Odor Representations in the Rat Olfactory Bulb Change Smoothly with Morphing Stimuli

Adil G Khan,Mukund Thattai,Upinder S Bhalla

doi:10.1016/j.neuron.2008.01.008

Adil G Khan, Mukund Thattai + Show 1 more

Open Access

https://doi.org/10.1016/j.neuron.2008.01.008

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Abstract

SummaryMany species of mammals are very good at categorizing odors. One model for how this is achieved involves the formation of “attractor” states in the olfactory processing pathway, which converge to stable representations for the odor. We analyzed the responses of rat olfactory bulb mitral/tufted (M/T) cells using stimuli “morphing” from one odor to another through intermediate mixtures. We then developed a phenomenological model for the representation of odors and mixtures by M/T cells and show that >80% of odorant responses to different concentrations and mixtures can be expressed in terms of smoothly summing responses to air and the two pure odorants. Furthermore, the model successfully predicts M/T cell responses to odor mixtures when respiration dependence is eliminated. Thus, odor mixtures are represented in the bulb through summation of components, rather than distinct attractor states. We suggest that our olfactory coding model captures many aspects of single and mixed odor representation in M/T cells.

Highlights

Attractor networks are the most common models for explaining memory storage and recall, and input-output transformations in networks of neurons (Amit, 1989; Hopfield, 1982; Rolls and Treves, 1998)
Our experiments were designed to answer the initial question: Does the olfactory bulb (OB) show signatures of attractor dynamics? In the process we have addressed the fundamental issue of the representation of odor identity and intensity in M/T cells, including their responses to varying odor mixtures
Our preliminary analysis argued against strong attractor dynamics in the OB, but this analysis was limited in several ways

Summary

Introduction

Attractor networks are the most common models for explaining memory storage and recall, and input-output transformations in networks of neurons (Amit, 1989; Hopfield, 1982; Rolls and Treves, 1998). These Artificial Neural Networks (ANNs) have multiple stable states. 2006, FENS Forum, abstract; Lee et al, 2004; Leutgeb et al, 2005; Vazdarjanova and Guzowski, 2004; Wills et al, 2005). The recent work by Wills et al and Jezek et al in the hippocampus and entorhinal cortex provides striking results in favor of such theories

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