Abstract:

Synaptic connectivity varies widely across neuronal types.  In the cerebellar cortex, granule cells receive five orders of magnitude fewer inputs than the Purkinje cells they innervate. A large divergence in synaptic connectivity is also seen in other circuits with cerebellum-like architectures, including the insect mushroom body. In the cerebral cortex, on the other hand, the number of inputs per neuron is more uniform and large.  In this talk, I will discuss recent work that addresses what determines the optimal number of connections for a given neuronal type, and what these different degrees of connectivity mean for neural computation. The theory I will describe predicts optimal values for the number of inputs to cerebellar granule cells and Kenyon cells of the Drosophila mushroom body, and it also provides a functional explanation for why the degrees of connectivity in cerebellum-like and cerebrocortical systems are so different.

I will then describe an analysis of a complete electron-microscopy reconstruction of a cerebellum-like structure in the Drosophila larva: the mushroom body, which is a center for learning and memory in this and other insect species. I will show that the mushroom body's anatomical organization is consistent with the theory, and that its developmental program appears to be optimized to produce high-dimensional representations of the input it receives. I will conclude by turning toward cerebrocortical systems, discussing ongoing work on the plasticity of neural representations of odor in mice.