- Poster presentation
Functional identification of an antennal lobe DM4 projection neuron of the fruit fly
BMC Neurosciencevolume 15, Article number: P5 (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 .
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).
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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RTXI: Experiments were performed using the real-time experiment interface. [http://www.rtxi.org]
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