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Simulated Activation Patterns of Biological Neurons Cultured onto a Multi-Electrode Array Based on a Modified Izhikevich's Model

Saito J. H., Mari J.-F., Pedrino E., Destro-Filho J. B., Nicoletti M. C.

Fundamenta Informaticae

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2013

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Vol. 124, nr 1/2

111--132

EN

Recently we have witnessed research efforts into developing real-time hybrid systems implementing interactions between computational models and live tissues, in an attempt to learn more about the functioning of biological neural networks. A fundamental role in the development of such systems is played by Multi-Electrode Array (MEA). In vitro cultures of neurons on MEAs, however, have some drawbacks such as: needing a rigorous adherence to sterile techniques, careful choice and replenishment of media and maintenance of pH, temperature, and osmolarity. An alternative way to study and investigate live tissues which partially circumvent some of the problems with in vitro cultures is by simulating them. This paper describes the proposal of Sim-MEA, a system for modeling and simulating neuron's communications in a MEA-based in vitro culture. Sim-MEA implements a modified Izhikevich model that takes into account both: distances between neurons and distances between microelectrodes and neurons. The system also provides ways of simulating microelectrodes and their recorded signals as well as recovering experimental MEA culture data, from their images. The soundness of the Sim-MEA simulation procedure was empirically evaluated using data from an experimental in vitro cultured hippocampal neurons of Wistar rat embryos. Results from simulations, compared to those of the in vitro experiment, are presented and discussed. The paper also describes a few experiments (varying several parameter values) to illustrate and detail the approach.

2

Coupling as Strategy for Reducing Concept-Drift in Never-ending Learning Environments

Hruschka Jr E. R., Duarte M. C., Nicoletti M. C.

Fundamenta Informaticae

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2013

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Vol. 124, nr 1/2

47--61

EN

The project and implementation of autonomous computational systems that incrementally learn and use what has been learnt to, continually, refine its learning abilities throughout time is still a goal far from being achieved. Such dynamic systems would conform to the main ideas of the automatic learning model conventionally characterized as never-ending learning (NEL). The never-ending approach to learning exhibits similarities to the semi-supervised (SS) model which has been successfully implemented by bootstrap learning methods. Bootstrap learning has been one of the most successful among the SS-methods proposed to date and, as such, the natural candidate for implementing NEL systems. Bootstrap methods learn from an available labeled set of data, use the induced knowledge to label some unlabeled new data and, recurrently, learn again from both sets of data in a cyclic manner. However the use of SS methods, particularly bootstrapping methods, to implement NEL systems can give rise to a problem known as concept-drift. Errors that may occur when the system automatically labels new unlabeled data can, over time, cause the system to run off track. The development of new strategies to lessen the impact of concept-drift is an important issue that should be addressed if the goal is to increase the plausibility of developing such systems, employing bootstrap methods. Coupling techniques can play an important role in reducing concept-drift effects over machine learning systems, particularly those designed to perform tasks related to machine reading. This paper proposes and formalizes relevant coupling strategies for dealing with the concept-drift problem in a NEL environment implemented as the system RTWP (Read The Web in Portuguese); initial results have shown they are promising strategies for minimizing the problem taking into account a few system settings.

3

Automatic Learning of Temporal Relations Under the Closed World Assumption

Nicoletti M. C., Lisboa F. O. S. S., Hruschka Jr E. R.

Fundamenta Informaticae

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2013

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Vol. 124, nr 1/2

133--151

EN

Time plays an important role in the vast majority of problems and, as such, it is a vital issue to be considered when developing computer systems for solving problems. In the literature, one of the most influential formalisms for representing time is known as Allen's Temporal Algebra based on a set of 13 relations (basic and reversed) that may hold between two time intervals. In spite of having a few drawbacks and limitations, Allen's formalism is still a convenient representation due to its simplicity and implementability and also, due to the fact that it has been the basis of several extensions. This paper explores the automatic learning of Allen's temporal relations by the inductive logic programming system FOIL, taking into account two possible representations for a time interval: (i) as a primitive concept and (ii) as a concept defined by the primitive concept of time point. The goals of the experiments described in the paper are (1) to explore the viability of both representations for use in automatic learning; (2) compare the facility and interpretability of the results; (3) evaluate the impact of the given examples for inducing a proper representation of the relations and (4) experiment with both representations under the assumption of a closed world (CWA), which would ease continuous learning using FOIL. Experimental results are presented and discussed as evidence that the CWA can be a convenient strategy when learning Allen's temporal relations.

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Rough Relation Properties

Nicoletti M. C., Uchoa J. Q., Baptistini M. T. Z.

International Journal of Applied Mathematics and Computer Science

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2001

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Vol. 11, no 3

621-635

EN

Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. This paper rewrites some properties of rough relations found in the literature, proving their validity.