page_title: Nervous system network models
section_title: Artificial neural networks
hierarchical_section_title: Nervous system network models / Nervous system models / Artificial neural networks
caption_attribution_description: English: Basic telecommunication network
context_page_description: Network of human nervous system comprises nodes that are connected by links. The connectivity may be viewed anatomically, functionally, or electrophysiologically. These are presented in several Wikipedia articles that include Connectionism, Biological neural network, Artificial neural network, Computational neuroscience, as well as in several books by Ascoli, G. A., Sterratt, D., Graham, B., Gillies, A., & Willshaw, D., Gerstner, W., & Kistler, W., and Rumelhart, J. L., McClelland, J. L., and PDP Research Group among others. The focus of this article is a comprehensive view of modeling a neural network. Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic, mesoscopic, or macroscopic levels. Computational modeling refers to models that are developed using computing tools.
context_section_description: As mentioned in Section 2.4, development of artificial neural network (ANN), or neural network as it is now called, started as simulation of biological neuron network and ended up using artificial neurons. Major development work has gone into industrial applications with learning process. Complex problems were addressed by simplifying the assumptions. Algorithms were developed to achieve a neurological related performance, such as learning from experience. Since the background and overview have been covered in the other internal references, the discussion here is limited to the types of models. The models are at the system or network level.
  The four main features of an ANN are topology, data flow, types of input values, and forms of activation (Meireles, M. R. G. (2003), Munakata, T. (1998)). Topology can be multilayered, single-layered, or recurrent. Data flow can be recurrent with feedback or non-recurrent with feedforward model. The inputs are binary, bipolar, or continuous. The activation is linear, step, or sigmoid. Multilayer Perceptron (MLP) is the most popular of all the types, which is generally trained with back-propagation of error algorithm. Each neuron output is connected to every neuron in subsequent layers connected in cascade and with no connections between neurons in the same layer. Figure 4 shows a basic MLP topology (Meireles, M. R. G. (2003)), and a basic telecommunication network (Subramanian, M. (2010)) that most are familiar with. We can equate the routers at the nodes in telecommunication network to neurons in MLP technology and the links to synapses.
caption: Figure 4(a)Basic telecommunication network
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