title: A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system.
abstract: Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. 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. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. 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.
authors.affiliation: Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology Stockholm, Sweden ; Stockholm Brain Institute, Karolinska Institute Stockholm, Sweden.
authors.forename: Bernhard A
authors.initials: BA
authors.lastname: Kaplan
authors.affiliation: Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology Stockholm, Sweden ; Stockholm Brain Institute, Karolinska Institute Stockholm, Sweden ; Department of Numerical Analysis and Computer Science, Stockholm University Stockholm, Sweden.
authors.forename: Anders
authors.initials: A
authors.lastname: Lansner
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology; D009830:Olfactory Bulb / Q000502:physiology; D009833:Olfactory Pathways / Q000502:physiology*; D018034:Olfactory Receptor Neurons / Q000502:physiology*; D046709:Pattern Recognition, Physiological / Q000502:physiology*
keywords: BCPNN; concentration invariance; large-scale neuromorphic systems; olfactory bulb; pattern recognition; pattern rivalry; piriform cortex; spiking neural network