Vector-Matrix Models of Pulse Neuron for Digital Signal Processing

Author:

Bondarev Vladimir

Publisher

Springer International Publishing

Reference14 articles.

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2. Maass, W.: Paradigms for computing with spiking neurons. In: van Hemmen, J.L., Cowan, J.D., Domany, E. (eds.) Models of Neural Networks. Early Vision and Attention, vol. 4, pp. 373–402. Springer, New York (2002)

3. Gerstner, W., Kistler, W.M.: Spiking Neuron Models – Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)

4. Natschlaeger, T., Ruf, B.: Spatial and temporal pattern analysis via spiking neurons. Netw.: Comput. Neural Syst. 9, 319–332 (1998)

5. Ponulak, F., Kasiński, A.: Introduction to spiking neural networks: information processing, learning and applications. Acta Neurobiol. Exp. 71, 409–433 (2011)

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1. Training a digital model of a deep spiking neural network using backpropagation;E3S Web of Conferences;2020

2. Blind signal deconvolution based on pulsed neuron model;ITM Web of Conferences;2019

3. Linear Filtering Based on a Pulsed Neuron Model with an Orthogonal Filter Bank;Advances in Neural Computation, Machine Learning, and Cognitive Research II;2018-10-05

4. Pulse Neuron Supervised Learning Rules for Adapting the Dynamics of Synaptic Connections;Advances in Neural Networks – ISNN 2018;2018

5. Pulse Neuron Learning Rules for Processing of Dynamical Variables Encoded by Pulse Trains;Advances in Neural Computation, Machine Learning, and Cognitive Research;2017-08-29

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