neural networks, recurrently-connected, time delays, time constants
Recurrently-connected spiking neural networks are difficult to use and understand because of the complex nonlinear dynamics of the system. Through empirical studies of spiking networks, we deduce several principles which are critical to success. Network parameters such as synaptic time delays and time constants and the connection probabilities can be adjusted to have a significant impact on accuracy. We show how to adjust these parameters to fit the type of problem.
Original Publication Citation
Eric Goodman and Dan Ventura, "Effectively Using Recurrently Connected Spiking Neural Networks", Proceedings of the International Joint Conference on Neural Networks, pp. 1542-1547, July 25.
BYU ScholarsArchive Citation
Goodman, Eric and Ventura, Dan A., "Effectively Using Recurrently-Connected Spiking Neural Networks" (2005). All Faculty Publications. 365.
Physical and Mathematical Sciences
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