Ted Carnevale

TL;DR: In this paper, a review of different aspects of the simulation of spiking neural networks is presented, with the aim of identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking networks.

TL;DR: A future for neuroscience is predicted increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.

TL;DR: This work presents the basic ideas that would help informed users make the most efficient use of NEURON, the powerful and flexible environment for implementing models of individual neurons and small networks of neurons.

TL;DR: The neuroscience gateway (NSG) provides a streamlined environment for uploading models, specifying HPC job parameters, querying running job status, receiving job completion notices, and storing and retrieving output data.

TL;DR: This work provides a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin–Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies.