Publications

Dumont, G., Payeur , A. & Longtin , A. (2017). A stochastic-field description of finite-size spiking neural networks. PLoS Comput Biol, 13(8), e1005691. doi:10.1371/journal.pcbi.1005691

Mcdonnell, M., Iannella, N., To, M., Tuckwell, H., Jost, J., Gutkin, B. & Ward, L. (2015). A review of methods for identifying stochastic resonance in simulations of single neuron models. Network: Computation in Neural Systems, 26(2), 35-71. doi:10.3109/0954898X.2014.990064

Keramati, M. & Gutkin, B. (2011). A Reinforcement Learning Theory for Homeostatic Regulation. NIPS, .

Oster, A. & Gutkin, B. (2011). A reduced model of DA neuronal dynamics that displays quiescence, tonic firing and bursting. Journal of Physiology Paris, 105, 53-58. doi:10.1016/j.jphysparis.2011.07.012

Gutkin, B., Dehaene, S. & Changeux, J. (2006). A neurocomputational hypothesis for nicotine addiction. Proceedings of the National Academy of Sciences of the United States of America, 103(4), 1106-1111. doi:10.1073/pnas.0510220103

Dumont, G., Pérez-Cervera, A. & Gutkin, B. (2022). A framework for macroscopic phase-resetting curves for generalised spiking neural networks. Plos Computational Biology. doi:10.1371/journal.pcbi.1010363

Caze, R., Humphries, M., Gutkin, B. & Schultz, S. (2013). A difficult classification for neurons without dendrites. In Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on, San Diego, CA, USA, IEEE, 215-218. doi:10.1109/NER.2013.6695910

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2017). A bright future for financial agent-based models. arXiv preprint arXiv:1801.08222

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2018). A bright future for financial agent-based models. arxiv, nc