..
[4] Martina Sgritta,et al. Hebbian Spike-Timing Dependent Plasticity at the Cerebellar Input Stage , 2017, The Journal of Neuroscience.
[5] J Szentágothai,et al. Dynamic single unit simulation of a realistic cerebellar network model. , 1973, Brain research.
[6] Torsten Kuhlen,et al. VisNEST — Interactive analysis of neural activity data , 2013, 2013 IEEE Symposium on Biological Data Visualization (BioVis).
[7] Chao Huang,et al. Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding , 2016, PLoS Comput. Biol..
[8] Thomas Wachtler,et al. Integrated platform and API for electrophysiological data , 2014, Front. Neuroinform..
[9] Yi Dong,et al. Optimization Methods for Spiking Neurons and Networks , 2010, IEEE Transactions on Neural Networks.
[10] Pablo Toharia,et al. ViSimpl: Multi-View Visual Analysis of Brain Simulation Data , 2016, Front. Neuroinform..
[11] J. Hámori,et al. Quantitative morphology and synaptology of cerebellar glomeruli in the rat , 1988, Anatomy and Embryology.
[12] Sonja Grün,et al. A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC , 2016, JHPCS.
[13] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[14] Garth Paine,et al. Oscillations , 2016, CHI Extended Abstracts.
[15] Eduardo Ros,et al. Spike Timing Regulation on the Millisecond Scale by Distributed Synaptic Plasticity at the Cerebellum Input Stage: A Simulation Study , 2013, Front. Comput. Neurosci..
[16] Andrzej J. Kasinski,et al. 'SNN3DViewer' - 3D Visualization Tool for Spiking Neural Network Analysis , 2008, ICCVG.
[17] Jeffrey L. Krichmar,et al. Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types , 2018, Front. Neuroinform..
[18] Henry Markram,et al. BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience , 2016, Front. Neuroinform..
[19] E. D’Angelo,et al. Theta-Frequency Resonance at the Cerebellum Input Stage Improves Spike Timing on the Millisecond Time-Scale , 2013, Front. Neural Circuits.
[20] Silvia Tolu,et al. Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks , 2016, Int. J. Neural Syst..
[21] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[22] Timothée Masquelier,et al. Competitive STDP-Based Spike Pattern Learning , 2009, Neural Computation.
[23] N. Alex Cayco-Gajic,et al. Re-evaluating Circuit Mechanisms Underlying Pattern Separation , 2019, Neuron.
[25] G. Bi,et al. Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.
[26] Robert C. Froemke,et al. Inhibitory and Excitatory Spike-Timing-Dependent Plasticity in the Auditory Cortex , 2015, Neuron.
[27] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[28] Egidio D'Angelo,et al. Fast-Reset of Pacemaking and Theta-Frequency Resonance Patterns in Cerebellar Golgi Cells: Simulations of their Impact In Vivo , 2007, Frontiers in cellular neuroscience.
[29] Alexander Peyser,et al. Integrating Visualizations into Modeling NEST Simulations , 2015, Front. Neuroinform..
[30] Nikola Kasabov,et al. Spiking neural networks for deep learning and knowledge representation: Editorial , 2019, Neural Networks.
[31] Gustavo Deco,et al. Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme , 2009, The Journal of Neuroscience.
[32] Marc Parizeau,et al. DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..
[33] H. Abarbanel,et al. Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. , 2006, Journal of neurophysiology.
[34] E. D’Angelo. The human brain project. , 2012, Functional neurology.