The cellular architecture of memory modules in Drosophila supports stochastic input integration

The ability to associate neutral stimuli with valence information and to store these associations as memories forms the basis for decision making. To determine the underlying computational principles, we build a realistic computational model of a central decision module within the Drosophila mushroom body (MB), the fly's center for learning and memory. Our model combines the electron microscopy-based architecture of one MB output neuron (MBON-a3), the synaptic connectivity of its 948 presynaptic Kenyon cells (KCs), and its membrane properties obtained from patch-clamp recordings. We show that this neuron is electrotonically compact and that synaptic input corresponding to simulated odor input robustly drives its spiking behavior. Therefore, sparse innervation by KCs can efficiently control and modulate MBON activity in response to learning with minimal requirements on the specificity of synaptic localization. This architecture allows efficient storage of large numbers of memories using the flexible stochastic connectivity of the circuit.

Automated summary

The ability to associate neutral stimuli with valence information and store them as memories is crucial for decision making. A computational model of the central decision module in the Drosophila mushroom body was built, incorporating the architecture of one output neuron, synaptic connectivity of its presynaptic cells, and membrane properties. This model showed that the neuron efficiently controls and modulates its activity in response to learning, allowing for storage of large numbers of memories.