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Publications

Other  

Branchini, C., Cardinaletti, A., Cecchetto, C., Donati, C. & Geraci, C. (2015). WH-duplication in Italian Sign Language (LIS) , 39–70John Benjamins Publishing Company. doi:10.1075/bct.71.03bra

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  

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

Book chapter  

Zuberbühler, K., Chemla, E. & Schlenker, P. (2021). Stereotyped vocalizations. Encyclopedia of Evolutionary Psychological Science (pp. 7970-7974).Springer. doi:10.1007/978-3-319-16999-6_3330-1

Other  

Geraci, C. (2014). Spatial syntax in your hands. , 1, 123–134

Monograph  

Quer, J., Cecchetto, C., Donati, C., Geraci, C., Kelepir, M., Pfau, R. & Steinbach, M. (2017). SignGram Blueprint: A Guide to Sign Language Grammar Writing.

Book chapter  

Donati, C., Barberà, G., Branchini, C., Cecchetto, C., Geraci, C. & Quer, J. (2017). Searching for imperatives in European sign languages. In Daniël Van}, Olmen and Heinold, Simone (Eds.), Imperatives and Directive Strategies (pp. 111–155).John Benjamins Publishing Company. doi:10.1075/slcs.184.04don

Other  
Other  

Battaglino, C., Geraci, C., Lombardo, V. & Mazzei, A. (2015). Prototyping and Preliminary Evaluation of Sign Language Translation System in the Railway Domain. , 339–350Springer. doi:10.1007/978-3-319-20681-3

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

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  

Geraci, C. & Cecchetto, C. (2013). Negleted cases of rightward movement. , 211–241John Benjamins Publishing Company

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

Other  

Geraci, C. (2015). Italian Sign Language. , 473–510De Gruyter Mouton

Book chapter  

Koralus, P. & Mascarenhas, S. (2018). Illusory Inferences in a question-based theory of reasoning. In Turner, Ken and Horn, Laurence (Eds.), Pragmatics, Truth, and Underspecification: Towards an Atlas of Meaning (pp. 300–322). Leiden: Brill

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  

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  

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

Other  

Geraci, C. & Quer, J. (2014). Determining argument structure in sign languages. , 45–60John Benjamins Publishing Company

Monograph  

Gutkin, B. & Ahmed, S. (2012). Computational Neuroscience of Drug Addiction.

Other  

Geraci, C. & Aristodemo, V. (2016). An in-depth tour into sentential complementation in Italian Sign Language. , 95–150

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  

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