A framework for application-oriented design of large-scale neural networks

Authors:
David Bouchain;Florian Hauser;Günther Palm
Affiliations:
Institute of Neural Information Processing, Ulm University, Ulm, Germany;Institute of Neural Information Processing, Ulm University, Ulm, Germany;Institute of Neural Information Processing, Ulm University, Ulm, Germany
Venue:
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Year:
2011

Citing 3
Cited 0

The NEURON simulation environment

Neural Computation
The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System

The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
Neural Assemblies, an Alternative Approach to Artificial Intelligence

Neural Assemblies, an Alternative Approach to Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Tools for simulations of neural networks exist aplenty. They range from simulators for detailed multi-compartment neurons, over packages for precise reconstruction of small biological networks, to simulators for large-scale networks with stochastic connectivity properties. However, no frameworks for constructing large-scale, dedicated networks exist. Based on the design principles used for our previous work, we introduce a C++ framework which is specifically tailored to simplify the construction of large networks with specific cognitive functionalities.