org). Use of digital reconstructions in morphologically and biophysically realistic simulations and network models allows direct investigation of the neuronal structure-activity-function relation.

The following is an overview of currently available modeling software, all of which are free and typically open source. The first four programs are for morphological modeling (marked with “∗∗∗” in Table 1). The remaining are for biophysical simulations of neuronal this website electrophysiology. 1. L-Neuron is a computational tool to generate anatomically accurate virtual neurons of various morphological classes. L-Neuron resamples statistical distributions extracted from experimental data to generate virtual neurons according to algorithms that implement established anatomical rules. The

3D morphological models can be visualized with the companion L-Viewer program and exported into classic graphic formats (bitmap, VRML, DXF, POV, and Blob) or standard SWC format for morphometric analysis and electrophysiological simulations. Online documentation and e-mail user support are available. Actively maintained but no longer developed, L-Neuron is written in C/C++ and runs on Windows and Linux. The source code is available upon request bundled within L-Measure. The above list does not include the numerous published computational models of neuronal morphology (e.g., Samsonovich and Dasatinib concentration Ascoli, 2005; López-Cruz et al., 2011), as this compilation focuses on research tools available to the scientific community as opposed to individual custom solutions restricted GBA3 to the domain of a single laboratory. The software programs listed for computational modeling of neuron electrophysiology include resources for circuit simulation but always with the ability of representing

dendritic and axonal morphology. Various other neural network simulators largely or exclusively consider point neurons as the elementary unit of computation, sacrificing neuron-level realism for larger-scale modeling. For example, PCSIM (Parallel neural Circuit SIMulator; http://www.lsm.tugraz.at/pcsim) allows parallel simulation of large-scale heterogeneous spiking and analog neural networks with up to millions of different point neurons and billions of synapses. Emergent (http://grey.colorado.edu/emergent) models neural dynamics at the level of activity rates. Topographica (http://topographica.org) focuses on modeling activity in cortical maps. CNS (Cortical Network Simulator; http://cbcl.mit.edu/jmutch/cns) is designed specifically for graphical processing units (GPUs). Other neural network simulators include CNRun (http://johnhommer.com/academic/code/cnrun), LENS (http://tedlab.mit.edu/∼dr/Lens), Nodus (http://www.tnb.ua.ac.be/software/nodus/nodus_info.shtml), Simbrain (http://simbrain.net), and NEST (Neural Simulation Technology; http://nest-initiative.org). PyNN (http://neuralensemble.