Abstract
The olfactory system of insects has two types of neurons based on the conditional response to odorants. Neurons that respond to a few odor classes are called specialists, while generalist neurons code for a wide range of input classes. The function of these neurons is intriguing. Specialist neurons are perhaps essential for odor discrimination, while generalist neurons may extract general properties of the odor space to be able to generalize to new odor spaces. Our goal is to shed light on this issue by analyzing the relevance of these neurons for pattern recognition purposes. The computational model is based on the olfactory system of insects. The model contains an approximation to the antennal lobe (AL) and mushroom body (MB) using a single-hidden-layer neural network. To determine the optimal balance between specialists and generalists we measure the classification error of the pattern recognition task. The mechanism to achieve the optimal balance is synaptic pruning to select the optimal synaptic configuration. The results show that specialists play an important role in odor classification, which is not observed for generalists. Furthermore, proper classification requires low neural activity in Kenyon cells, KC, which is consistent with the sparseness condition observed in MB neurons. Moreover, we also observe that the model is robust against noise to input patterns showing better resilience for low connection probabilities between AL and MB.
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Montero, A., Huerta, R., Rodríguez, F.B. (2014). Neural Trade-Offs among Specialist and Generalist Neurons in Pattern Recognition. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_7
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DOI: https://doi.org/10.1007/978-3-319-11071-4_7
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