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| Poster (After Call) | FZJ-2019-03684 |
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2019
Abstract: A statement like “$N_\text{s}$ source neurons and $N_\text{t}$ target neurons are connected randomly with connectionprobability $p$” may be used to describe the structure of a neuronal network model, but itsinterpretation is inherently ambiguous. A lacking detail is, for example, information on thedistribution of in- and outgoing connections, resulting in substantial differences in networkdynamics. For reproducible research, unambiguous network descriptions and correspondingalgorithmic implementations are necessary [1]. Here, we review simulation software (e.g., NEST[2]), specification languages (e.g., CSA [3]), and published network models made available by thecommunity in databases like ModelDB [4] and Open Source Brain [5]. We investigate the networkstructures computational neuroscientists use in their models and the terminology they use todescribe these models. From this, we derive a set of connectivity concepts providing modelers withguidelines to specify connectivity in a complete and concise way. Furthermore, this work aims toguide the comprehensive and efficient implementation of connection routines in simulationsoftware like NEST, thereby facilitating reproducible research on network models.$\qquad$References: $\qquad$1. Nordlie E, et al. (2009) Towards Reproducible Descriptions of Neuronal Network Models. PLoS Comput Biol. 5(8): e1000456. doi:10.1371/journal.pcbi.1000456 $\qquad$2. Gewaltig M-O and Diesmann M (2007). NEST (NEural Simulation Tool). Scholarpedia. 2(4):1430, doi:10.4249/scholarpedia.1430 $\qquad$3. Djurfeldt M (2012) The Connection-set Algebra—A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models. Neuroinform. 10:287–304. doi:10.1007/s12021-012-9146-1 $\qquad$4. ModelDB [https://senselab.med.yale.edu/modeldb] $\qquad$5. Gleeson P, Cantarelli M, Marin B, . . . van Albada SJ, van Geit W, R Silver RA (in press) Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits. Neuron.
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