Computational model
Competition model of pheromone ratio detection (Zavada et al. 2011)
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.
  • Zavada A, Buckley CL, Martinez D, Rospars JP, Nowotny T (2011) Show Other
  • Nowotny, Thomas [t.nowotny at] Show Other
  • Zavada, Andrei [johnhommer at] Show Other
Zavada, Andrei <>
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Last Time: 10/17/2018 4:09:27 PM
Reviewer: Tom Morse - MoldelDB admin
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