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Estimating receptive fields and spike-processing neural circuits in Drosophila

One of the long-term goals of sensory neuroscience is the development of sound experimental and theoretical methods for understanding the functional organization of sensory systems. In this regard, Drosophila melanogaster is the model organism of choice: it boasts a relatively small brain, its sensory systems have been anatomically well characterized and it offers an extensive genetic toolbox for visualizing and altering its neural circuits. Despite these advantages however, comprehensive models of sensory processing in Drosophila are sparse, in particular due to the lack of methods for estimating spike-processing neural circuits in higher brain centers. The majority of existing neural circuit models and methods for their identification assume rate-based systems (see [1] for a review), and take both the input (stimuli) and the output (response rates) to be in the continuous domain. In a practical setting, however, outputs of most neurons in a sensory system are sequences of all-or-none action potentials. Furthermore, input signals are continuous only for those neurons that are located at the sensory periphery. In contrast, input signals for neurons upstream of sensory neurons are spatiotemporal spike trains. Hence, there is a need to develop a generic framework for estimation of both receptive fields in the periphery and of spatiotemporal spike processing upstream.

Here we propose a novel theoretical approach for estimating receptive fields in circuit models that incorporate biophysical spike-generating mechanisms (e.g., the Hodgkin-Huxley neuron) and admit both continuous sensory signals and multidimensional spike trains as input stimuli. We thus explicitly take into account the highly nonlinear nature of spike generation that has been shown to result in significant interactions between various stimulus features [2], [3] and to fundamentally affect the estimation of receptive fields [4]. Furthermore, and in contrast to many existing methods [1], our approach estimates receptive fields directly from spike times produced by a neuron, thereby obviating the need to repeat experiments in order to compute the neuron’s instantaneous rate of response (e.g., PSTH). The employed test signals belong to spaces of bandlimited functions and bridge the gap between identification using synthetic and naturalistic stimuli. This makes our methodology particularly attractive in those sensory modalities (most notably olfaction [5]), where it is difficult to produce stimuli that are white and/or have particular distribution/ attributes [1]-[4]. First, we work out in detail algorithms for identifying temporal, spatial and spatiotemporal receptive fields in the sensory periphery. We show that our methodology is readily generalizable to multiple receptive fields as well as to higher dimensions, allowing one to consider more complex receptive fields, if needed. Second, we demonstrate how to identify the processing of multiple spiking inputs converging onto the dendritic tree of a spiking neuron. Third, we show that the presented methodology allows one to model integration of sensory modalities in higher brain centers. Finally, we test the proposed approach using in-vivo data recorded from the olfactory system of Drosophila.


  1. Wu Michael CK, David Stephen V, Gallant Jack L: Complete functional characterization of sensory neurons by system identification. Annual Reviews of Neuroscience. 2006, 29: 477-505. 10.1146/annurev.neuro.29.051605.113024.

    Article  CAS  Google Scholar 

  2. Slee Sean J, Higgs Matthew H, Fairhall Adrienne L, Spain William J: Two-dimensional time coding in the auditory brainstem. The Journal of Neuroscience. 2005, 25 (43): 9978-9988. 10.1523/JNEUROSCI.2666-05.2005.

    Article  CAS  PubMed  Google Scholar 

  3. Hong Sungho, Arcas Blaise Agüera y, Fairhall Adrienne L: Single neuron computation: From dynamical system to feature detector. Neural Computation. 2007, 112: 3133-3172.

    Article  Google Scholar 

  4. Pillow Jonathan W, Simoncelli Eero P: Biases in white noise analysis due to non-Poisson spike generation. Neurocomputing. 2003, 52–54: 109-115.

    Article  Google Scholar 

  5. Kim Anmo J, Lazar Aurel A, Slutskiy Yevgeniy B: System identification of Drosophila olfactory sensory neurons. Journal of Computational Neuroscience. 2010, 30 (1):

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The work presented here was supported by NIH under the grant number R01DC008701-05.

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Correspondence to Aurel A Lazar.

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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 (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Lazar, A.A., Slutskiy, Y.B. Estimating receptive fields and spike-processing neural circuits in Drosophila. BMC Neurosci 13 (Suppl 1), O10 (2012).

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