Publications

Lussange, J., Vrizzi, S., Bourgeois-Gironde, S., Palminteri, S. & Gutkin, B. (2022). Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model. Comput Econ. doi:10.1007/s10614-022-10249-3

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

Gurnani, H. & Cayco Gajic, A. (2023). Signatures of task learning in neural representations. Signatures of task learning in neural representations, Current Opinion in Neurobiology,, 83, 102759. doi:10.1016/j.conb.2023.102759

Pérez-Cervera, A. , Gutkin, B., J Thomas, P. & Lindner, B. (2023). A universal description of stochastic oscillators. Proceedings of the National Academy of Sciences of the United States of America, 120(29), e2303222120. doi:10.1073/pnas.2303222120

Gurnani, H. & Cayco Gajic, A. (2023). Signatures of task learning in neural representations. Current opinion in neurobiology, 83, 102759. doi:10.1016/j.conb.2023.102759

Duriez, A., Bergerot, C., J Cone, J., F Roitman, M. & Gutkin, B. (2023). Homeostatic Reinforcement Theory Accounts for Sodium Appetitive State- and Taste-Dependent Dopamine Responding. Nutrients, 15(4), 1015. doi:10.3390/nu15041015

Lazarevich, I. , Prokin, I., Gutkin, B. & Kazantsev, V. (2023). Spikebench: An open benchmark for spike train time-series classification. PLoS computational biology, 19(1), e1010792. doi:10.1371/journal.pcbi.1010792