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  title = {Neural Networks of the Hippocampus},
  publisher = {Cambridge University Press},
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  author = {R. D. Traub and R. Miles},
  address = {New York}
}

@article{izhikevich2003simple,
  title={Simple model of spiking neurons},
  author={Izhikevich, Eugene M},
  journal={IEEE Transactions on neural networks},
  volume={14},
  number={6},
  pages={1569--1572},
  year={2003}
}

@article{Morrison2008,
    author = {Morrison, Abigail and Diesmann, Markus and Gerstner, Wulfram},
    doi = {10.1007/s00422-008-0233-1},
    isbn = {0340-1200 (Print)$\backslash$r0340-1200 (Linking)},
    issn = {03401200},
    journal = {Biological Cybernetics},
    keywords = {Learning,Modeling,Short term plasticity,Simulation,Spike-timing dependent plasticity},
    pages = {459--478},
    pmid = {18491160},
    title = {{Phenomenological models of synaptic plasticity based on spike timing}},
    volume = {98},
    year = {2008}
}

@article{nowotny2005self,
  title={Self-organization in the olfactory system: one shot odor recognition in insects},
  author={Nowotny, Thomas and Huerta, Ram{\'o}n and Abarbanel, Henry DI and Rabinovich, Mikhail I},
  journal={Biological cybernetics},
  volume={93},
  number={6},
  pages={436--446},
  year={2005},
  publisher={Springer},
	doi={10.1007/s00422-005-0019-7} 

}

@article{Rulkov2002,
  title={Modeling of spiking-bursting neural behavior using two-dimensional map},
  author={Rulkov, Nikolai F},
  journal={Physical Review E},
  volume={65},
  number={4},
  pages={041922},
  year={2002},
  publisher={APS}
}

@INPROCEEDINGS{Nowotny2010,
  author = {T.~Nowotny},
  title = {Parallel implementation of a spiking neuronal network model of unsupervised
	olfactory learning on {NVidia CUDA}},
  booktitle = {IEEE World Congress on Computational Intelligence},
  year = {2010},
  editor = {P. Sobrevilla},
  pages = {3238-3245},
  address = {Barcelona},
  organization = {IEEE}
}

@article {brian2genn2018,
	author = {Stimberg, Marcel and Goodman, Dan F. M. and Nowotny, Thomas},
	title = {Brian2GeNN: a system for accelerating a large variety of spiking neural networks with graphics hardware},
	year = {2018},
	doi = {10.1101/448050},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {"Brian" is a popular Python-based simulator for spiking neural networks, commonly used in computational neuroscience. GeNN is a C++-based meta-compiler for accelerating spiking neural network simulations using consumer or high performance grade graphics processing units (GPUs). Here we introduce a new software package, Brian2GeNN, that connects the two systems so that users can make use of GeNN GPU acceleration when developing their models in Brian, without requiring any technical knowledge about GPUs, C++ or GeNN. The new Brian2GeNN software uses a pipeline of code generation to translate Brian scripts into C++ code that can be used as input to GeNN, and subsequently can be run on suitable NVIDIA GPU accelerators. From the user{\textquoteright}s perspective, the entire pipeline is invoked by adding two simple lines to their Brian scripts. We have shown that using Brian2GeNN, typical models can run tens to hundreds of times faster than on CPU.},
	URL = {https://www.biorxiv.org/content/early/2018/10/20/448050},
	eprint = {https://www.biorxiv.org/content/early/2018/10/20/448050.full.pdf},
	journal = {bioRxiv}
}