The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and primates. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the functional logic of fruit fly brain circuits at this scale poses significant challenges.
In principle, a whole brain simulation could be instantiated by modeling all the neurons and synapses of the connectome/synaptome with the simple dynamics of integrate-and-fire neurons and synapses, with parameters tuned according to certain criteria. Such an effort, however, would fall short of revealing the fundamental computational units necessary for understanding the true functional logic of the brain, as the complexity of the different computational units becomes lost with a single uniform treatment of such a vast number of neurons and their connection patterns. It is, therefore, imperative to develop a formal reasoning framework of the functional logic of brain circuits that goes beyond simple instantiations of flows on graphs generated from the connectome.
To address these challenges, we present here a framework for building functional brain circuits from components whose functional logic can be readily evaluated, and for determining the canonical computational principles underlying these components using available data. Our focus is on modeling neural circuits arising in odor signal processing in the early olfactory, and motion detection in the early vision systems of the fruit fly using divisive normalization building blocks.
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