Name
Abstract | ||
|---|---|---|
Lovelace and Cios (2008) recently proposed a very simple spiking neuron (VSSN) model for simulations of large neuronal networks as an efficient replacement for the integrate-and-fire neuron model. We argue that the VSSN model falls behind key advances in neuronal network modeling over the past 20 years, in particular, techniques that permit simulators to compute the state of the neuron without repeated summation over the history of input spikes and to integrate the subthreshold dynamics exactly. State-of-the-art solvers for networks of integrate-and-fire model neurons are substantially more efficient than the VSSN simulator and allow routine simulations of networks of some 105 neurons and 109 connections on moderate computer clusters. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1162/neco.2008.03-08-731 | Neural Computation |
Keywords | Field | DocType |
neuronal network | Biological neuron model,Computer science,Artificial intelligence,Subthreshold conduction,Neuron,Winner-take-all,Spiking neural network,Biological neural network,Machine learning,Computer cluster | Journal |
Volume | Issue | ISSN |
21 | 2 | 0899-7667 |
Citations | PageRank | References |
11 | 0.72 | 23 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hans E Plesser | 1 | 133 | 20.47 |
| Markus Diesmann | 2 | 1692 | 129.76 |