Vectorized algorithms for spiking neural network simulation (Brette and Goodman 2011)


Brette R, Goodman DF. (2011). Vectorized algorithms for spiking neural network simulation. Neural computation. 23 [PubMed]

See more from authors: Brette R · Goodman DF

References and models cited by this paper

Bower JM, Beeman D. (1998). The Book Of Genesis: Exploring Realistic Neural Models With The General Neural Simulation System.

Bower JM, Protopapas AD, Vanier M. (1998). Simulating large networks of neurons. Methods in Neuronal Modeling (2nd ed)..

Brette R et al. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. Journal of computational neuroscience. 23 [PubMed]

Cannon RC et al. (2007). Interoperability of neuroscience modeling software: current status and future directions. Neuroinformatics. 5 [PubMed]

D'Haene M, Schrauwen B. (2010). Fast and exact simulation methods applied on a broad range of neuron models. Neural computation. 22 [PubMed]

Davison AP et al. (2008). PyNN: A Common Interface for Neuronal Network Simulators. Frontiers in neuroinformatics. 2 [PubMed]

Deneve S. (2008). Bayesian spiking neurons I: inference. Neural computation. 20 [PubMed]

Destexhe A, Mainen ZF, Sejnowski TJ. (1994). Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. Journal of computational neuroscience. 1 [PubMed]

Diesmann M, Gewaltig M-O. (2007). NEST (Neural Simulation Tool) Scholarpedia. 2

Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig MO. (2008). PyNEST: A Convenient Interface to the NEST Simulator. Frontiers in neuroinformatics. 2 [PubMed]

Garny A et al. (2008). CellML and associated tools and techniques. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences. 366 [PubMed]

Giugliano M. (2000). Synthesis of generalized algorithms for the fast computation of synaptic conductances with Markov kinetic models in large network simulations. Neural computation. 12 [PubMed]

Giugliano M, Bove M, Grattarola M. (1999). Fast calculation of short-term depressing synaptic conductances. Neural computation. 11 [PubMed]

Goddard NH et al. (2001). Towards NeuroML: model description methods for collaborative modelling in neuroscience. Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 356 [PubMed]

Goodman D, Brette R. (2008). Brian: a simulator for spiking neural networks in python. Frontiers in neuroinformatics. 2 [PubMed]

Goodman DF, Brette R. (2009). The brian simulator. Frontiers in neuroscience. 3 [PubMed]

Hanuschkin A, Kunkel S, Helias M, Morrison A, Diesmann M. (2010). A general and efficient method for incorporating precise spike times in globally time-driven simulations. Frontiers in neuroinformatics. 4 [PubMed]

Harris M et al. (2007). A survey of general-purpose computation on graphics hardware Computer Graphics Forum. 26

Hines M. (1984). Efficient computation of branched nerve equations. International journal of bio-medical computing. 15 [PubMed]

Hines ML, Carnevale NT. (2000). Expanding NEURON's repertoire of mechanisms with NMODL. Neural computation. 12 [PubMed]

Hines ML, Carnevale NT. (2006). The NEURON Book.

Hines ML, Davison AP, Muller E. (2009). NEURON and Python. Frontiers in neuroinformatics. 3 [PubMed]

Hirsch MW, Smale S. (1974). Differential Equations, Dynamical Systems and Linear Algebra.

Izhikevich EM. (2006). Polychronization: computation with spikes. Neural computation. 18 [PubMed]

Jahnke A, Roth U, Schnauer T. (1999). Digital simulation of spiking neural networks Pulsed neural networks.

Kistler WM, Gerstner W. (2002). Spiking neuron models.

Köhn J, Wörgötter F. (1998). Employing the zeta-transform to optimize the calculation of the synaptic conductance of NMDA and other synaptic channels in network simulations. Neural computation. 10 [PubMed]

Lansner A, Djurfeldt M. (2007). Workshop report: 1st INCF Workshop on Largescale Modeling of the Nervous System Nature Precedings (http:--dx.doi.org-10.1038-npre.2007.262.1).

Loebel A, Tsodyks M. (2002). Computation by ensemble synchronization in recurrent networks with synaptic depression. Journal of computational neuroscience. 13 [PubMed]

Lytton WW. (1996). Optimizing synaptic conductance calculation for network simulations. Neural computation. 8 [PubMed]

Markram H, Wang Y, Tsodyks M. (1998). Differential signaling via the same axon of neocortical pyramidal neurons. Proceedings of the National Academy of Sciences of the United States of America. 95 [PubMed]

Mongillo G, Barak O, Tsodyks M. (2008). Synaptic theory of working memory. Science (New York, N.Y.). 319 [PubMed]

Morrison A, Aertsen A, Diesmann M. (2007). Spike-timing-dependent plasticity in balanced random networks. Neural computation. 19 [PubMed]

Morrison A, Diesmann M, Gerstner W. (2008). Phenomenological models of synaptic plasticity based on spike timing. Biological cybernetics. 98 [PubMed]

Morrison A, Mehring C, Geisel T, Aertsen AD, Diesmann M. (2005). Advancing the boundaries of high-connectivity network simulation with distributed computing. Neural computation. 17 [PubMed]

Morrison A, Straube S, Plesser HE, Diesmann M. (2007). Exact subthreshold integration with continuous spike times in discrete-time neural network simulations. Neural computation. 19 [PubMed]

Morse T. (2007). Model sharing in computational neuroscience Scholarpedia. 2

Platkiewicz J, Brette R. (2010). A threshold equation for action potential initiation. PLoS computational biology. 6 [PubMed]

Plesser HE, Diesmann M. (2009). Simplicity and efficiency of integrate-and-fire neuron models. Neural computation. 21 [PubMed]

Rossant C, Goodman DF, Platkiewicz J, Brette R. (2010). Automatic fitting of spiking neuron models to electrophysiological recordings. Frontiers in neuroinformatics. 4 [PubMed]

Rotter S, Diesmann M. (1999). Exact digital simulation of time-invariant linear systems with applications to neuronal modeling. Biological cybernetics. 81 [PubMed]

Sanchez-montanez MA. (2001). Strategies for the optimization of large scale networks of integrate and fire neurons.

Schutter ED. (2008). Why are computational neuroscience and systems biology so separate? Plos Comput Bio. 4(5)

Sejnowski TJ, Destexhe A, Mainen Z. (1994). An efficient method for computing synaptic conductances based on a kinetic model of receptor binding Neural Comput. 6

Song S, Miller KD, Abbott LF. (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature neuroscience. 3 [PubMed]

Tsodyks M, Pawelzik K, Markram H. (1998). Neural networks with dynamic synapses. Neural computation. 10 [PubMed]

Tsodyks MV, Markram H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proceedings of the National Academy of Sciences of the United States of America. 94 [PubMed]

References and models that cite this paper

Chavlis S, Petrantonakis PC, Poirazi P. (2017). Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity. Hippocampus. 27 [PubMed]

Danielson NB et al. (2017). In Vivo Imaging of Dentate Gyrus Mossy Cells in Behaving Mice. Neuron. 93 [PubMed]

Goodman DFM, Brette R. (2013). Brian simulator Scholarpedia. 8(1)

Magalhães BRC, Sterling T, Hines M, Schürmann F. (2019). Asynchronous Branch-Parallel Simulation of Detailed Neuron Models. Frontiers in neuroinformatics. 13 [PubMed]

Tomsett RJ et al. (2015). Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue. Brain structure & function. 220 [PubMed]

This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.