Computing with spikes: architecture, properties and implementation

 
9783659203947: Computing with spikes: architecture, properties and implementation
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In this work we studied at a practical level how computation with action potentials (spikes) can be performed. We address the problem of programming of a dynamical system modeled as a neural network and considering both, hardware and software implementations. For this, we considered a discrete-time spiking neuron model, which was introduced by Soula et al. in 2006, and called BMS. On one hand, we proposed an efficient method to properly estimate the parameters (delayed synaptic weights) of a neural network from the observation of its spiking dynamics. Thus, the idea was to avoid the underlying NP-complete problem (when both weights and inter-neural transmission delays are considered in the parameters estimation). So far, our method defines a Linear Programming (LP) system to perform the parameters estimation. Another aspect considered in this work was the fact that we included a reservoir computing mechanism (hidden network) as to increase the computational power as to add robustness. Furthermore, these ideas are applied to implement input-output transformation for learning the implicit parameters of the corresponding transfer function.

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About the Author:

Horacio Rostro Gonzalez, Postdoctoral Research Fellow: Neural Engineering at Universiy of Cyprus. Dsc: Computational Neuroscience at University of Nice-Sophia Antipolis. MEng: Instrumentation and Digital Systems at University of Guanajuato. He is currently an Associate Professor within the Department of Electronics at the University of Guanajuato

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Book Description Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | architecture, properties and implementation | In this work we studied at a practical level how computation with action potentials (spikes) can be performed. We address the problem of programming of a dynamical system modeled as a neural network and considering both, hardware and software implementations. For this, we considered a discrete-time spiking neuron model, which was introduced by Soula et al. in 2006, and called BMS. On one hand, we proposed an efficient method to properly estimate the parameters (delayed synaptic weights) of a neural network from the observation of its spiking dynamics. Thus, the idea was to avoid the underlying NP-complete problem (when both weights and inter-neural transmission delays are considered in the parameters estimation). So far, our method defines a Linear Programming (LP) system to perform the parameters estimation. Another aspect considered in this work was the fact that we included a reservoir computing mechanism (hidden network) as to increase the computational power as to add robustness. Furthermore, these ideas are applied to implement input-output transformation for learning the implicit parameters of the corresponding transfer function. | Format: Paperback | Language/Sprache: english | 168 pp. Seller Inventory # K9783659203947

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Book Description LAP Lambert Academic Publishing Aug 2012, 2012. Taschenbuch. Condition: Neu. Neuware - In this work we studied at a practical level how computation with action potentials (spikes) can be performed. We address the problem of programming of a dynamical system modeled as a neural network and considering both, hardware and software implementations. For this, we considered a discrete-time spiking neuron model, which was introduced by Soula et al. in 2006, and called BMS. On one hand, we proposed an efficient method to properly estimate the parameters (delayed synaptic weights) of a neural network from the observation of its spiking dynamics. Thus, the idea was to avoid the underlying NP-complete problem (when both weights and inter-neural transmission delays are considered in the parameters estimation). So far, our method defines a Linear Programming (LP) system to perform the parameters estimation. Another aspect considered in this work was the fact that we included a reservoir computing mechanism (hidden network) as to increase the computational power as to add robustness. Furthermore, these ideas are applied to implement input-output transformation for learning the implicit parameters of the corresponding transfer function. 168 pp. Englisch. Seller Inventory # 9783659203947

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Book Description LAP Lambert Academic Publishing Aug 2012, 2012. Taschenbuch. Condition: Neu. Neuware - In this work we studied at a practical level how computation with action potentials (spikes) can be performed. We address the problem of programming of a dynamical system modeled as a neural network and considering both, hardware and software implementations. For this, we considered a discrete-time spiking neuron model, which was introduced by Soula et al. in 2006, and called BMS. On one hand, we proposed an efficient method to properly estimate the parameters (delayed synaptic weights) of a neural network from the observation of its spiking dynamics. Thus, the idea was to avoid the underlying NP-complete problem (when both weights and inter-neural transmission delays are considered in the parameters estimation). So far, our method defines a Linear Programming (LP) system to perform the parameters estimation. Another aspect considered in this work was the fact that we included a reservoir computing mechanism (hidden network) as to increase the computational power as to add robustness. Furthermore, these ideas are applied to implement input-output transformation for learning the implicit parameters of the corresponding transfer function. 168 pp. Englisch. Seller Inventory # 9783659203947

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Book Description LAP Lambert Academic Publishing Aug 2012, 2012. Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Neuware - In this work we studied at a practical level how computation with action potentials (spikes) can be performed. We address the problem of programming of a dynamical system modeled as a neural network and considering both, hardware and software implementations. For this, we considered a discrete-time spiking neuron model, which was introduced by Soula et al. in 2006, and called BMS. On one hand, we proposed an efficient method to properly estimate the parameters (delayed synaptic weights) of a neural network from the observation of its spiking dynamics. Thus, the idea was to avoid the underlying NP-complete problem (when both weights and inter-neural transmission delays are considered in the parameters estimation). So far, our method defines a Linear Programming (LP) system to perform the parameters estimation. Another aspect considered in this work was the fact that we included a reservoir computing mechanism (hidden network) as to increase the computational power as to add robustness. Furthermore, these ideas are applied to implement input-output transformation for learning the implicit parameters of the corresponding transfer function. 168 pp. Englisch. Seller Inventory # 9783659203947

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Book Description LAP Lambert Academic Publishing, Germany, 2012. Paperback. Condition: New. Aufl.. Language: English . Brand New Book ***** Print on Demand *****.In this work we studied at a practical level how computation with action potentials (spikes) can be performed. We address the problem of programming of a dynamical system modeled as a neural network and considering both, hardware and software implementations. For this, we considered a discrete-time spiking neuron model, which was introduced by Soula et al. in 2006, and called BMS. On one hand, we proposed an efficient method to properly estimate the parameters (delayed synaptic weights) of a neural network from the observation of its spiking dynamics. Thus, the idea was to avoid the underlying NP-complete problem (when both weights and inter-neural transmission delays are considered in the parameters estimation). So far, our method defines a Linear Programming (LP) system to perform the parameters estimation. Another aspect considered in this work was the fact that we included a reservoir computing mechanism (hidden network) as to increase the computational power as to add robustness. Furthermore, these ideas are applied to implement input-output transformation for learning the implicit parameters of the corresponding transfer function. Seller Inventory # AAV9783659203947

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