computer science

Random neural network

Random neural network is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Random neural network rather than just read about it. In short: The Random Neural Network (RNN) is a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals. It was invented by Erol Gelenbe and is linked to the G-network model of queueing networks which Erol Gelenbe also invented, and with his Gene Regulatory Network models.

Key takeaways

Random neural network belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Random neural network to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Random neural network from memory before moving on to harder problems.

Reference excerpt

The Random Neural Network (RNN) is a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals. It was invented by Erol Gelenbe and is linked to the G-network model of queueing networks which Erol Gelenbe also invented, and with his Gene Regulatory Network models. In this model, each neuronal 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. The learning algorithm for an n-node random neural network that includes feedback loops (it is also a recurrent neural network) is of computational complexity O(n^3) (the number of computations is proportional to the cube of n, the number of neurons). The random neural network can also be used with other learning algorithms such as reinforcement learning. The RNN has been shown to be a universal approximator for bounded and continuous functions.

See also Linear-nonlinear-Poisson cascade model

References and sources References

Sources E. Gelenbe, Random neural networks with negative and positive signals and product form solution, Neural Computation, vol. 1, no. 4, pp. 502–511, 1989. E. Gelenbe, Stability of the random neural network model, Neural Computation, vol. 2, no. 2, pp. 239–247, 1990. E. Gelenbe, A. Stafylopatis, and A. Likas, Associative memory operation of the random network model, in Proc. Int. Conf. Artificial Neural Networks, Helsinki, pp. 307–312, 1991. E. Gelenbe, F. Batty, Minimum cost graph covering with the random neural network, Computer Science and Operations Research, O. Balci (ed.), New York, Pergamon, pp. 139–147, 1992. E. Gelenbe, Learning in the recurrent random neural network, Neural Computation, vol. 5, no. 1, pp. 154–164, 1993. E. Gelenbe, V. Koubi, F. Pekergin, Dynamical random neural network approach to the traveling salesman problem, Proc. IEEE Symp. Syst., Man, Cybern., pp. 630–635, 1993. E. Gelenbe, C. Cramer, M. Sungur, P. Gelenbe "Traffic and video quality in adaptive neural compression", Multimedia Systems, 4, 357–369, 1996. C. Cramer, E. Gelenbe, H. Bakircioglu Low bit rate video compression with neural networks and temporal sub-sampling, Proceedings of the IEEE, Vol. 84, No. 10, pp. 1529–1543, October 1996. E. Gelenbe, T. Feng, K.R.R. Krishnan Neural network methods for volumetric magnetic resonance imaging of the human brain, Proceedings of the IEEE, Vol. 84, No. 10, pp. 1488–1496, October 1996. E. Gelenbe, A. Ghanwani, V. Srinivasan, "Improved neural heuristics for multicast routing", IEEE J. Selected Areas in Communications, 15, (2), 147–155, 1997. E. Gelenbe, Z. H. Mao, and Y. D. Li, "Function approximation with the random neural network", IEEE Trans. Neural Networks, 10, (1), January 1999. E. Gelenbe, J.M. Fourneau '"Random neural networks with multiple classes of signals", Neural Computation, 11, 721–731, 1999. Ugur Halici "Reinforcement learning with internal expectation for the random neural network", European Journal of Operational Research 126 (2): 288–307, 2000. Aristidis Likas, Andreas Stafylopatis "Training the random neural network using quasi-Newton methods", European Journal of Operational Research 126 (2): 331–339, 2000. Samir Mohamed, Gerardo Rubino, Martín Varela "Performance evaluation of real-time speech through a packet network: a random neural networks-based approach", Perform. Eval. 57 (2): 141–161, 2004. E. Gelenbe, Z.-H. Mao and Y-D. Li "Function approximation by random neural networks with a bounded number of layers", 'Differential Equations and Dynamical Systems', 12 (1&2), 143–170, Jan. April 2004. Gerardo Rubino, Pierre Tirilly, Martín Varela "Evaluating Users' Satisfaction in Packet Networks Using Random Neural Networks", ICANN (1) 2006: 303–312, 2006. Gülay Öke and Georgios Loukas. A denial of service detector based on maximum likelihood detection and the random neural network. Computer Journal, 50(6):717–727, November 2007. S. Timotheou. Nonnegative least squares learning for the random neural network. In Proceedings of the 18th International Conference on Artificial Neural Networks, Prague, Czech Republic, pages 195–204, 2008. S. Timotheou. A novel weight initialization method for the random neural network. In Fifth International Symposium on Neural Networks (ISNN), Beijing, China, 2008. Stelios Timotheou. "The Random Neural Network: A Survey", Comput. J. 53 (3): 251–267, 2010. Pedro Casas, Sandrine Vaton. "On the use of random neural networks for traffic matrix estimation in large-scale IP networks", IWCMC 2010: 326–330, 2010. S. Basterrech, G. Rubino, "Random Neural Network as Supervised Learning Tool," Neural Network World, 25(5), 457-499, doi:10.14311/NNW.2015.25.024, 2015. S. Basterrech, S. Mohamed, G. Rubino, M. Soliman. "Levenberg-Marquardt Training Algorithms for Random Neural Networks," Computer Journal, 54 (1), 125–135, 2011. Michael Georgiopoulos, Cong Li and Taskin Kocak. "Learning in the feed-forward random neural network: A critical review", Performance Evaluation, 68 (4): 361–384, 2011.

Worked examples

Example 1 — a first encounter with Random neural network

Start with the simplest possible case. Write down what Random neural network claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Random neural network before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Random neural network ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Random neural network

In research: Random neural network appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Random neural network in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Random neural network is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial neural networks, Stochastic models, so understanding it makes those chapters shorter.
In everyday life: Look for Random neural network outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Random neural network in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Random neural network means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Random neural network out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Random neural network in simple terms?

Why does Random neural network matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Random neural network?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Random neural network.