Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Decision Making Logic of Visual Brain
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
The Neurophysiological Bases of Cognitive Computation Using Rough Set Theory
Transactions on Rough Sets IX
Brain-like approximate reasoning
VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference
Logical rules of visual brain: From anatomy through neurophysiology to cognition
Cognitive Systems Research
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Most information about the external world comes from our visual brain. However, it is not clear how this information is processed. We will analyze brain responses using machine learning methods based on rough set theory. We will test the expertise of the visual area V4, which is responsible for shape classifications. Characteristic of each stimulus are treated as a set of learning attributes. We assume that bottom-up information is related to hypotheses, while top-down information is related to predictions. Therefore, neuronal responses are divided into three categories. Category 0 occurs if cell response is below 20 spikes/s (sp/s), indicating that the hypothesis is not valid. Category 1 occurs if cell activity is higher than 20 spikes, implying the hypothesis is valid. Category 2 occurs if cell response is above 40 sp/s; in this case we conclude that the hypothesis and prediction are valid. By using experimental data we make a decision table for each cell, and generate equivalence classes. We express the brains basic concepts by means of the learners basic categories. By approximating stimulus categories with concepts of different cells we determine core properties of cells, and differences between them. On this basis we have created profiles of their receptive field properties.