Etienne Dumesnil

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A robot is presented whose behavior is based on two fundamental types of learning in the animal world: Classical Conditioning (CC) and Operant Conditioning (OC). It is shown how both share Spike-Timing-Dependent-Plasticity (STDP) as learning process for a Spiking Neural Network (SNN). STDP was implemented on a Field-Programmable Gate Array (FPGA) with very(More)
We describe a method for circuit synthesis that determines the parameter values by using a set of artificial neural networks (ANNs) that learn in sequence. Each ANN is optimized to output only one design parameter, and the latter constrains the learning/recall of its successor(s). Two competing ANN architectures are considered, the multilayer perceptron(More)
This work presents the implementation of operant conditioning (OC) and classical conditioning (CC) with a single spiking neural network (SNN) architecture, thus suggesting that the two types of leaning may relate to the same cognitive process. Both are achieved by using a modified version of spike-timing-dependent plasticity (STDP), where the connection(More)
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