Functional identification of an antennal lobe DM4 projection neuron of the fruit fly
© Lazar and Yeh; licensee BioMed Central Ltd. 2014
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 . 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)  was proposed for identifying dendritic processing in simple neural circuits using conditional phase response curves (cPRCs) . By combining cPRCs with the reduced project-integrated-and-fire neuron (PIF) model , 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 .
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).
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