A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system
2014 (English)In: Frontiers in Neural Circuits, ISSN 1662-5110, Vol. 8, no Feb, 5- p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2014. Vol. 8, no Feb, 5- p.
BCPNN, Concentration invariance, Large-scale neuromorphic systems, Olfactory bulb, Pattern recognition, Pattern rivalry, Piriform cortex, Spiking neural network
Bioinformatics (Computational Biology) Neurology
IdentifiersURN: urn:nbn:se:kth:diva-142800DOI: 10.3389/fncir.2014.00005ISI: 000332714100001ScopusID: 2-s2.0-84893603482OAI: oai:DiVA.org:kth-142800DiVA: diva2:704719
FunderEU, FP7, Seventh Framework Programme, 237955 FP7-269921 FP7-216916
QC 201403132014-03-132014-03-122015-05-04Bibliographically approved