Castro, V. , Clodic, A., Alami, R. & Pacherie, E. (2019). Commitments in Human-Robot Interaction. In AI-HRI 2019 Proceedings. AAAI Fall Symposium Series.
International Journal article
Bobashev, G., Costenbader, E. & Gutkin, B. (2007). Comprehensive mathematical modeling in drug addiction sciences. Drug and Alcohol Dependence, 89(1), 102-106. doi:10.1016/j.drugalcdep.2006.12.029
International Journal article
Tegnér, J., Compte, A., Auffray, C., An, G., Cedersund, G., Clermont, G., Gutkin, B., Oltvai, Z., Stephan, K., Thomas, R. & Villoslada, P. (2009). Computational disease modeling - Fact or fiction? BMC Systems Biology, 3. doi:10.1186/1752-0509-3-56
Monograph
Gutkin, B. & Ahmed, S. (2012). Computational Neuroscience of Drug Addiction.
Proust, J. (2018). Consensus as an epistemic norm for group acceptance. In J. A. Carter, A. Clark, J. Kallestrup, S.O. Palermos, and D. Pritchard (Eds.), Extended Epistemology Oxford : Oxford University Press.
Tran-Van-Minh, A., Caze, R., Abrahamsson, T., Cathala, L., Gutkin, B. & Digregorio, D. (2015). Contribution of sublinear and supralinear dendritic integration to neuronal computations. Frontiers in cellular neuroscience, 9, 67. doi:10.3389/fncel.2015.00067
International Journal article
Morozova, E., Myroshnychenko, M., Zakharov, D., Di Volo, M., Gutkin, B., Lapish, C. & Kuznetsov, A. (2016). Contribution of synchronized GABAergic neurons to dopaminergic neuron firing and bursting. Journal of neurophysiology, 116(4), 1900-1923. doi:10.1152/jn.00232.2016
International Journal article
Dipoppa, M., Szwed, M. & Gutkin, B. (2016). Controlling Working Memory Operations by Selective Gating: The Roles of Oscillations and Synchrony. Advances in cognitive psychology, 12(4), 209-232. doi:10.5709/acp-0199-x
Dipoppa, M. & Gutkin, B. (2013). Correlations in background activity control persistent state stability and allow execution of working memory tasks. Frontiers in Computational Neuroscience, 7, 139. doi:10.3389/fncom.2013.00139
International Journal article
Wu, J., Gao, M., Shen, J., Shi, W., Oster, A. & Gutkin, B. (2013). Cortical control of VTA function and influence on nicotine reward. Biochemical Pharmacology, 86(8), 1173-1180. doi:10.1016/j.bcp.2013.07.013
International Journal article
Brumberg, J. & Gutkin, B. (2007). Cortical pyramidal cells as non-linear oscillators: Experiment and spike-generation theory. Brain Research, 1171(1), 122-137. doi:10.1016/j.brainres.2007.07.028
Volk, D., Dubinin, I., Gutkin, B., Myasnikova, A. & Nikulin, V. (2018). Cross-Frequency Synchrony Analysis. Frontiers in Neuroinformatics , 12, 72. doi:10.3389/fninf.2018.00072
International Journal article
Bourgeois-Gironde, S. (2018). Daniel Serra, Économie Comportementale, Paris, Economica, 2017. Revue d'économie politique, 128, 208
International Journal article
Bouvier, A. (2002). Dans quelle mesure la théorie sociale de James Coleman est-elle trop parcimonieuse?
International Journal article
Bruno, N., Sachs, N., Demily, C., Franck, N. & Pacherie, E. (2012). Delusions and metacognition in patients with schizophrenia. Cognitive neuropsychiatry, 17(1), 1-18. doi:10.1080/13546805.2011.562071
International Journal article
Remme, M., Lengyel, M. & Gutkin, B. (2010). Democracy-independence trade-off in oscillating dendrites and its implications for grid cells. Neuron, 66(3), 429-437. doi:10.1016/j.neuron.2010.04.027
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
Kim, S., Shahaeian, A. & Proust, J. (2018). Developmental diversity in mindreading and metacognition. In Proust, J. & Fortier, M (Eds.), Metacognitive Diversity (pp. 97-133).OUP
International Journal article
Bertoux, M., De Souza, L., Zamith, P., Dubois, B. & Bourgeois-Gironde, S. (2015). Discounting of future rewards in behavioural variant frontotemporal dementia and Alzheimer's disease. Neuropsychology, 29(6), 933-9. doi:10.1037/neu0000197
Gruber, A., Dayan, P., Gutkin, B. & Solla, S. (2006). Dopamine modulation in the basal ganglia locks the gate to working memory. Journal of Computational Neuroscience, 20(2), 153-166. doi:10.1007/s10827-005-5705-x