DYNAMICAL EVENT NEURON AND SYNAPSE MODELS FOR LEARNING SPIKING NEURAL NETWORKS
Certain aspects of the present disclosure support a technique for updating the state of an artificial neuron. A first state of the artificial neuron can be first determined, wherein a neuron model for the artificial neuron has a closed-form solution in continuous time and wherein state dynamics of t...
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| creator | HUNZINGER JASON FRANK |
| description | Certain aspects of the present disclosure support a technique for updating the state of an artificial neuron. A first state of the artificial neuron can be first determined, wherein a neuron model for the artificial neuron has a closed-form solution in continuous time and wherein state dynamics of the neuron model are divided into two or more regimes. An operating regime for the artificial neuron can be determined based, at least in part, on the first state. The state of the artificial neuron can be updated based, at least in part, on the first state of the artificial neuron and the determined operating regime. |
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A first state of the artificial neuron can be first determined, wherein a neuron model for the artificial neuron has a closed-form solution in continuous time and wherein state dynamics of the neuron model are divided into two or more regimes. An operating regime for the artificial neuron can be determined based, at least in part, on the first state. 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A first state of the artificial neuron can be first determined, wherein a neuron model for the artificial neuron has a closed-form solution in continuous time and wherein state dynamics of the neuron model are divided into two or more regimes. An operating regime for the artificial neuron can be determined based, at least in part, on the first state. The state of the artificial neuron can be updated based, at least in part, on the first state of the artificial neuron and the determined operating regime.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | DYNAMICAL EVENT NEURON AND SYNAPSE MODELS FOR LEARNING SPIKING NEURAL NETWORKS |
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