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We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC).
Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants.
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The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning.
We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants.
Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures."
Reference:
1 .
Kaplan BA, Lansner A (2014) A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system. Front Neural Circuits 8:5 [PubMed]
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