Name
Title | ||
|---|---|---|
A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function |
Abstract | ||
|---|---|---|
Spiking neural networks --- networks that encode information in the timing of spikes --- are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters --- more that 15 --- to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-88190-2_28 | SBIA |
Keywords | Field | DocType |
cognitive science,spiking neural network,radial basis function,artificial neural network,neural network,propagation delay | Feedforward neural network,Radial basis function network,Random neural network,Computer science,Recurrent neural network,Types of artificial neural networks,Time delay neural network,Artificial intelligence,Spiking neural network,Catastrophic interference,Machine learning | Conference |
Volume | ISSN | Citations |
5249 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandre da Silva Simões | 1 | 2 | 2.15 |
| Anna Helena Reali Costa | 2 | 192 | 31.97 |