K. Oh and K. Jung, GPU implementation of neural networks, Pattern Recognit, vol.37, p.13, 2004.

T. Rolfes, Neural networks on programmable graphics hardware, 2004.

. Nvidia-®-corporation and . Cuda-?, , 2006.

J. M. Nageswaran, N. Dutt, J. L. Krichmar, A. Nicolau, and A. V. Veidenbaum, A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors, Neural Networks, vol.22, pp.791-800, 2009.

A. Fidjeland and M. Shanahan, Accelerated simulation of spiking neural networks using GPUs, The 2010 International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2010.

J. Mutch, U. Knoblich, and T. Poggio, CNS: a GPU-based framework for simulating cortically-organized networks, Comput. Sci. Artif. Intell. Lab. Tech. Rep, 2010.

R. V. Hoang, D. Tanna, L. C. Bray, S. M. Dascalu, and F. C. Harris, A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling, Front. Neuroinformatics, vol.7, 2013.

T. Bekolay, Nengo: a Python tool for building large-scale functional brain models, Front. Neuroinformatics, vol.7, 2014.

E. Yavuz, J. Turner, and T. Nowotny, GeNN: A code generation framework for accelerated brain simulations, Sci. Rep, vol.6, 2016.

D. F. Goodman, Code Generation: A Strategy for Neural Network Simulators, Neuroinformatics, vol.8, pp.183-196, 2010.

I. Blundell, Code Generation in Computational Neuroscience: A Review of Tools and Techniques, Front. Neuroinformatics, vol.12, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01996352

J. C. Knight and T. Nowotny, GPUs outperform current HPC and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model, Front. Neurosci, vol.12, 2018.

M. Augustin, D. Alevi, M. Stimberg, and K. Obermayer, Flexible simulation of neuronal network models on graphics processing units: an efficient code generation approach based on Brian, Bernstein Conference, 2018.

J. Vitay, H. Ü. Dinkelbach, and F. H. Hamker, ANNarchy: a code generation approach to neural simulations on parallel hardware, Front. Neuroinformatics, vol.9, 2015.

D. Goodman and R. Brette, Brian: a simulator for spiking neural networks in python, Front. Neuroinformatics, vol.2, 2008.

D. F. Goodman and R. Brette, The Brian simulator, Front. Neurosci, vol.3, pp.192-197, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02325277

D. F. Goodman, . Brette, and . Brian-simulator, , vol.8, p.10883, 2013.

M. Stimberg, R. Brette, and D. F. Goodman, Brian 2, an intuitive and efficient neural simulator. eLife 8, e47314, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02325389

M. Stimberg, D. F. Goodman, V. Benichoux, and R. Brette, Equation-oriented specification of neural models for simulations, Front. Neuroinformatics, vol.8, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01686592

R. Brette, Simulation of networks of spiking neurons: A review of tools and strategies, J. Comput. Neurosci, vol.23, pp.349-398, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00180662

R. D. Traub and R. Miles, Neural Networks of the Hippocampus, 1991.

T. Nowotny, R. Huerta, H. D. Abarbanel, and M. I. Rabinovich, Self-organization in the olfactory system: Rapid odor recognition in insects, Biol Cybern, vol.93, pp.436-446, 2005.

S. J. Albada, Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model, Front. Neurosci, vol.12, p.291, 2018.

R. Brette and D. F. Goodman, Simulating spiking neural networks on GPU, vol.23, p.730170, 2012.

M. Stimberg, D. F. Goodman, and R. Brette, , 2018.

J. Knight, E. Yavuz, J. Turner, T. Nowotny, and . Genn, , 2018.

M. Stimberg, T. Nowotny, D. F. Goodman, and . Brian2genn, , 2018.