Decorrelation of odor representations via spike timing-dependent plasticity

• Main Authors: Christiane Linster, Thomas A Cleland
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2010-12-01
• Series: Frontiers in Computational Neuroscience
• Subjects: Olfactory Bulb; STDP; gamma oscillations; Olfaction; antennal lobe; conditioning
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00157/full

The nontopographical representation of odor quality space differentiates early olfactory representations from those in other sensory systems. Decorrelation among olfactory representations with respect to physical odorant similarities has been proposed to rely upon local feed-forward inhibitory circuits in the glomerular layer that decorrelate odor representations with respect to the intrinsically high-dimensional space of ligand-receptor potency relationships. A second stage of experience-dependent decorrelation is likely to be mediated by the circuitry of the olfactory bulb external plexiform layer. Computations in this layer, or in the analogous interneuronal network of the insect antennal lobe, are dependent on fast network oscillations that regulate the timing of mitral cell and projection neuron (MC/PN) action potentials; this suggests a largely spike timing-dependent metric for representing odor information, here proposed to be a precedence code. We first illustrate how the rate coding metric of the glomerular layer can be transformed into a spike precedence code in MC/PNs. We then show how this mechanism of representation, combined with spike timing-dependent plasticity at MC/PN output synapses, can progressively decorrelate high-dimensional, nontopographical odor representations in third-layer olfactory neurons. Reducing MC/PN oscillations abolishes the spike precedence code and blocks this progressive decorrelation, demonstrating the learning network’s selectivity for these sparsely synchronized MC/PN spikes even in the presence of temporally disorganized background activity. Finally, we apply this model to odor representations derived from calcium imaging in the honeybee antennal lobe, and show how odor learning progressively decorrelates odor representations, and how the abolition of PN oscillations impairs odor discrimination.