Bio-Inspired Computing and Communication: First Workshop on by Jon Crowcroft (auth.), Pietro Liò, Eiko Yoneki, Jon

By Jon Crowcroft (auth.), Pietro Liò, Eiko Yoneki, Jon Crowcroft, Dinesh C. Verma (eds.)

The e-book constitutes the completely refereed post-workshop complaints of the 1st Workshop on Bio-Inspired layout of Networks, BIOWIRE 2007, held in Cambridge, united kingdom, in April 2007.

The 35 revised complete papers awarded have been conscientiously reviewed and chosen from many prime quality submissions. All contemporary advancements within the box of bio-inspired layout of networks are addressed, with specific regard to instant networks and the self-organizing homes of organic networks. The papers are geared up in topical sections on organic networks, community epidemics, advanced networks, bio-inspired community mode, community protocol in instant communique, facts administration, dispensed computing, and security.

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Additional resources for Bio-Inspired Computing and Communication: First Workshop on Bio-Inspired Design of Networks, BIOWIRE 2007 Cambridge, UK, April 2-5, 2007 Revised Selected Papers

Example text

30 E. Gelenbe These results do not allow us to compute the successive states that a network of agents will enter into in the course of time. Suppose that we consider that a regulatory network acts as the control system of a “biochemical nano-factory”, with the rate of production of certain compounds being determined by the probability that certain sets of agents are activated. Then our analysis would enable the computation of the rate of production of these compounds over a period of time. When the system being considered has some form of cyclic behaviour, with agents being successively activated and deactivated through their interactions, then our analysis can provide two things (a) the proportion of time that each agent or each combination of agents is in some combination of active or inactive states, and (b) by fixing the state of some of the agents in the right-hand-side of expressions of the form (24) or (32), we can predict the state of some of the agents when other agents’ states are known.

For example, in ecology, the corresponding terms are “mechanistic” versus “phenomenological” models (Nathan and Muller-Landau, 2000). Mechanistic models attempt to simulate mechanisms underlying key behaviours, whereas phenomenological models are more concerned with replicating the behaviours, irrespective of the actual mechanisms that may generate those behaviours. A valid criticism against simplifying models is often that they are “biologically implausible” and, hence, do not tell us how the brain solves the task.

This back-propagation of error is unlikely to occur in neural systems, and hence this learning algorithm is validly regarded as biologically implausible. However, if one regards this learning algorithm as simply a way of training a network, we can still explore the properties of the network, and compare them with human performance. For example, during training there is a stage-like progression of behaviour, seen in young children, where an early babbling-phase can be distinguished from the later, more-refined, performance.

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