For some closely related sympatric moth species, recognizing a specific pheromone component concentration ratio is essential for mating success. We propose and test a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which. (3) longer durations of the competition process between LNs did not result in higher recognition accuracy.
Model Type: Realistic Network
Cell Type(s): Olfactory receptor GLU cell; Hodgkin-Huxley neuron
Receptors: Gaba
Transmitters: Acetylcholine; Gaba
Model Concept(s): Simplified Models; Sensory processing; Olfaction
Simulation Environment: C or C++ program; CNrun; neuroConstruct
Implementer(s): Nowotny, Thomas [t.nowotny at sussex.ac.uk]; Zavada, Andrei [johnhommer at gmail.com]
References:
Zavada A, Buckley CL, Martinez D, Rospars JP, Nowotny T. (2011). Competition-based model of pheromone component ratio detection in the moth. PloS one. 6 [PubMed]