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Publications

Other  

Tallon-Baudry, C. (2013). Catherine Tallon-Baudry. Current Biology, 23(14), R588-R590. doi:10.1016/j.cub.2013.06.010

Other  

Lebreton, M. & Palminteri, S. (2016). When are inter-individual brain-behavior correlations informative? bioRxiv. doi:10.1101/036772

Other  

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

Other  

Recanatesi, S., Farrell, M., Lajoie, G., Denève, S., Rigotti, M. & Shea-Brown, E. (2018). Signatures and mechanisms of low-dimensional neural predictive manifolds. bioRxiv. doi:10.1101/471987

Other  
Other  

Martinez-Saito, M. , Konovalov, R. , Piradov, M. , Shestakova, A. , Gutkin, B. & Klucharev, V. (2018). Action in auctions: neural and computational mechanisms of bidding behavior. BioRxiv, 464925. doi:10.1101/464925

Other  

Lazarevich, I. , Gutkin, B. & Prokin, I. (2018). Neural activity classification with machine learning models trained on interspike interval series data. arxiv , 1810.03855

Other  

Ting, C. , Palminteri, S., Engelmann, J. & Lebreton, M. (2019). Decreased confidence in loss-avoidance contexts is a primary meta-cognitive bias of human reinforcement learning. bioRxiv. doi:10.1101/593368

Book chapter  

Gutkin, B. & Stiefel, K. (2007). Phase-resetting curves and neuromodulation of action potential dynamics in the cortex. (Vol. 40, pp. 14-15).

Book chapter  

Graupner, M. & Gutkin, B. (2012). Dynamical Approaches to understanding cholinergic control of nicotine action pathways in the dopaminergic reward circuits. Computational Neuroscience of Drug Addiction (Springer ed.).Ahmed and Gutkin (eds.)

Book chapter  

Caze, R., Humphries, M. & Gutkin, B. (2013). Dendrites enhance both single neuron and network computation. In Remme et al (eds) (Eds.), Dendritic ComputationSpringer

Book chapter  

Remme, M., Lengyel, M. & Gutkin, B. (2014). Phase Response Methods in Dendritic Dynamics. In Schultheiss et al (eds) (Eds.), Phase Response Cruves in NeuroscienceSpringer

Book chapter  

Remme, M., Lengyel, M. & Gutkin, B. (2015). Trade-off between dendritic democracy and independence in neurons with intrinsic subthreshold membrane potential oscillatio. In Remme et al (eds) (Eds.), Dendritic ComputationSpringer

Book chapter  

Kuznetsov, A. & Gutkin, B. (2015). Dopaminergic cell Models. The Encyclopedia of Computational Neuroscience (pp. 2958-2965).

Book chapter  

Gutkin, B. (2015). Theta-neurons. In Springer Verlag (Eds.), Encyclopedia of Comptutational Neuroscience (pp. 1034-1042).

Book chapter  

Dumont, G., Maex, R. & Gutkin, B. (2018). Dopaminergic Neurons in the Ventral Tegmental Area and Their Dysregulation in Nicotine Addiction. In Alan Anticevic and John D. Murray (Eds.), Computational Psychiatry: Mathematical Modeling of Mental Illness (pp. 47-84). doi:10.1016/B978-0-12-809825-7.00003-1

Book chapter  

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2021). Learning and Cognition in Financial Markets: A Paradigm Shift for Agent-Based Models. Advances in Intelligent Systems and Computing (Vol. 1252, pp. 241-255). doi:10.1007/978-3-030-55190-2_19

Reviewed conference proceeding  

Caze, R., Humphries, M. & Gutkin, B. (2012). Spiking and saturating dendrites differentially expand single neuron computation capacity. , Vol. 13: In Twenty First Annual Computational Neuroscience Meeting: CNS*2012, Decatur, GA, USA.

Reviewed conference proceeding  

Alemi, A., Machens, C., Denève, S. & Slotine, J. (2018). Learning nonlinear dynamics in efficient, balanced spiking networks using local plasticity rules. In Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, AAAI Press.