Matches in DBpedia 2014 for { <http://dbpedia.org/resource/Random_neural_network> ?p ?o. }
Showing items 1 to 27 of
27
with 100 items per page.
- Random_neural_network abstract "The random neural network (RNN) is a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals that was invented by Erol Gelenbe and is linked to the G-network model of queueing networks as well as to Gene Regulatory Network models. Each cell state is represented by an integer whose value rises when the cell receives an excitatory spike and drops when it receives an inhibitory spike. The spikes can originate outside the network itself, or they can come from other cells in the networks. Cells whose internal excitatory state has a positive value are allowed to send out spikes of either kind to other cells in the network according to specific cell-dependent spiking rates. The model has a mathematical solution in steady-state which provides the joint probability distribution of the network in terms of the individual probabilities that each cell is excited and able to send out spikes. Computing this solution is based on solving a set of non-linear algebraic equations whose parameters are related to the spiking rates of individual cells and their connectivity to other cells, as well as the arrival rates of spikes from outside the network. The RNN is a recurrent model, i.e. a neural network that is allowed to have complex feedback loops.A highly energy-efficient implementation of Random Neural Networks was demonstrated by Krishna Palem et al. using the Probabilistic CMOS or PCMOS technology and was shown to be c. 226–300 times more efficient in terms of Energy-Performance-Product.RNNs are also related to Artificial neural networks, which (like the random neural network) have gradient-based learning algorithms whose computational complexity is proportional to the cube of the number of cells, and other learning algorithms such as reinforcement learning can also be used. Such approaches have been shown to be universal approximators for bounded and continuous functions.".
- Random_neural_network wikiPageID "8278198".
- Random_neural_network wikiPageRevisionID "571075665".
- Random_neural_network hasPhotoCollection Random_neural_network.
- Random_neural_network subject Category:Neural_networks.
- Random_neural_network subject Category:Stochastic_models.
- Random_neural_network type Assistant109815790.
- Random_neural_network type CausalAgent100007347.
- Random_neural_network type LivingThing100004258.
- Random_neural_network type Model110324560.
- Random_neural_network type Object100002684.
- Random_neural_network type Organism100004475.
- Random_neural_network type Person100007846.
- Random_neural_network type PhysicalEntity100001930.
- Random_neural_network type StochasticModels.
- Random_neural_network type Whole100003553.
- Random_neural_network type Worker109632518.
- Random_neural_network type YagoLegalActor.
- Random_neural_network type YagoLegalActorGeo.
- Random_neural_network comment "The random neural network (RNN) is a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals that was invented by Erol Gelenbe and is linked to the G-network model of queueing networks as well as to Gene Regulatory Network models. Each cell state is represented by an integer whose value rises when the cell receives an excitatory spike and drops when it receives an inhibitory spike.".
- Random_neural_network label "Random neural network".
- Random_neural_network sameAs m.026yrsf.
- Random_neural_network sameAs Q7291980.
- Random_neural_network sameAs Q7291980.
- Random_neural_network sameAs Random_neural_network.
- Random_neural_network wasDerivedFrom Random_neural_network?oldid=571075665.
- Random_neural_network isPrimaryTopicOf Random_neural_network.