Neural Network Classification and Prior Class Probabilities
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Discretization Problem for Rough Sets Methods
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
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Understanding cellular properties of neurons is central in neuroscience. It is especially important in light of recent discoveries suggesting that similar neural activity can be produced by cells with quite disparate characteristics. Unfortunately, due to experimental constraints, analyzing how the activity of neurons depends on cellular parameters is difficult. Computational modeling of biological neurons allows for exploration of many parameter combinations, without the necessity of a large number of "wet" experiments. However, analysis and interpretation of often very extensive databases of models can be hard. Thus there is a need for efficient algorithms capable of mining such data. This article proposes a rough sets-based approach to the problem of classifying functional and non-functional neuronal models. In addition to presenting a successful application of the theory of rough sets in the field of computational neuroscience, we are hoping to foster with this paper a greater interest among the members of the rough sets community to explore the plethora of important problems in that field.