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<https://discovery.ucl.ac.uk/id/eprint/10080543> <http://purl.org/dc/terms/title> "Normalisation of Weights and Firing Rates in Spiking Neural Networks with Spike-Timing-Dependent Plasticity"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10080543> <http://purl.org/ontology/bibo/abstract> "Maintaining the ability to fire sparsely is crucial for infor- mation encoding in neural networks. Additionally, spiking homeostasis is vital for spiking neural networks with chang- ing numbers of weights and neurons. We discuss a range of network stabilisation approaches, inspired by homeostatic synaptic plasticity mechanisms reported in the brain. These include weight scaling, and weight change as a function of the network’s spiking activity. We tested normalisation of the sum of weights for all neurons, and by neuron type. We ex- amined how this approach affects firing rate and performance on clustering of time-series data in the form of moving geo- metric shapes. We found that neuron type-specific normali- sation is a promising approach for preventing weight drift in spiking neural networks, thus enabling longer training cycles. It can be adapted for networks with architectural plasticity."^^<http://www.w3.org/2001/XMLSchema#string> .
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