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.
Highlights
The major task of the olfactory system is to perform recognition of odors which is essential for survival by identifying edibility or danger
In order to study the mechanisms embedded in the olfactory system, a system-level approach is required, comprising the three major levels of the early olfactory hierarchy including the epithelium, where the stimulus enters the nervous system, the olfactory bulb (OB) where the first transformation happens, and the piriform cortex (PC) which integrates and stores the information relevant for odor recognition (Gottfried, 2010; Wilson and Sullivan, 2011), Abbreviations: OR, olfactory receptor; ORN, olfactory receptor neuron; OB, olfactory bulb; MT, mitral or tufted cell; PG, periglomerular cell; PC, piriform cortex; pyramidal cells (PYR), pyramidal neurons; AMPA, α-amino-3-hydroxy-5-methyl-4isoxazolepropionic acid; NMDA, N-methyl-D-aspartate; GABA, γ-aminobutyric acid; WTA, winner-take-all operation; MC, minicolumn; HC, hypercolumn; vector quantization (VQ), vector-quantization; multi-dimensional scaling algorithm (MDS), multi-dimensional scaling
We will first show the response curves of ORNs and MT cells realizing the hypothesized fuzzy concentration interval code before we focus on the five functional tasks the system has been tested with
Summary
The major task of the olfactory system is to perform recognition of odors which is essential for survival by identifying edibility or danger. The crucial mechanisms involved in odor object recognition are widely unknown, which is mainly due to the complexity of interactions and the transformations of information occurring between the different stages. In order to study the mechanisms embedded in the olfactory system, a system-level approach is required, comprising the three major levels of the early olfactory hierarchy including the epithelium, where the stimulus enters the nervous system, the olfactory bulb (OB) where the first transformation happens, and the piriform cortex (PC) which integrates and stores the information relevant for odor recognition (Gottfried, 2010; Wilson and Sullivan, 2011), Abbreviations: OR, olfactory receptor; ORN, olfactory receptor neuron; OB, olfactory bulb; MT, mitral or tufted cell; PG, periglomerular cell; PC, piriform cortex; PYR, pyramidal neurons; AMPA, α-amino-3-hydroxy-5-methyl-4isoxazolepropionic acid; NMDA, N-methyl-D-aspartate; GABA, γ-aminobutyric acid; WTA, winner-take-all operation; MC, minicolumn; HC, hypercolumn; VQ, vector-quantization; MDS, multi-dimensional scaling. Neurons in the PC receive convergent synaptic input from different glomeruli (Apicella et al, 2010), but the question as to which principles underlie the connectivity between OB and PC is not yet resolved
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