Add github wiki misc and link in readme

Author: Jaquier Aurélien Tristan

README.rst

@@ -193,6 +193,10 @@ API documentation
 =================
 The API documentation can be found on `ReadTheDocs <http://bluepyopt.readthedocs.io/en/latest/>`_.
 
+Publications that use or mention BluePyOpt
+==========================================
+The list of publications that use or mention BluePyOpt can be found on `the github wiki page <https://github.com/BlueBrain/BluePyOpt/wiki/Publications-that-use-or-mention-BluePyOpt>`_.
+
 Funding
 =======
 This work has been partially funded by the European Union Seventh Framework Program (FP7/2007­2013) under grant agreement no. 604102 (HBP), the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270, 785907 (Human Brain Project SGA1/SGA2) and by the EBRAINS research infrastructure, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).

misc/github_wiki/bibtex/mentions_BPO.bib

@@ -0,0 +1,46 @@
+
+@InProceedings{pmlr-v96-lueckmann19a,
+  title = 	 {Likelihood-free inference with emulator networks},
+  author =       {Lueckmann, Jan-Matthis and Bassetto, Giacomo and Karaletsos, Theofanis and Macke, Jakob H.},
+  booktitle = 	 {Proceedings of The 1st Symposium on Advances in Approximate Bayesian Inference},
+  pages = 	 {32--53},
+  year = 	 {2019},
+  editor = 	 {Ruiz, Francisco and Zhang, Cheng and Liang, Dawen and Bui, Thang},
+  volume = 	 {96},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {02 Dec},
+  publisher =    {PMLR},
+  pdf = 	 {http://proceedings.mlr.press/v96/lueckmann19a/lueckmann19a.pdf},
+  url = 	 {https://proceedings.mlr.press/v96/lueckmann19a.html},
+  abstract = 	 {Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data - both ’local’ emulators which approximate the likelihood for specific observed data, as well as ’global’ ones which are applicable to a range of data. Simulations are chosen adaptively using an acquisition function which takes into account uncertainty about either the posterior distribution of interest, or the parameters of the emulator. Our approach does not rely on user-defined rejection thresholds or distance functions. We illustrate inference with emulator networks on synthetic examples and on a biophysical neuron model, and show that emulators allow accurate and efficient inference even on problems which are challenging for conventional ABC approaches.}
+}
+
+@unpublished{appukuttan:hal-03586825,
+  TITLE = {{A Software Framework for Validating Neuroscience Models}},
+  AUTHOR = {Appukuttan, Shailesh and Sharma, Lungsi and Garcia-Rodriguez, Pedro and Davison, Andrew},
+  URL = {https://hal.archives-ouvertes.fr/hal-03586825},
+  NOTE = {working paper or preprint},
+  YEAR = {2022},
+  MONTH = Feb,
+  PDF = {https://hal.archives-ouvertes.fr/hal-03586825/file/Validation__Methods__Paper___Draft___v1__Reduced_.pdf},
+  HAL_ID = {hal-03586825},
+  HAL_VERSION = {v1},
+}
+
+@misc{https://doi.org/10.48550/arxiv.2203.06391,
+  doi = {10.48550/ARXIV.2203.06391},
+  
+  url = {https://arxiv.org/abs/2203.06391},
+  
+  author = {Jedlicka, Peter and Bird, Alex and Cuntz, Hermann},
+  
+  keywords = {Neurons and Cognition (q-bio.NC), FOS: Biological sciences, FOS: Biological sciences},
+  
+  title = {Pareto optimality, economy-effectiveness trade-offs and ion channel degeneracy: Improving population models of neurons},
+  
+  publisher = {arXiv},
+  
+  year = {2022},
+  
+  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}
+}

misc/github_wiki/bibtex/mentions_BPO_extra.bib

@@ -0,0 +1,40 @@
+@inproceedings{damartDataDrivenBuilding2020,
+  title = {Data Driven Building of Realistic Neuron Model Using {{IBEA}} and {{CMA}} Evolution Strategies},
+  booktitle = {Proceedings of the 2020 {{Genetic}} and {{Evolutionary Computation Conference Companion}}},
+  author = {Damart, Tanguy and Van Geit, Werner and Markram, Henry},
+  year = {2020},
+  month = jul,
+  pages = {35--36},
+  publisher = {{ACM}},
+  address = {{Canc\'un Mexico}},
+  doi = {10.1145/3377929.3398161},
+  isbn = {978-1-4503-7127-8},
+  langid = {english}
+}
+
+@misc{rizzaRealisticModel,
+  title = {A Realistic Model of Cerebellar Stellate Neurons Predicts Intrinsic Excitability and the Impact of Synaptic Inputs},
+  author = {Rizza, Martina Francesca and Locatelli, Francesca and Masoli, Stefano and Prestori, Francesca and Sanchez-Ponce, Diana and Munoz, Alberto and D‘Angelo, Egidio},
+  year = {2018},
+  howpublished = {https://www.researchgate.net/profile/Joseph-Davids/publication/336990052_Artificial_nano-intelligence_Using_deep_learning_models_to_study_the_formation_of_gold_nanoparticles/links/5dbdd4194585151435e24dab/Artificial-nano-intelligence-Using-deep-learning-models-to-study-the-formation-of-gold-nanoparticles.pdf#page=98}
+},
+
+@misc{nylenReconstructingStriatal,
+  title = {Reconstructing the striatal microcircuit in silico},
+  author = {Nylén, Johanna Frost and Hjorth, Johannes and Kozlov, Alexander and Lindroos, Robert and Carannante, Ilaria and Suryanarayana, Shreyas M. and Silberberg, Gilad and Kotaleski, Jeanette Hellgren and Grillner, Sten},
+  year = {2018},
+  howpublished = {https://www.researchgate.net/profile/Joseph-Davids/publication/336990052_Artificial_nano-intelligence_Using_deep_learning_models_to_study_the_formation_of_gold_nanoparticles/links/5dbdd4194585151435e24dab/Artificial-nano-intelligence-Using-deep-learning-models-to-study-the-formation-of-gold-nanoparticles.pdf#page=72}
+},
+
+@misc{tognolinaModeling,
+  title = {Modeling optimization procedures predict specific filtering channels in the cerebellar granule cell layer},
+  author = {Tognolina, Marialuisa and Masoli, Stefano and Moccia, Francesco and D‘Angelo, Egidio},
+  year = {2018},
+  howpublished = {https://www.researchgate.net/profile/Joseph-Davids/publication/336990052_Artificial_nano-intelligence_Using_deep_learning_models_to_study_the_formation_of_gold_nanoparticles/links/5dbdd4194585151435e24dab/Artificial-nano-intelligence-Using-deep-learning-models-to-study-the-formation-of-gold-nanoparticles.pdf#page=86}
+},
+
+@misc{shieldsOptimizedFitting,
+  title = {Optimized Fitting of a Stochastic Auditory Nerve Fiber Model to Patch-Clamp Data},
+  author = {Shields, Daniel and Rutherford, Mark A. and Bruce, Ian C.},
+  howpublished = {https://vepimg.b8cdn.com/uploads/vjfnew/content/files/16258518561401-poster-pdf1625851856.pdf}
+}