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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 weight between a cue neuron and an action neuron...
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 low-demanding resources, using an adaptation...
We show how a minimal components requirement and very low resource demanding field-programmable gate array (FPGA) implementation of an adapted version of the synapto-dendritic Kernel Adapting Neuron (SKAN) model can be used to underlie two of the most basic learning processes: classical conditioning (CC) and operant conditioning (OC). In the CC architecture, this adapted SKAN model is used in a spiking...
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 (MLP) and the radial basis functions (RBF) network,...
A genetic algorithm (GA) was used to determine the optimal architecture and input parameters of a feed-forward artificial neural network (ANN), the purpose of which was to synthesize a radio-frequency, low noise amplifier (RF-LNA) circuit. The parameters (chromosomes) processed by the GA included: i) the LNA performance specifications and design constraints; ii) the type of ANN to use multi-layer...
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