Volume 15 Supplement 1

Abstracts from the Twenty Third Annual Computational Neuroscience Meeting: CNS*2014

Open Access

Functional identification of an antennal lobe DM4 projection neuron of the fruit fly

BMC Neuroscience201415(Suppl 1):P5

DOI: 10.1186/1471-2202-15-S1-P5

Published: 21 July 2014

A rich set of genetic tools and extensive anatomical data make the olfactory system of the fruit fly a neural circuit of choice for studying function in sensory systems. Though a substantial amount of work has been published on the neural coding of olfactory sensory neurons (OSNs) of the fruit fly, yet little is known how projection neurons (PNs) encode time-varying odor stimuli [1]. Here we address this question with in vivo experiments coupled with a phenomenological characterization of the spiking activity of PNs.

Recently, a new class of identification algorithms called Channel Identification Machines (CIMs) [2] was proposed for identifying dendritic processing in simple neural circuits using conditional phase response curves (cPRCs) [3]. By combining cPRCs with the reduced project-integrated-and-fire neuron (PIF) model [4], the CIM algorithms identify a complete phenomenological description of spike generation of a biological neuron for weak to moderately strong stimuli. Moreover, the identification method employed does not require white noise stimuli nor an infinitesimal pulse injection protocol as widely used in the past [5].

Here we identify the PNs both in silico and in vivo. Starting with simulations, we investigate the feasibility of the CIM method on PNs modeled as pseudo uni-polar neurons in silico, as shown in Figures 1.(B) and 1.(C). We then systematically convert the CIM method into a step-by-step experimental protocol, and carry it out in vivo by injecting currents into PNs using the patch clamping technique [6, 7]. A snapshot of PN patching is depicted in Figure 1.(A).
Figure 1

(A) PN with GFP expression under 60x magnification; (B) Schematic of the anatomy of the PN; (C) Circuit diagram of the simulated PN model; (D) Identified PRC of the simulated PN model; The injected current varies from 25 [pA] to 60 [pA] with step size 1 [pA]; (E) Identified PRC of PN. The injected current varies from 30 [pA] to 50 [pA] with step size 5 [pA]; (F-J) Comparison between identified PRC of simulated PN model and the in vivo PN; Injected current value is 30 [pA] to 50 [pA] with step size 5 [pA] from (F) to (J). Time is in seconds.

We demonstrate that the CIM method accurately identifies the cPRCs of the in silico PN model for a wide range of bias currents, as shown in Figure 1.(D). Moreover, the new method also accurately identifies a set of cPRCs of PNs in vivo, as shown in Figure 1.(E). For comparison, we tune the identified cPRCs of the in silico PN model to fit the in vivo identified cPRCs of biological PNs. We demonstrate that: (i) the new method accurately identifies the cPRCs of PNs for small bias currents; (ii) the accuracy of the cPRC is qualitatively lower during the refractory period, as depicted in Figures 1.(F-J).

Authors’ Affiliations

(1)
Department of Electrical Engineering, Columbia University

References

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Copyright

© Lazar and Yeh; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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