Mar 22, 2019 · Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i.e. rules that update synapses ...
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We employ genetic algorithms to evolve delayed synaptic plasticity (DSP) rules and perform synaptic updates based on NATs and delayed reinforcement signals. We ...
ABSTRACT. The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their configuration.
We employ genetic algorithms to evolve delayed synaptic plasticity (DSP) rules and perform synaptic updates based on NATs and delayed reinforcement signals. We ...
Jul 13, 2019 · We employ genetic algorithms to evolve delayed synaptic plasticity (DSP) rules and perform synaptic updates based on NATs and delayed ...
Every neuron in the network is connected to 20% of the others, with delays drawn from a uniform distribution between 0 and 5 ms. Within the network, all the ...
Many methods have been proposed for adapting propagation delays, inspired by spike timing dependent plasticity (Wang et al., 2013) or based on the ReSuMe ...
Oct 7, 2020 · This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method ...
Sep 6, 2024 · We derive synaptic update rules for this model by framing synaptic plasticity as an optimal control problem. This leads to substantial ...
Mar 26, 2021 · We propose a modified supervised learning algorithm for optical spiking neural networks, which introduces synaptic time-delay plasticity on the ...