Abstract. We present a framework for characterizing spike (and spiketrain) synchrony in parallel neuronal spike trains that is based on identifying spikes with what we call influence maps: real-valued functions describing an influence region around the corresponding spike times within which possibly graded synchrony with other spikes is defined. We formalize two models of synchrony in this framework: the bin-based model (the almost exclusively applied model in the literature) and a novel, alternative model based on a continuous, graded notion of synchrony, aimed at overcoming the drawbacks of the bin-based model. We study the task of identifying frequent (and synchronous) neuronal patterns from parallel spike trains in our framework, formalized as an instance of what we call the fuzzy frequent pattern mining problem (a generalization of standard frequent pattern mining) and briefly evaluate our synchrony models on this task.

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This paper presents an automatic method to design interval type-2 fuzzy systems for load forecasting applications using a memetic algorithm. This hybridisation of a variable-length genetic algorithm and a gradient descent method allows for concurrent learning of the system's parameters and structure in a versatile fashion. Results are presented addressing chaotic system and market-level one-day-ahead load forecasting.

Real-time control and operation of future power systems demands substantially different approaches from the conventional. Inherent short-term uncertainty and complexity in power systems will be notably increased. In order to deal with these two challenging attributes, this paper introduces a preliminary autonomic control scheme formed through the conjunction of type-2 fuzzy systems and a multi-agent architecture. Results are also presented addressing voltage control on an 11 kV UK distribution network.