Publications

Dautriche, I., Chemla, E. & Christophe, A. (2016). Word Learning: Homophony and the Distribution of Learning Exemplars. Language Learning and Development, 12(3), 231-251. doi:10.1080/15475441.2015.1127163

Dautriche, I. & Chemla, E. (2016 ). What Homophones Say about Words. PLOS ONE, 11(9), 1-2. doi:10.1371/journal.pone.0162176

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

Schlenker, P. (2018). Visible Meaning: Sign language and the foundations of semantics. Theoretical Linguistics, 44(3-4), 123-208. doi:10.1515/tl-2018-0012

Aristodemo, V. & Geraci, C. (2017). Visible degrees in Italian Sign Language. Natural Language {&} Linguistic Theory, -(on line). doi:10.1007/s11049-017-9389-5

Geraci, C., Bayley, R., Cardinaletti, A., Cecchetto, C. & Donati, C. (2015). Variation in Italian Sign Language (LIS): The case of wh -signs. Linguistics, 53(1), 125–151. doi:10.1515/ling-2014-0031

Križ, M. & Chemla, E. (). Two Methods to Find Truth-Value Gaps and Their Application to the Projection Problem of Homogeneity. Natural Language Semantics, 23(3), 205–248

Chemla, E. & Singh, R. (2017). Training and timing local scalar enrichments under global pragmatic pressures. Journal of Semantics, 34(1), 107-126. doi:10.1093/jos/ffw006

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

Schlenker, P., Chemla, E., Arnold, K., Lemasson, A., Ouattara, K., Keenan, S., Stephan, C., Ryder, R. & Zuberbühler, K. (2013). Towards a formal analysis of primate alarm calls. In 23rd Semantics and Linguistics Theory Conference.

Schlenker, P., Chemla, E., Cäsar, C., Zuberbühler, K. & Ryder, R. (2017). Titi semantics: Context and meaning in Titi monkey call sequences. Natural Language and Linguistic Theory, 35(1), 271-298. doi:10.1007/s11049-016-9337-9

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

Cecchetto, C., Checchetto, A., Geraci, C., Santoro, M. & Zucchi, S. (2015). The syntax of predicate ellipsis in Italian Sign Language (LIS) Lingua, 166, 214–235. doi:10.1016/j.lingua.2014.12.011

Spector, B. (2017). The pragmatics of plural predication: Homogeneity and Non-Maximality within the Rational Speech Act Model. In Cremers, Alexandre and Thom van Gessel and Floris Roelofsen (Eds.), In Proceedings of the 21st Amsterdam Colloquium, 435–444.

Denève, S., Alemi, A. & Bourdoukan, R. (2017). The Brain as an Efficient and Robust Adaptive Learner. Neuron, 94(5), 969-977. doi:10.1016/j.neuron.2017.05.016

Turnbull, R. & Peperkamp, S. (2017). The asymmetric contribution of consonants and vowels to phonological similarity: Evidence from lexical priming. The Mental Lexicon, 12, 404-430. doi:10.1075/ ml .17010.tur

Maex, R. & Gutkin, B. (2017). Temporal integration and 1/f power scaling in a circuit model of cerebellar interneurons. Journal of neurophysiology, 118(1), 471-485. doi:10.1152/jn.00789.2016

Bouchacourt, F., Palminteri, S., Koechlin, E. & Ostojic, S. (2020). Temporal Chunking as a Mechanism for Unsupervised Learning of Task-Sets. eLife, 9, e50469. doi:10.7554/eLife.50469

Zakharov, D., Lapish, C., Gutkin, B. & Kuznetsov, A. (2016). Synergy of AMPA and NMDA Receptor Currents in Dopaminergic Neurons: A Modeling Study. Frontiers in computational neuroscience, 10, 48. doi:10.3389/fncom.2016.00048

Chemla, E. & Egré, P. (2019). Suszko's problem: mixed consequence and compositionality. Review of Symbolic Logic, 12(4), 736-767. doi:10.1017/S1755020318000503

Nicolas, D. & Linnebo, Ø. (2008). Superplurals in English. Analysis, 68(3), 186-197. doi:10.1093/analys/68.3.186

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

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.

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

Muller, L., Brette, R. & Gutkin, B. (2011). Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking. Frontiers in Computational Neuroscience, 15(5), 45. doi:10.3389/fncom.2011.00045

Hyafil, A., Fontolan, L., Kabdebon, C., Gutkin, B. & Giraud, A. (2015). Speech encoding by coupled cortical theta and gamma oscillations. eLife, 4, 1-45. doi:10.7554/eLife.06213

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

Cattaneo, Z., Rinaldi, L., Geraci, C., Cecchetto, C. & Papagno, C. (2018). Spatial biases in deaf, blind, and deafblind individuals as revealed by a haptic line bisection task. Quarterly journal of experimental psychology, 1–9. doi:10.1177/1747021817741288

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

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