Abstract
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.
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Wessnitzer, J., Webb, B., Smith, D. (2007). A Model of Non-elemental Associative Learning in the Mushroom Body Neuropil of the Insect Brain. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_54
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DOI: https://doi.org/10.1007/978-3-540-71618-1_54
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