The map of synaptic connectivity among neurons shapes the computations that neural circuits may perform, and so, identifying the design principles of neural connectomes is fundamental for understanding brain development and architecture, neural computations, learning, and behavior. We present probabilistic generative models for the connectomes of the olfactory bulb of zebrafish, part of the mouse visual cortex, and C. elegans. We show that in all cases, our models are highly accurate in replicating a wide range of properties of the measured circuits, while relying on a surprisingly small number of biological and physical features. Specifically, we accurately predict the existence of individual synapses and their strength, synaptic indegree and outdegree of the neurons, frequency of sub-network motifs, and more. We then simulate synthetic circuits generated by our model for the olfactory bulb of zebrafish and show that they replicate the decorrelating computation that the real circuit performs in response to olfactory cues. Finally, we present an extension of these generative models for the development of connectomes over time, and show it accurately replicates the “developmental trajectory” of the connectome of C. elegans, revealing a surprisingly small number of relevant functional cell types and distinct developmental epochs. Thus, our results reflect surprisingly simple design principles of real connectomes in different systems and species and offer a novel general computational framework for analyzing connectomes and linking structure and function in neural circuits.
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