Neuron Threshold Variability in an Olfactory Model Improves Odorant Discrimination
We used a model based on the olfactory system of insects to analyze the impact of neuron threshold variability in the mushroom body (MB) for odorant discrimination purposes. This model is a single-hidden-layer neural network (SLN) where the input layer represents the antennal lobe (AL), which contains a binary code for each odorant; the hidden layer that represents the Kenyon cells (KC) and the output layer named the output neurons. The KC and output layers are responsible for learning odor discrimination. The binary code obtained for each odorant in the output layer has been used to measure the discrimination error and to know what kind of thresholds (heterogeneous or homogeneous) provide better results when they are used in KC and output neurons. We show that discrimination error is lower for heterogeneous thresholds than for homogeneous thresholds.
Keywordsneural variability pattern recognition odor learning and discrimination decision making
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