A Simple Computational Model of the Bee Mushroom Body Can Explain Seemingly Complex Forms of Olfactory Learning and Memory

Abstract

Honeybees are models for studying how animals with relatively small brains accomplish complex cognition, displaying seemingly advanced (or “non-elemental”) learning phenomena involving multiple conditioned stimuli. These include “peak shift” [1–4]—where animals not only respond to entrained stimuli, but respond even more strongly to similar ones that are farther away from non-rewarding stimuli. Bees also display negative and positive patterning discrimination [5], responding in opposite ways to mixtures of two odors than to individual odors. Since Pavlov, it has often been assumed that such phenomena are more complex than simple associate learning. We present a model of connections between olfactory sensory input and bees’ mushroom bodies [6], incorporating empirically determined properties of mushroom body circuitry (random connectivity [7], sparse coding [8], and synaptic plasticity [9, 10]). We chose not to optimize the model's parameters to replicate specific behavioral phenomena, because we were interested in the emergent cognitive capacities that would pop out of a network constructed solely based on empirical neuroscientific information and plausible assumptions for unknown parameters. We demonstrate that the circuitry mediating “simple” associative learning can also replicate the various non-elemental forms of learning mentioned above and can effectively multi-task by replicating a range of different learning feats. We found that PN-KC synaptic plasticity is crucial in controlling the generalization-discrimination trade-off—it facilitates peak shift and hinders patterning discrimination—and that PN-to-KC connection number can affect this trade-off. These findings question the notion that forms of learning that have been regarded as “higher order” are computationally more complex than “simple” associative learning.