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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 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 present a full custom hardware implementation of a deep neural network, built using multiple neuromorphic VLSI devices that integrate analog neuron and synapse circuits together with digital asynchronous logic circuits. The deep network comprises an event-based convolutional stage for feature extraction connected to a spike-based learning stage for feature classification. We describe the properties...
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