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.

Neuroscience: Intelligence in the Honeybee Mushroom Body

“…With these models, learning can be replicated and features critical for its function can be identified. In a recent paper in Current Biology , Peng and Chittka [3] use a computer model of the mushroom body to examine its capacity to learn and reveal how that capacity depends on circuit features.…”

“…Anatomical and functional evidence suggests that there might be additional sites where learning takes place, at least in the honeybee mushroom body: the VUMmx1 neuron, an octopaminergic neuron activated by sugar that could mediate plasticity, innervates the input region of the mushroom body where projection neuron to Kenyon cell synapses are located [10]. In their study, Peng and Chittka [3] examine the effect of this additional site of plasticity on the learning performance of the mushroom body.…”

“…The model of the mushroom body developed by Peng and Chittka [3] contains 100 projection neurons that connect to 4000 Kenyon cells — numbers smaller than those for the real honeybee mushroom body but large enough to duplicate some of its functions. The model’s Kenyon cells connect to two output neurons, one mediating attraction and the other aversion, again a simplification compared to the multiple attractive and aversive outputs in the actual mushroom body.…”

“…This feature of the model is based on several recent studies that provide evidence for synaptic plasticity between Kenyon cells and output neurons [7–9]. This form of plasticity allows the model to exhibit simple associative learning, but Peng and Chittka [3] were interested in more complex relationships between stimulus and reward or punishment.…”

“…One task that Peng and Chittka [3] considered, which they call ‘patterning’, is similar to the exclusive-or problem famous in machine learning. Two odors, A and B, are associated with one valence (either attraction or aversion), while the mixture A–B is associated with the opposite valence.…”