Abstract

We examined the presence of maximum information preservation, which may be a fundamental principle of information transmission in all sensory modalities, in the Drosophila antennal lobe using an experimentally grounded network model and physiological data. Recent studies have shown a nonlinear firing rate transformation between olfactory receptor neurons (ORNs) and second-order projection neurons (PNs). As a result, PNs can use their dynamic range more uniformly than ORNs in response to a diverse set of odors. Although this firing rate transformation is thought to assist the decoder in discriminating between odors, there are no comprehensive, quantitatively supported studies examining this notion. Therefore, we quantitatively investigated the efficiency of this firing rate transformation from the viewpoint of information preservation by computing the mutual information between odor stimuli and PN responses in our network model. In the Drosophila olfactory system, all ORNs and PNs are divided into unique functional processing units called glomeruli. The nonlinear transformation between ORNs and PNs is formed by intraglomerular transformation and interglomerular interaction through local neurons (LNs). By exploring possible nonlinear transformations produced by these two factors in our network model, we found that mutual information is maximized when a weak ORN input is preferentially amplified within a glomerulus and the net LN input to each glomerulus is inhibitory. It is noteworthy that this is the very combination observed experimentally. Furthermore, the shape of the resultant nonlinear transformation is similar to that observed experimentally. These results imply that information related to odor stimuli is almost maximally preserved in the Drosophila olfactory circuit. We also discuss how intraglomerular transformation and interglomerular inhibition combine to maximize mutual information.

Highlights

  • How is sensory information received by sensory receptor cells transferred to higher brain regions? The data processing inequality of information theory states that any kind of information processing can only reduce the amount of information [1]

  • We parameterized the form of intraglomerular transformation as one variable and the strength of Local neurons (LNs) input to each glomerulus as another variable. By systematically varying these two variables, we found that mutual information between odor stimuli and projection neurons (PNs) responses was maximized when the intraglomerular transformation preferentially amplified a weak olfactory receptor neurons (ORNs) input and the net LN input was inhibitory

  • Information theoretic approach First, we computed the mutual information between odor stimuli and PN responses while systematically varying the intraglomerular transformation parameter a and LN input strength K

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Summary

Introduction

How is sensory information received by sensory receptor cells transferred to higher brain regions? The data processing inequality of information theory states that any kind of information processing can only reduce the amount of information [1]. We investigated the presence and mechanisms of maximum information preservation in the olfactory system using a network model and physiological data of neural responses [3,4]. We chose the Drosophila antennal lobe as a model circuit because it has many advantages for investigating information transformation within the circuit. It is organized into discrete compartments termed glomeruli as in the vertebrate olfactory bulb (Fig. 1 (A)) [5,6]. Local neurons (LNs) interconnect glomeruli and mediate both excitation and inhibition [6,12,13,14,15,16,17,18,19,20,21,22] This glomerular architecture simplifies physiological investigations of the circuit’s connectivity. These advantages enabled us to study information processing in the olfactory system on the basis of an olfactory network model that takes account of (1) the actual connectivity, (2) almost all neurons engaged in the olfactory processing, and (3) the response properties of ORNs and PNs to real odorants

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