Publications

Gu, Z., Smith, K. , Loukatou, G., Completo Guerreiro, I., Dudek, S. , Gutkin, B., Jensen, P. & Yakel, J. (2020). Hippocampal Interneuronal α7 nAChRs Modulate Theta Oscillations in Freely Moving Mice . Cell Reports, 31(10), 107740. doi:10.1016/j.celrep.2020.107740

Keramati, M. & Gutkin, B. (2014). Homeostatic reinforcement learning for integrating reward collection and physiological stability. eLife, 3. doi:10.7554/eLife.04811

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

Ostojic, S., Brunel, N. & Hakim, V. (2009). How connectivity, background activity, and synaptic properties shape the cross-correlation between spike trains. The Journal of Neuroscience, 29(33), 10234-53. doi:10.1523/JNEUROSCI.1275-09.2009

Grèzes, J. & Dezecache, G. (2014). How do shared-representations and emotional processes cooperate in response to social threat signals? Neuropsychologia, 55, 105-14. doi:10.1016/j.neuropsychologia.2013.09.019

Conty, L., Grèzes, J. & Sander, D. (2010). How Does Perceiving Eye Direction Modulate Emotion Recognition? Behavioral and Brain Sciences, 33(6), 443–444

Balsdon, T. & Clifford, C. (2018). How wide is the cone of direct gaze? Royal Society open science 6, 5(8), 180249

Jacquet, P., Wyart, V., Desantis, A., Hsu , Y. , Granjon, L. , Sergent, C. & Waszak, F. (2018). Human susceptibility to social influence and its neural correlates are related to perceived vulnerability to extrinsic morbidity risks. Scientific Reports, 8, 13347. doi:10.1038/s41598-018-31619-8

Radke, S., Seidel, E., Boubela, R. , Thaler, H., Metzler, H., Kryspin-Exner, I., Moser, E., Habel, U. & Derntl, B. (2018). Immediate and delayed neuroendocrine responses to social exclusion in males and females. Psychoneuroendocrinology, 93, 56*64. doi:10.1016/j.psyneuen.2018.04.005

Zhang, D., Gao, M., Xu, D., Shi, W., Gutkin, B., Steffensen, S., Lukas, R. & Wu, J. (2012). Impact of prefrontal cortex in nicotine-induced excitation of ventral tegmental area dopamine neurons in anesthetized rats. Journal of Neuroscience, 32(36), 12366-12375. doi:10.1523/JNEUROSCI.5411-11.2012

Kogler, L., Seidel, E., Metzler, H., Thaler, H., Boubela, R. , Pruessner, J. , Kryspin-Exner, I., Gur, R., Windischberger, C. & Moser, E. (2017). Impact of self-esteem and sex on stress reactions. Scientific reports, 7(1), 17210. doi:10.1038/s41598-017-17485-w

Canavier, C., Evans, R., Oster, A., Pissadaki, E., Drion, G., Kuznetsov, A. & Gutkin, B. (2016). Implications of cellular models of dopamine neurons for disease. Journal of neurophysiology, 116(6), 2815-2830. doi:10.1152/jn.00530.2016

Balsdon, T., Summersby, S., Kemp, R. & White, D. (2018). Improving face identification with specialist teams. Cognitive Research: Principles and Implications, 3(1), 25. doi:10.1186/s41235-018-0114-7

Pouga, L., Berthoz, S., De Gelder, B. & Grèzes, J. (2010). Individual differences in socioaffective skills influence the neural bases of fear processing: the case of alexithymia. Human brain mapping, 31(10), 1469-81. doi:10.1002/hbm.20953

Ladenbauer, J., McKenzie , S., English , D., Hagens , O. & Ostojic, S. (2019). Inferring and validating mechanistic models of neural microcircuits based on spike-train data. Nature Communications, 10(4933). doi:10.1038/s41467-019-12572-0

Yesilaltay , S., Hacquin, A., Chevallier, C. & Mercier, H. (2021). Information delivered by a chatbot has a positive impact on COVID-19 vaccines attitudes and intentions. Journal of Experimental Psychology: Applied. doi:10.1037/xap0000400

Tuckwell, H., Jost, J. & Gutkin, B. (2009). Inhibition and modulation of rhythmic neuronal spiking by noise. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 80(3). doi:10.1103/PhysRevE.80.031907

Bimbard, C., Ledoux, E. & Ostojic, S. (2016). Instability to a heterogeneous oscillatory state in randomly connected recurrent networks with delayed interactions. Physical review. E, 94(6-1), 062207. doi:10.1103/PhysRevE.94.062207

Van Den Stock, J., Peretz, I., Grèzes, J. & De Gelder, B. (2009). Instrumental music influences recognition of emotional body language. Brain topography, 21(3-4), 216-20. doi:10.1007/s10548-009-0099-0

Weiss, A., Chambon, V., Drugowitsch, J. & Wyart, V. (2021). Interacting with volatile environments stabilizes hidden-state inference and its brain signatures. Nature Communications, 12, 2228. doi:10.1038/s41467-021-22396-6

Jazayeri, M. & Ostojic, S. (2021). Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity. Current Opinion in Neurobiology, 70, 113-120. doi:10.1016/j.conb.2021.08.002

Ostojic, S. (2011). Interspike interval distributions of spiking neurons driven by fluctuating inputs. Journal of neurophysiology, 106(1), 361-73. doi:10.1152/jn.00830.2010

Krupa, M., Gielen, S. & Gutkin, B. (2014). Intrinsic and synaptic mechanisms for clustered cortical gamma. J Comput. Neurosci , 37(2), 357-76

Mastrogiuseppe, F. & Ostojic, S. (2017). Intrinsically-generated fluctuating activity in excitatory-inhibitory networks. PLoS computational biology, 13(4), e1005498. doi:10.1371/journal.pcbi.1005498

Buchin, A., Rieubland, S., Häusser, M., Gutkin, B. & Roth, A. (2016). Inverse Stochastic Resonance in Cerebellar Purkinje Cells. PLoS computational biology, 12(8), e1005000. doi:10.1371/journal.pcbi.1005000

Simeone, O. , Rajendran, B. , Gruning, A. , Eleftheriou, E. , Davies, M. , Denève, S. & Huang, G. (2019). Learning Algorithms and Signal Processing for Brain-Inspired Computing. IEEE Signal Processing Magazine, 36(6), 12-15. doi:10.1109/MSP.2019.2935557

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.

Brendel, W. , Bourdoukan, R., Vertechi, P. , Machens, C. & Denève, S. (2020). Learning to represent signals spike by spike. PLOS Computational Biology, 16(3), e1007692. doi:10.1371/journal.pcbi.1007692

Wyart, V. (2018). Leveraging decision consistency to decompose suboptimality in terms of its ultimate predictability. Behavioral and Brain Sciences, 41, e248. doi:10.1017/S0140525X18001504

Mastrogiuseppe, F. & Ostojic, S. (2018). Linking connectivity, dynamics and computations in recurrent neural networks. Neuron, 99(3), 609-623. doi:DOI:https://doi.org/10.1016/j.neuron.2018.07.003