J. G. Hildebrand and G. M. Shepherd, Mechanisms of olfactory discrimination: converging evidence for common principles across phyla, Annu. Rev. Neurosci, vol.20, pp.595-631, 1997.

R. I. Wilson and Z. F. Mainen, Early events in olfactory processing, Annu. Rev. Neurosci, vol.29, pp.163-201, 2006.

P. Lánský and J. Rospars, Odorant concentration and receptor potential in olfactory sensory neurons, BioSystems, vol.48, issue.98, pp.58-64, 1998.

B. Lindemann, Predicted profiles of ion concentrations in olfactory cilia in the steady state, Biophys. J, vol.80, pp.76142-76147, 2001.

N. Suzuki, M. Takahata, and K. Sato, Oscillatory current responses of olfactory receptor neurons to odorants and computer simulation based on a cyclic AMP transduction model, Chem. Senses, vol.27, pp.789-801, 2002.

D. P. Dougherty, G. A. Wright, and A. C. Yew, Computational model of the cAMP-mediated sensory response and calcium-dependent adaptation in vertebrate olfactory receptor neurons, Proc. Natl Acad. Sci. USA, vol.102, p.415, 2005.

Y. Gu, P. Lucas, and J. Rospars, Computational model of the insect pheromone transduction cascade, PLOS Comput. Biol, vol.5, 2009.

K. Kaissling, Olfactory perireceptor and receptor events in moths: a kinetic model revised, 2009.

, J. Comp. Physiol. A, vol.195, pp.895-922

M. Schmuker, N. Yamagata, M. Nawrot, and R. Menzel, Parallel representation of stimulus identity and intensity in a dual pathway model inspired by the olfactory system of the honeybee. Front. Neuroeng. 4, 17, 2011.

J. Wessnitzer, J. M. Young, J. D. Armstrong, and B. Webb, 2012 A model of non-elemental olfactory learning in Drosophila, J. Comput. Neurosci, vol.32, pp.197-212

T. Kee, P. Sanda, N. Gupta, M. Stopfer, and M. Bazhenov, Feed-forward versus feedback inhibition in a basic olfactory circuit, PLOS Comput. Biol, vol.11, 2015.

H. Maboudi, H. Shimazaki, M. Giurfa, and L. Chittka, Olfactory learning without the mushroom bodies: spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities, PLoS Comput. Biol, vol.13, p.1005551, 2017.

J. Rospars, P. Lánský, H. C. Tuckwell, A. Vermeulen, S. Gorur-shandilya et al., Olfactory receptor neurons use gain control and complementary kinetics to encode intermittent odorant stimuli, J. Comput. Neurosci, vol.3, pp.51-72, 1996.

L. Kostal, P. Lansky, and J. Rospars, Efficient olfactory coding in the pheromone receptor neuron of a moth, PLoS Comput. Biol, vol.4, 2008.

J. Rospars, P. Lánský, and V. K?ivan, Extracellular transduction events under pulsed stimulation in moth olfactory sensilla, Chem. Senses, vol.28, pp.509-522, 2003.

Y. Gu and J. Rospars, Dynamical modeling of the moth pheromone-sensitive olfactory receptor neuron within its sensillar environment, PLoS ONE, vol.6, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01000502

J. Reingruber and D. Holcman, Gated narrow escape time for molecular signaling, Phys. Rev. Lett, vol.103, p.148102, 2009.

L. Lapicque, Recherches quantitatives sur lexcitation electrique des nerfs traitee comme une polarization, J. Physiol. Pathol. Gen, vol.9, pp.620-635, 1907.

R. B. Stein, A theoretical analysis of neuronal variability, Biophys. J, vol.5, issue.65, pp.86709-86710, 1965.

A. N. Burkitt, A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input, Biol. Cybern, vol.95, pp.1-19, 2006.

A. Rauch, L. Camera, G. Luscher, H. Senn, W. Fusi et al., Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents, J. Neurophysiol, vol.90, pp.1598-1612, 2003.

R. Jolivet, R. Kobayashi, A. Rauch, R. Naud, S. Shinomoto et al., A benchmark test for a quantitative assessment of simple neuron models, 2008.

, J. Neurosci. Methods, vol.169, pp.417-424

R. Borisyuk, Oscillatory activity in the neural networks of spiking elements, BioSystems, vol.67, pp.3-16, 2002.

M. Helias, M. Deger, M. Diesmann, and S. Rotter, Equilibrium and response properties of the integrate-and-fire neuron in discrete time, Front. Comput. Neurosci, vol.3, 2009.

P. Lánský, J. Rospars, and A. Vermeulen, Basic mechanisms of coding stimulus intensity in the olfactory sensory neuron, Neural Process. Lett, vol.1, pp.9-12, 1994.

A. Celani, E. Villermaux, and M. Vergassola, Odor landscapes in turbulent environments, Phys. Rev. X, vol.4, p.41015, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01243688

A. Grémiaux, T. Nowotny, D. Martinez, P. Lucas, and J. Rospars, Modelling the signal delivered by a population of first-order neurons in a moth olfactory system, Brain Res, vol.1434, pp.123-135, 2012.

J. Rospars, A. Grémiaux, D. Jarriault, A. Chaffiol, C. Monsempes et al., Heterogeneity and convergence of olfactory first-order neurons account for the high speed and sensitivity of second-order neurons, PLoS Comp. Biol, vol.10, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01209986

K. Kaissling, Olfactory perireceptor and receptor events in moths: a kinetic model, Chem. Senses, vol.26, pp.125-150, 2001.

K. Kaissling and J. Rospars, Dose-response relationships in an olfactory flux detector model revisited, Chem. Senses, vol.29, pp.529-531, 2004.

P. Dayan and L. F. Abbott, Theoretical neuroscience: computational and mathematical modeling of neural systems, 2001.

M. J. Chacron, K. Pakdaman, and A. Longtin, Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-and-fire model with threshold fatigue, Neural Comput, vol.15, pp.253-278, 2003.

R. Jolivet, A. Rauch, H. Lüscher, and W. Gerstner, Predicting spike timing of neocortical pyramidal neurons by simple threshold models, J. Comput. Neurosci, vol.21, pp.35-49, 2006.

R. Kobayashi, Y. Tsubo, and S. Shinomoto, Made-toorder spiking neuron model equipped with a multitimescale adaptive threshold, Front. Comput. Neurosci, vol.3, 2009.

R. Kobayashi and K. Kitano, Impact of slow K + currents on spike generation can be described by an adaptive threshold model, J. Comput. Neurosci, vol.40, pp.347-362, 2016.

J. Rospars, V. K?ivan, and P. Lánský, Perireceptor and receptor events in olfaction. Comparison of concentration and flux detectors: a modeling study, Chem. Senses, vol.25, pp.293-311, 2000.

A. Minor and K. Kaissling, Cell responses to single pheromone molecules may reflect the activation kinetics of olfactory receptor molecules, J. Comp. Physiol. A, vol.189, pp.221-230, 2003.

P. Lucas and T. Shimahara, Voltage-and calciumactivated currents in cultured olfactory receptor neurons of male Mamestra brassicae (Lepidoptera), Chem. Senses, vol.27, pp.599-610, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00201231

F. Zufall, M. Stengl, C. Franke, J. G. Hildebrand, and H. Hatt, Ionic currents of cultured olfactory receptor neurons from antennae of male Manduca sexta, 1991.

, J. Neurosci, vol.11, pp.956-965, 1991.

M. N. Geffen, B. M. Broome, G. Laurent, and M. Meister, Neural encoding of rapidly fluctuating odors, Neuron, vol.61, pp.570-586, 2009.

V. Jacob, C. Monsempès, J. Rospars, J. Masson, and P. Lucas, Olfactory coding in the turbulent realm, PLoS Comput. Biol, vol.13, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02624610

, /journal/rsif J. R. Soc. Interface, vol.16, p.20190246

X. Grosmaitre, A. Vassalli, P. Mombaerts, G. M. Shepherd, and M. Ma, Odorant responses of olfactory sensory neurons expressing the odorant receptor MOR23: a patch clamp analysis in gene-targeted mice, Proc. Natl Acad. Sci. USA, vol.103, 1970.
URL : https://hal.archives-ouvertes.fr/hal-00355653

Y. Liu and X. Wang, Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron, J. Comput. Neurosci, vol.10, pp.25-45, 2001.

K. I. Nagel and R. I. Wilson, Biophysical mechanisms underlying olfactory receptor neuron dynamics, Nat. Neurosci, vol.14, pp.208-216, 2011.

J. J. Hopfield, Pattern recognition computation using action potential timing for stimulus representation, Nature, vol.376, pp.33-36, 1995.

C. D. Brody and J. Hopfield, Simple networks for spike-timing-based computation, with application to olfactory processing, Neuron, vol.37, pp.843-852, 2003.

S. Cassenaer and G. Laurent, Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts, Nature, vol.448, pp.709-713, 2007.

A. Coulon, G. Beslon, and H. A. Soula, Enhanced stimulus encoding capabilities with spectral selectivity in inhibitory circuits by STDP, Neural Comput, vol.23, pp.882-908, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00593704

K. Kaissling, C. Z. Strausfeld, and E. Rumbo, Adaptation processes in insect olfactory receptors, 1987.

, Ann. N. Y. Acad. Sci, vol.510, pp.104-112

J. Dolzer, K. Fischer, and M. Stengl, Adaptation in pheromone-sensitive trichoid sensilla of the hawkmoth Manduca sexta, J. Exp. Biol, vol.206, pp.1575-1588, 2003.

M. S. Goldman, J. Golowasch, E. Marder, and L. Abbott, Global structure, robustness, and modulation of neuronal models, J. Neurosci, vol.21, pp.5229-5238, 2001.

P. Achard and E. De-schutter, Complex parameter landscape for a complex neuron model, PLOS Comp. Biol, vol.2, p.94, 2006.

L. H. Cao, B. Y. Jing, D. Yang, X. Zeng, Y. Shen et al., Distinct signaling of Drosophila chemoreceptors in olfactory sensory neurons, Proc. Natl Acad. Sci. USA, vol.113, pp.902-911, 2016.

F. Kawai, Ca 2+ -activated K + currents regulate odor adaptation by modulating spike encoding of olfactory receptor cells, Biophys. J, vol.82, 2002.

D. Wicher, Tuning insect odorant receptors, Front. Cell. Neurosci, vol.12, 2018.

M. Stengl, Pheromone transduction in moths, Front. Cell. Neurosci, vol.4, pp.1-15, 2010.

C. Martelli, J. R. Carlson, and T. Emonet, Intensity invariant dynamics and odor-specific latencies in olfactory receptor neuron response, J. Neurosci, vol.33, pp.6285-6297, 2013.

M. Levakova, L. Kostal, C. Monsempès, V. Jacob, and P. Lucas, Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations, PLoS Comp. Biol, vol.14, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02621297

A. Nolte, P. Gawalek, S. Koerte, H. Wei, R. Schumann et al., No evidence for ionotropic pheromone transduction in the hawkmoth Manduca sexta, PLoS ONE, vol.11, 2016.

. R-core-team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, 2017.

S. Poitout and R. Bues, Elevage de chenilles de vingt-huit espèces de Lépidoptères Noctuidae et de deux espèces d'arctiidae sur milieu artificiel simple. particularités de l'élevage selon les espèces, Ann. Zool. Ecol. Anim, vol.6, pp.431-441, 1974.

M. Nawrot, A. Aertsen, and S. Rotter, Single-trial estimation of neuronal firing rates: from singleneuron spike trains to population activity, 1999.

, J. Neurosci. Methods, vol.94, issue.99, pp.127-129

H. Shimazaki and S. Shinomoto, Kernel bandwidth optimization in spike rate estimation, J. Comput. Neurosci, vol.29, pp.171-182, 2010.

J. A. Nelder and R. Mead, A simplex method for function minimization, Comput. J, vol.7, pp.308-313, 1965.

, /journal/rsif J. R. Soc. Interface, vol.16, p.20190246