Computing with spikes, architecture, properties and implementation of emerging paradigms
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Summary
This thesis proposes an efficient method to properly estimate the parameters (delayed synaptic weights) of a neural network from the observa- tion of its spiking dynamics, and proposes a C++ library, called EnaS, which is distributed under the CeCILL-C free license.
- Type
- preprint
- Published
- 2011-01-24
- Cited by
- 2
- References
- 120
- Access
- Open access
- OpenAlex
- https://openalex.org/W86059165
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11833763
Keywords
Computer science, Artificial neural network, Robustness (evolution), Implementation, Spiking neural network
References
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- Implementing a Multi-Model Estimation Method
- On Use of the EM Algorithm for Penalized Likelihood Estimation
- Computer arithmetic - algorithms and hardware designs
- Theory of Cortical Plasticity
- Pulsed Neural Networks
- Advances in kernel methods: support vector learning
- Nature and precision of temporal coding in visual cortex: a metric-space analysis.
- TestU01: A C library for empirical testing of random number generators
- A Note on the Generation of Random Normal Deviates
- A Fast p Spike Alignment Metric
- Dynamic neural field with local inhibition
- Efficient implementation of piecewise linear activation function for digital VLSI neural networks
- Piecewise linear approximation applied to nonlinear function of a neural network
- Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit
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