Wiring Economy and Volume Exclusion Determine Neuronal Placement in the Drosophila Brain

Wiring economy has successfully explained the individual placement of neurons in simple nervous systems like that of Caenorhabditis elegans [1–3] and the locations of coarser structures like cortical areas in complex vertebrate brains [4]. However, it remains unclear whether wiring economy can explain the placement of individual neurons in brains larger than that ofC. elegans. Indeed, given the greater number of neuronal interconnections in larger brains, simply minimizing the length of connections results in unrealistic configurations, with multiple neurons occupying the same position in space. Avoiding such configurations, or volume exclusion, repels neurons from each other, thus counteracting wiring economy. Here we test whether wiring economy together with volume exclusion can explain the placement of neurons in amodule of theDrosophilamelanogaster brain known as lamina cartridge [5–13]. We used newly developed techniques for semiautomated reconstruction from serial electron microscopy (EM) [14] to obtain the shapes of neurons, the location of synapses, and the resultant synaptic connectivity. We show that wiring length minimization and volume exclusion together can explain the structure of the lamina microcircuit. Therefore, even in brains larger than that of C. elegans, at least for some circuits, optimization can play an important role in individual neuron placement.

[1]  D. Chklovskii,et al.  Maps in the brain: what can we learn from them? , 2004, Annual review of neuroscience.

[2]  Current Biology , 2012, Current Biology.

[3]  N. Strausfeld,et al.  Dissection of the Peripheral Motion Channel in the Visual System of Drosophila melanogaster , 2007, Neuron.

[4]  Dmitri B. Chklovskii,et al.  Wiring Optimization in Cortical Circuits , 2002, Neuron.

[5]  Gonzalo G de Polavieja,et al.  Structure of deviations from optimality in biological systems , 2009, Proceedings of the National Academy of Sciences.

[6]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[7]  Louis K. Scheffer,et al.  Semi-automated reconstruction of neural circuits using electron microscopy , 2010, Current Opinion in Neurobiology.

[8]  Alexander Borst,et al.  ON and OFF pathways in Drosophila motion vision , 2010, Nature.

[9]  Damon A. Clark,et al.  Defining the Computational Structure of the Motion Detector in Drosophila , 2011, Neuron.

[10]  Jason C. Caldwell,et al.  Drosophila N-cadherin mediates an attractive interaction between photoreceptor axons and their targets , 2005, Nature Neuroscience.

[11]  W. Ribi,et al.  Gap junctions coupling photoreceptor axons in the first optic ganglion of the fly , 1978, Cell and Tissue Research.

[12]  D. Chklovskii,et al.  Wiring optimization can relate neuronal structure and function. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Yan Zhu,et al.  Peripheral Visual Circuits Functionally Segregate Motion and Phototaxis Behaviors in the Fly , 2009, Current Biology.

[14]  S. Laughlin A Simple Coding Procedure Enhances a Neuron's Information Capacity , 1981, Zeitschrift fur Naturforschung. Section C, Biosciences.

[15]  P. Sterling,et al.  How the Optic Nerve Allocates Space, Energy Capacity, and Information , 2009, The Journal of Neuroscience.

[16]  Dmitri B Chklovskii,et al.  Synaptic Connectivity and Neuronal Morphology Two Sides of the Same Coin , 2004, Neuron.

[17]  S. Laughlin,et al.  Fly Photoreceptors Demonstrate Energy-Information Trade-Offs in Neural Coding , 2007, PLoS biology.

[18]  Hausser Michael,et al.  One rule to grow them all: A general theory of neuronal branching and its practical application , 2010 .

[19]  Michael J. Berry,et al.  Metabolically Efficient Information Processing , 2001, Neural Computation.

[20]  Vitaly A Klyachko,et al.  Connectivity optimization and the positioning of cortical areas , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[21]  A. Borst,et al.  Internal Structure of the Fly Elementary Motion Detector , 2011, Neuron.

[22]  Michael J. Berry,et al.  A test of metabolically efficient coding in the retina , 2002, Network.

[23]  I. Meinertzhagen,et al.  Synaptic organization in the fly's optic lamina: few cells, many synapses and divergent microcircuits. , 2001, Progress in brain research.

[24]  Dmitri B. Chklovskii,et al.  Synaptic Connectivity and Neuronal MorphologyTwo Sides of the Same Coin , 2004 .

[25]  I. Meinertzhagen,et al.  Synaptic organization of columnar elements in the lamina of the wild type in Drosophila melanogaster , 1991, The Journal of comparative neurology.

[26]  Roger C. Hardie,et al.  Feedback Network Controls Photoreceptor Output at the Layer of First Visual Synapses in Drosophila , 2006, The Journal of general physiology.

[27]  G. Buzsáki,et al.  Interneuron Diversity series: Circuit complexity and axon wiring economy of cortical interneurons , 2004, Trends in Neurosciences.

[28]  Shin-ya Takemura,et al.  Synaptic circuits of the Drosophila optic lobe: The input terminals to the medulla , 2008, The Journal of comparative neurology.

[29]  G. Mitchison Neuronal branching patterns and the economy of cortical wiring , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[30]  Louis K. Scheffer,et al.  Wiring economy and volume exclusion determine neuronal placement in the Drosophila brain. , 2011, Current biology : CB.

[31]  Christopher Cherniak,et al.  Local optimization of neuron arbors , 1992, Biological Cybernetics.

[32]  Gonzalo G de Polavieja Errors drive the evolution of biological signalling to costly codes. , 2002, Journal of theoretical biology.

[33]  A. Pérez-Escudero,et al.  Optimally wired subnetwork determines neuroanatomy of Caenorhabditis elegans , 2007, Proceedings of the National Academy of Sciences.

[34]  Dmitri B. Chklovskii,et al.  Exact Solution for the Optimal Neuronal Layout Problem , 2004, Neural Computation.