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- The Galves–Löcherbach model (or GL model) is a mathematical model for a network of neurons with intrinsic stochasticity. In the most general definition, a GL network consists of a countable number of elements (idealized neurons) that interact by sporadic nearly-instantaneous discrete events (spikes or firings). At each moment, each neuron N fires independently, with a probability that depends on the history of the firings of all neurons since the last time N last fired. Thus each neuron "forgets" all previous spikes, including its own, whenever it fires. This property is a defining feature of the GL model. In specific versions of the GL model, the past network spike history since the last firing of a neuron N may be summarized by an internal variable, the potential of that neuron, that is a weighted sum of those spikes. The potential may include the spikes of only a finite subset of other neurons, thus modeling arbitrary synapse topologies. In particular, the GL model includes as a special case the general leaky integrate-and-fire neuron model. (en)
- O Modelo Galves-Löcherbach é um modelo com estocasticidade intrínseca para redes de neurônios, no qual a probabilidade de disparos futuros é dependente da evolução total do sistema desde o último disparo. Esse modelo de redes neurais foi desenvolvido pelos matemáticos Antonio Galves e . No artigo original, de 2013, os autores chamaram o modelo de "sistema de cadeias estocásticas com memória de alcance variável interagindo entre si". (pt)
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- O Modelo Galves-Löcherbach é um modelo com estocasticidade intrínseca para redes de neurônios, no qual a probabilidade de disparos futuros é dependente da evolução total do sistema desde o último disparo. Esse modelo de redes neurais foi desenvolvido pelos matemáticos Antonio Galves e . No artigo original, de 2013, os autores chamaram o modelo de "sistema de cadeias estocásticas com memória de alcance variável interagindo entre si". (pt)
- The Galves–Löcherbach model (or GL model) is a mathematical model for a network of neurons with intrinsic stochasticity. In the most general definition, a GL network consists of a countable number of elements (idealized neurons) that interact by sporadic nearly-instantaneous discrete events (spikes or firings). At each moment, each neuron N fires independently, with a probability that depends on the history of the firings of all neurons since the last time N last fired. Thus each neuron "forgets" all previous spikes, including its own, whenever it fires. This property is a defining feature of the GL model. (en)
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- Galves–Löcherbach model (en)
- Modelo Galves-Löcherbach (pt)
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