ESANN 2000 - PDF Preprint Papers

ESANN 2000

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ESANN
2000

402views Neural Networks» more ESANN 2000»

SpikeProp: backpropagation for networks of spiking neurons

13 years 5 months ago

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Abstract. For a network of spiking neurons with reasonable postsynaptic potentials, we derive a supervised learning rule akin to traditional error-back-propagation, SpikeProp and s...

Sander M. Bohte, Joost N. Kok, Johannes A. La Pout...

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Id	Views	Title	Status

1	402	SpikeProp: backpropagation for networks of spiking neurons	claim
2	177	Algorithmic approaches to training Support Vector Machines: a survey	claim
3	175	Analytical comparison of the Temporal Kohonen Map and the Recurrent Self Organizing Map	claim
4	174	Parametric approach to blind deconvolution of nonlinear channels	claim
5	163	Distributed clustering and local regression for knowledge discovery in multiple spatial databases	claim
6	159	Bootstrap for neural model selection	claim
7	152	An algorithm for the addition of time-delayed connections to recurrent neural networks	claim
8	151	Bootstrapping Self-Organizing Maps to assess the statistical significance of local proximity	claim
9	146	Curve forecast with the SOM algorithm: using a tool to follow the time on a Kohonen map	claim
10	145	Load forecasting dealing with medium voltage network reconfiguration	claim
11	143	Automatic detection of clustered microcalcifications in digital mammograms using an SVM classifier	claim
12	141	Learning VOR-like stabilization reflexes in robots	claim
13	139	Evaluating SOMs using order metrics	claim
14	130	Confidence estimation methods for neural networks : a practical comparison	claim
15	129	Discriminative learning for neural decision feedback equalizers	claim
16	125	A neural network approach to adaptive pattern analysis - the deformable feature map	claim
17	120	Nonsynaptically connected neural nets	claim
18	118	Boundary based movement correction of functional MR data using a genetic algorithm	claim
19	118	Committee formation for reliable and accurate neural prediction in industry	claim
20	116	Self-Organisation in the SOM with a finite number of possible inputs	claim
21	113	Iterative learning neural network control for nonlinear system trajectory tracking	claim
22	105	Quantum iterative algorithm for image reconstruction problems	claim
23	104	Nonlinear prediction of spatio-temporal time series	claim
24	100	Regularization in oculomotor control	claim
25	97	A neural network architecture for automatic segmentation of fluorescence micrographs	claim
26	95	Influence of weight-decay training in input selection methods	claim
27	95	The K.U.Leuven competition data: a challenge for advanced neural network techniques	claim
28	95	Local input-output stability of recurrent networks with time-varying weights	claim
29	88	Support Vector Committee Machines	claim
30	87	A comparative design of a MIMO neural adaptive rate damping for a nonlinear helicopter model	claim
31	87	A new information criterion for the selection of subspace models	claim
32	83	A neuro-fuzzy approach as medical diagnostic interface	claim
33	78	A statistical model selection strategy applied to neural networks	claim

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