Firing-rate models for neurons with a broad repertoire of spiking behaviors
暂无分享,去创建一个
Gaute T. Einevoll | Tom Tetzlaff | Hans Ekkehard Plesser | Birgit Kriener | Thomas Heiberg | G. Einevoll | T. Tetzlaff | H. Plesser | B. Kriener | Thomas Heiberg
[1] R. Stein. A THEORETICAL ANALYSIS OF NEURONAL VARIABILITY. , 1965, Biophysical journal.
[2] Gaute T. Einevoll,et al. Firing-rate models capture essential response dynamics of LGN relay cells , 2013, Journal of Computational Neuroscience.
[3] Wulfram Gerstner,et al. The quantitative single-neuron modeling competition , 2008, Biological Cybernetics.
[4] Ben H. Jansen,et al. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns , 1995, Biological Cybernetics.
[5] M. Carandini,et al. Inhibition dominates sensory responses in awake cortex , 2012, Nature.
[6] T. Sejnowski,et al. [Letters to nature] , 1996, Nature.
[7] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[8] Henry C. Tuckwell,et al. Introduction to theoretical neurobiology , 1988 .
[9] Mark A. Kramer,et al. Capturing Spike Variability in Noisy Izhikevich Neurons Using Point Process Generalized Linear Models , 2018, Neural Computation.
[10] Eero P. Simoncelli,et al. Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model , 2004, Neural Computation.
[11] Stefan Rotter,et al. Exact digital simulation of time-invariant linear systems with applications to neuronal modeling , 1999, Biological Cybernetics.
[12] Shigeru Shinomoto,et al. Kernel bandwidth optimization in spike rate estimation , 2009, Journal of Computational Neuroscience.
[13] Sen Song,et al. Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits , 2005, PLoS biology.
[14] Eric Jones,et al. SciPy: Open Source Scientific Tools for Python , 2001 .
[15] B. Mandelbrot,et al. RANDOM WALK MODELS FOR THE SPIKE ACTIVITY OF A SINGLE NEURON. , 1964, Biophysical journal.
[16] B. Ermentrout. Neural networks as spatio-temporal pattern-forming systems , 1998 .
[17] Marc-Oliver Gewaltig,et al. NEST (NEural Simulation Tool) , 2007, Scholarpedia.
[18] Pierre Yger,et al. NeuralEnsemble.Org: Unifying neural simulators in Python to ease the model complexity bottleneck , 1970 .
[19] Wulfram Gerstner,et al. Spiking Neuron Models , 2002 .
[20] Frances S. Chance,et al. Effects of synaptic noise and filtering on the frequency response of spiking neurons. , 2001, Physical review letters.
[21] Shigeru Shinomoto,et al. Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times , 2011, Front. Comput. Neurosci..
[22] C. Petersen,et al. The Excitatory Neuronal Network of the C2 Barrel Column in Mouse Primary Somatosensory Cortex , 2009, Neuron.
[23] Nicolas Brunel,et al. Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons , 2000, Journal of Computational Neuroscience.
[24] Eugene M Izhikevich,et al. Hybrid spiking models , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[25] Stephen Wolfram,et al. The Mathematica Book , 1996 .
[26] Anthony N. Burkitt,et al. A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input , 2006, Biological Cybernetics.
[27] Rupert Swarbrick,et al. Firing-rate response of a neuron receiving excitatory and inhibitory synaptic shot noise. , 2010, Physical review letters.
[28] Nicolas Brunel,et al. From Spiking Neuron Models to Linear-Nonlinear Models , 2011, PLoS Comput. Biol..
[29] H. Gurden,et al. Alteration of sensory-evoked metabolic and oscillatory activities in the olfactory bulb of GLAST-deficient mice , 2012, Front. Neural Circuits.
[30] N. Matsuki,et al. Interpyramid spike transmission stabilizes the sparseness of recurrent network activity. , 2013, Cerebral cortex.
[31] E J Chichilnisky,et al. Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model , 2005, The Journal of Neuroscience.
[32] Nicholas J. Higham,et al. The Scaling and Squaring Method for the Matrix Exponential Revisited , 2005, SIAM J. Matrix Anal. Appl..
[33] P. Johannesma,et al. Diffusion Models for the Stochastic Activity of Neurons , 1968 .
[34] Markus Diesmann,et al. Exact Subthreshold Integration with Continuous Spike Times in Discrete-Time Neural Network Simulations , 2007, Neural Computation.
[35] H. Markram,et al. Interneurons of the neocortical inhibitory system , 2004, Nature Reviews Neuroscience.
[36] K. Funke,et al. On the significance of temporally structured activity in the dorsal lateral geniculate nucleus (LGN) , 1997, Progress in Neurobiology.
[37] Sylvain Crochet,et al. Synaptic Computation and Sensory Processing in Neocortical Layer 2/3 , 2013, Neuron.
[38] Eugene M. Izhikevich,et al. Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.
[39] Gaute T. Einevoll,et al. Frontiers in Computational Neuroscience , 2022 .
[40] Shigeru Shinomoto,et al. Made-to-Order Spiking Neuron Model Equipped with a Multi-Timescale Adaptive Threshold , 2009, Front. Comput. Neurosci..
[41] L. Bécu,et al. Evidence for three-dimensional unstable flows in shear-banding wormlike micelles. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.
[42] Youping Xiao,et al. A simple model of retina-LGN transmission , 2008, Journal of Computational Neuroscience.
[43] Tomoki Fukai,et al. Spiking network simulation code for petascale computers , 2014, Front. Neuroinform..
[44] W. Gerstner,et al. Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex. , 2012, Journal of neurophysiology.
[45] Alex Roxin,et al. The Role of Degree Distribution in Shaping the Dynamics in Networks of Sparsely Connected Spiking Neurons , 2011, Front. Comput. Neurosci..
[46] Stephen Coombes,et al. Waves, bumps, and patterns in neural field theories , 2005, Biological Cybernetics.
[47] Wulfram Gerstner,et al. Predicting spike timing of neocortical pyramidal neurons by simple threshold models , 2006, Journal of Computational Neuroscience.
[48] C. Morris,et al. Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.
[49] J. Cowan,et al. Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.
[50] Anders M. Dale,et al. Estimation of Thalamocortical and Intracortical Network Models from Joint Thalamic Single-Electrode and Cortical Laminar-Electrode Recordings in the Rat Barrel System , 2009, PLoS Comput. Biol..
[51] Wulfram Gerstner,et al. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. , 2005, Journal of neurophysiology.
[52] R. FitzHugh. Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.
[53] A. Hodgkin,et al. A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.
[54] Frances S. Chance,et al. Gain Modulation from Background Synaptic Input , 2002, Neuron.
[55] Jonathan W. Pillow,et al. Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models , 2016, Neural Computation.
[56] Gaute T. Einevoll,et al. Neural network firing-rate models on integral form , 2007, Biological Cybernetics.
[57] Awad H. Al-Mohy,et al. A New Scaling and Squaring Algorithm for the Matrix Exponential , 2009, SIAM J. Matrix Anal. Appl..
[58] Eugene M. Izhikevich,et al. Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.
[59] Kenneth D. Harris,et al. Laminar-dependent effects of cortical state on auditory cortical spontaneous activity , 2012, Front. Neural Circuits.
[60] Anthony N. Burkitt,et al. A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties , 2006, Biological Cybernetics.
[61] Tomoki Fukai,et al. Supercomputers Ready for Use as Discovery Machines for Neuroscience , 2012, Front. Neuroinform..
[62] Markus Diesmann,et al. Simplicity and Efficiency of Integrate-and-Fire Neuron Models , 2009, Neural Computation.
[63] M. J. Richardson,et al. Firing-rate response of linear and nonlinear integrate-and-fire neurons to modulated current-based and conductance-based synaptic drive. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.