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Brain research has been driven by enquiry for principles of brain structureorganization and its control mechanisms. The neuronal wiring map of C. elegans,the only complete connectome available till date, presents an incredibleopportunity to learn basic governing principles that drive structure andfunction of its neuronal architecture. Despite its apparently simple nervoussystem, C. elegans is known to possess complex functions. The neuronalarchitecture forms an important underlying framework which specifies phenotypicfeatures associated to sensation, movement, conditioning and memory. In thisstudy, with the help of graph theoretical models, we investigated the C.elegans neuronal network to identify network features that are critical for itscontrol. The 'driver neurons' are associated with important biologicalfunctions such as reproduction, signalling processes and anatomical structuraldevelopment. We created 1D and 2D network models of C. elegans neuronal systemto probe the role of features that confer controllability and small worldnature. The simple 1D ring model is critically poised for the number of feedforward motifs, neuronal clustering and characteristic path-length in responseto synaptic rewiring, indicating optimal rewiring. Using empirically observeddistance constraint in the neuronal network as a guiding principle, we createda distance constrained synaptic plasticity model that simultaneously explainssmall world nature, saturation of feed forward motifs as well as observednumber of driver neurons. The distance constrained model suggests optimum longdistance synaptic connections as a key feature specifying control of thenetwork.

A distance constrained synaptic plasticity model of C. elegans neuronal network