We developed a computational model of the mushroom body (MB), a prominent region of multimodal integration in the insect brain, and tested the model's performance for non-elemental associative learning in visual pattern avoidance tasks. We employ a realistic spiking neuron model and spike time dependent plasticity, and learning performance is investigated in closed-loop conditions. We show that the distinctive neuroarchitecture (divergence onto MB neurons and convergence from MB neurons, with an otherwise non-specific connectivity) is sufficient for solving non-elemental learning tasks and thus modulating underlying reflexes in context-dependent, heterarchical manner. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Wessnitzer, J., Webb, B., & Smith, D. (2007). A model of non-elemental associative learning in the mushroom body neuropil of the insect brain. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4431 LNCS, pp. 488–497). Springer Verlag. https://doi.org/10.1007/978-3-540-71618-1_54
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