What are the design principles of neural connectomes?
We show that a small # of biophysical features shape the structure and function of connecotmes in zebrafish, mice, & c. elegans.
lead by @_adam_haber, w/@RainerFriedri12 & @AdrianAWanner
The map of synaptic connectivity among neurons shapes the computations that neural circuits perform. Identifying the design principles of connectomes is fundamental for understanding brain development and architecture, neural computations, learning, and behavior... 2/n
We therefore learned probabilistic generative models for connectomes of the olfactory bulb of zebrafish, the mouse visual cortex, and of C. elegans .. 3/n
We find that models that rely on a surprisingly small number of simple biological and physical features are highly accurate in replicating the measured connectomes (features here shown for zebrafish).. 4/n
Specifically, in all species we accurately predict the existence of individual synapses, as well as their synaptic strength.. 5/n
We also accurately predict, in all cases, the distributions of synaptic indegree and outdegree of the neurons, the frequency of sub-network motifs (without any special features), and more.. 6/n
We then simulate synthetic circuits generated by our model for the olfactory bulb of zebrafish and show that they replicate the computation that the real circuit performs in response to olfactory cues.. 7/n
We then show that specific failures of our models reflect missing design features that we uncover by adding latent features to the model.. 8/n
Our results reflect surprisingly simple design principles of real connectomes in 3 different species, offering a general computational framework for analyzing connectomes and linking structure & function in neural circuits.
Inspired by models of collective behavior in animals and artificial agents (pnas.org/content/114/22…), we studied co-learning by ensembles of interacting neural networks that aim to maximize their own performance but also their functional relations to other networks
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Ensembles’ performance was optimal when coupling between networks increased diversity and degraded the performance of individual networks. Thus, even without a global goal for the ensemble, efficient collective behavior emerges from local interactions between networks 3/4