Home > Papers from 2017 > Mushroom Body Modelling

Mushroom Body Modelling

These papers might not be concerned with navigation directly, but they are both nice examples of how computational modelling can help us pursue a bottom-up approach to understanding the insect brain. Behavioural experiments investigating learning often result in descriptions of the “cognitive” capabilities of insects. However, without mechanistic grounding, the follow-up to these papers can often be semantic bickering or a contrarian attempt to think of different experimental stimuli, that might offer a different conclusion. With modelling one can at least demonstrate how certain “cognitive” properties can emerge from a particular architecture.
Here we have two papers from Lars Chittka’s group. Both take biological realistic models of the nervous architecture of insect and simulate a learning paradigm. Without specific tuning of the models, we see emergent properties. In simulations of olfactory experiments (Peng and Chittka) the network naturally shows peak shift and can deal with positive and negative patterning. In the visual domain (Roper et al.) we see visual generalisation and location invariance.
Peng, F., & Chittka, L. (2016). A Simple Computational Model of the Bee Mushroom Body Can Explain Seemingly Complex Forms of Olfactory Learning and Memory. Current Biology.
Roper, M., Fernando, C., & Chittka, L. (2017). Insect Bio-inspired Neural Network Provides New Evidence on How Simple Feature Detectors Can Enable Complex Visual Generalization and Stimulus Location Invariance in the Miniature Brain of Honeybees. PLOS Computational Biology13(2), e1005333.
Categories: Papers from 2017
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