@article {1233,
	title = {Structural properties of the Caenorhabditis elegans neuronal network.},
	journal = {PLoS Computational Biology},
	volume = {7},
	year = {2011},
	month = {2011 Feb 3},
	pages = {e1001066},
	abstract = {<p>Despite recent interest in reconstructing neuronal networks, complete wiring diagrams on the level of individual synapses remain scarce and the insights into function they can provide remain unclear. Even for Caenorhabditis elegans, whose neuronal network is relatively small and stereotypical from animal to animal, published wiring diagrams are neither accurate nor complete and self-consistent. Using materials from White et al. and new electron micrographs we assemble whole, self-consistent gap junction and chemical synapse networks of hermaphrodite C. elegans. We propose a method to visualize the wiring diagram, which reflects network signal flow. We calculate statistical and topological properties of the network, such as degree distributions, synaptic multiplicities, and small-world properties, that help in understanding network signal propagation. We identify neurons that may play central roles in information processing, and network motifs that could serve as functional modules of the network. We explore propagation of neuronal activity in response to sensory or artificial stimulation using linear systems theory and find several activity patterns that could serve as substrates of previously described behaviors. Finally, we analyze the interaction between the gap junction and the chemical synapse networks. Since several statistical properties of the C. elegans network, such as multiplicity and motif distributions are similar to those found in mammalian neocortex, they likely point to general principles of neuronal networks. The wiring diagram reported here can help in understanding the mechanistic basis of behavior by generating predictions about future experiments involving genetic perturbations, laser ablations, or monitoring propagation of neuronal activity in response to stimulation.</p>
},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1001066},
	author = {Varshney, Lav R and Chen, Beth L and Paniagua, Eric and Hall, David H and Chklovskii, Dmitri B}
}
@article {1238,
	title = {Optimal information storage in noisy synapses under resource constraints.},
	journal = {Neuron},
	volume = {52},
	year = {2006},
	month = {2006 Nov 9},
	pages = {409-23},
	abstract = {<p>Experimental investigations have revealed that synapses possess interesting and, in some cases, unexpected properties. We propose a theoretical framework that accounts for three of these properties: typical central synapses are noisy, the distribution of synaptic weights among central synapses is wide, and synaptic connectivity between neurons is sparse. We also comment on the possibility that synaptic weights may vary in discrete steps. Our approach is based on maximizing information storage capacity of neural tissue under resource constraints. Based on previous experimental and theoretical work, we use volume as a limited resource and utilize the empirical relationship between volume and synaptic weight. Solutions of our constrained optimization problems are not only consistent with existing experimental measurements but also make nontrivial predictions.</p>
},
	keywords = {Animals, Excitatory Postsynaptic Potentials, Information Storage and Retrieval, Memory, Models, Neurological, Synapses, Synaptic Transmission},
	issn = {0896-6273},
	doi = {10.1371/journal.pcbi.1001066},
	author = {Varshney, Lav R and Sj{\"o}str{\"o}m, Per Jesper and Chklovskii, Dmitri B}
}
