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• 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.".
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• Random_neural_network wikiPageRevisionID "611126932".
• Random_neural_network wikiPageWikiLink Action_potential.
• Random_neural_network wikiPageWikiLink Algorithm.
• Random_neural_network wikiPageWikiLink Artificial_neural_network.
• Random_neural_network wikiPageWikiLink Bounded_function.
• Random_neural_network wikiPageWikiLink Category:Artificial_neural_networks.
• Random_neural_network wikiPageWikiLink Category:Stochastic_models.
• Random_neural_network wikiPageWikiLink Cell_(biology).
• Random_neural_network wikiPageWikiLink Continuous_function.
• Random_neural_network wikiPageWikiLink Erol_Gelenbe.
• Random_neural_network wikiPageWikiLink G-network.
• Random_neural_network wikiPageWikiLink Joint_probability_distribution.
• Random_neural_network wikiPageWikiLink Krishna_Palem.
• Random_neural_network wikiPageWikiLink Linear-nonlinear-Poisson_cascade_model.
• Random_neural_network wikiPageWikiLink Machine_learning.
• Random_neural_network wikiPageWikiLink Neural_network.
• Random_neural_network wikiPageWikiLink Neuron.
• Random_neural_network wikiPageWikiLink Nonlinear_system.
• Random_neural_network wikiPageWikiLink Nonlinearity.
• Random_neural_network wikiPageWikiLink PCMOS.
• Random_neural_network wikiPageWikiLink Parameter.
• Random_neural_network wikiPageWikiLink Reinforcement_learning.
• Random_neural_network wikiPageWikiLinkText "Random neural network".
• Random_neural_network wikiPageWikiLinkText "random neural network".
• Random_neural_network hasPhotoCollection Random_neural_network.
• Random_neural_network wikiPageUsesTemplate Template:Page_needed.
• Random_neural_network wikiPageUsesTemplate Template:Reflist.
• Random_neural_network subject Category:Artificial_neural_networks.
• Random_neural_network subject Category:Stochastic_models.
• Random_neural_network hypernym Representation.
• Random_neural_network type Article.
• Random_neural_network type Model.
• Random_neural_network type Article.
• Random_neural_network type Model.
• Random_neural_network type Network.
• Random_neural_network type Process.
• 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 wasDerivedFrom Random_neural_network?oldid=611126932.
• Random_neural_network isPrimaryTopicOf Random_neural_network.