The polychronic economy

Eugene M. Izhikevich's stimulating paper on polychronization (Izhikevich, 2006) describes a number of neuro-computational properties observed in simulations of neural networks based on his computationally tractable model of the neuron (Izhikevich, 2003). ‘Polychronization’ is coined by Izhikevich to refer to the reproducible time-locked (but not synchronous) firing patterns that occur in networks of ‘spiking cortical neurons’. Imagine a juggler juggling three balls. The release of a ball as it rises from the juggler's hand is neither synchronized with the release of the other two nor random; throws must simply occur at precisely the right time relative to each other. Polychronization is like neurons juggling: it is the effect of unsynchronized neuron firings that cascade because they are timed correctly with respect to each other. Santiago Ramón y Cajal was the first to identify the neuron as the ‘primary functional unit’ of the human nervous system, an insight that created the modern discipline of neuroscience and for which he won the 1906 Nobel prize.1 With Turing unborn, and the computer many years' distant, the possibility of modelling the neuron's function lay far into the future. Today it is very different: with a multitude of excellent simulation tools, complex simulations are possible, some might even say easy. Now it's perfecting the detail of the model that's the challenge. Enter Izhikevich with his neuron model – its computationally tractable firing behaviour allows simulations of tens of thousands of neurons with millions of synaptic connections in real time on an ordinary PC. Moreover, by altering his model's parameters, there arise neural firing patterns that closely resemble those observed in the rat's motor cortex. And that is where we come in: they are also familiar in closely resembling the different ways that the products of engineered manufacturing processes sustain a modern economy. Rogers' definition of engineering (see Editorial 26 (1)) leads, quite naturally as it happens, to the representation of an engineering problem as a triple (Hall & Rapanotti, 2008) consisting of an environment, a solution and a need. In combination, the environment's elements are used to engineer a solution to meet a need. As illustration, consider a world that needs, say, potato peelers: in an environment in which suitable raw materials, skills and finance exist a solution can be engineered that produces potato peelers. If the environment, say, lacks the ability to sharpen the potato peeler blade – and that ability isn't part of the engineered solution – then any need for potato peelers may go unfulfilled. Otherwise, the environment is adequate, and solutions, such as potato peelers or, more generally, products, services or whatever, appear in the environment of other problems – unsynchronized but arriving at the right time – to drive their solutions. In seeing it this way, perhaps there is an inkling of a suspicion of an analogy between an engineering problem and a neuron that can link Izhikevich's spiking neuron model to a polychronic economy. Can an economy be said to be a polychronic brain of sorts with engineered solution processes playing the role of neurons? Does the economy have brain-like activity? Does it rest, work and play? Does it become exuberant? And is the global crash simply a symptom that the economy is currently suffering from a depressive disorder? To fix the analogy in science is a matter of playing with Izhikevich's model to loosen – or, better, scale – the timescales over which it reacts and to accommodate the changing overlaps between problem solvers in the global economy. The benefit? A better understanding of the economy could bring better models for economic management. Perhaps a ‘happier, more satisfied’ economy can be built using tools for the analysis and synthesis of polychrony. And, even if they already contribute greatly, knowledge engineering and expert systems would have much more to contribute. It's a no brainer, really. It is a great pleasure to welcome a new board member: Dr Desheng Dash Wu has joined the board as an active associate editor. Dash is a tenured assistant professor at Reykjavik University, an affiliated professor at RiskLab and director of the RiskChina Research Center, both at the University of Toronto. His research interests focus on enterprise risk management, performance evaluation and business decision support. His work has appeared in journals including the International Journal of Production Research, the IEEE Transactions on Knowledge and Data Engineering, the Journal of the Operational Research Society, the European Journal of Operational Research, the Annals of Operations Research, the International Journal of Production Economics and the International Journal of System Science, to name but a few. He has two books to his name, has served as editor and guest editor for several journals, and has chaired many conferences. He is also a member of the Professional Risk Managers' International Association's Academic Advisory Committee and is a steering committee member. Dash is currently preparing a special issue for Expert Systems, and his work as a guest editor on this issue has identified him as someone who will contribute much as an associate editor. We have a bumper crop of six papers this month. In ‘Extracting new patterns for cardiovascular disease prognosis’, Mena, Gonzalez and Maestre identify abnormal blood pressure variability as a cardiovascular risk factor and assess its ability to classify sick people correctly with other, more traditional algorithms. In ‘Colon segmentation and colonic polyp detection using cellular neural networks and three-dimensional template matching’ by Kilic, Ucan and Osman the authors describe an improved detection system using three heuristic optimization techniques. The third paper by Kong, Xu, Liu and Yang, ‘Applying a belief rule-base inference methodology to a guideline-based clinical decision support system’, contributes to the debate of how one arrives at an accurate clinical decision in the light of uncertain medical domain knowledge and clinical symptoms. Through a case study, the paper applies the generic rule-base inference methodology using evidential reasoning (RIMER) approach to model clinical guidelines and clinical inference processes in a clinical decision support system. Moving on to the use of ontologies within service-oriented architectures (or SOAs), Goodwin and Russomanno, in their paper ‘Ontology integration within a service-oriented architecture for expert system applications using sensor networks’, describe how ontology-enhanced search can better match discovered services to requests. Back to clinical uses of expert systems with work on the classification of Pap smear cells by Marinakis, Marinaki, Dounias, Jantzen and Bjerregaard in their paper ‘Intelligent and nature inspired optimization methods in medicine: the Pap smear cell classification problem’. Tested on two large data sets, the methods used include tabu search, genetic algorithms and particle swarm and ant colony optimizations. The last paper of Volume 26 is entitled ‘Unlabelled extra data do not always mean extra performance for semi-supervised fault prediction’. In it, Catal and Diri investigate and benchmark a number of high performance classifiers for software fault prediction with limited fault data. With each employed within YATSI (Yet Another Two Stage Idea), a meta algorithm, the authors show that the YATSI versions may improve on naive Bayes for large data sets.

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