Why do we have so many excitatory neurons?
Wang, Q.; Cardona, A.; Zlatic, M.; Vogelstein, J.; Priebe, C.
Show abstract
The emerging electron microscopy connectome datasets provides connectivity maps of the brains at single cell resolution, enabling us to estimate various network statistics, such as connectedness. We desire the ability to assess how the functional complexity of these networks depends on these network statistics. To this end, we developed an analysis pipeline and a statistic, XORness, which quantifies the functional complexity of these networks with varying network statistics. We illustrate that actual connectomes have high XORness, as do generated connectomes with the same network statistics, suggesting a normative role for functional complexity in guiding the evolution of connectomes, and providing clues to guide the development of artificial neural networks.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Conservative Significance Testing of Tripartite Interactions in Multivariate Neural Data 95%
- Circuit Analysis of the Drosophila Brain using Connectivity-based Neuronal Classification Reveals Organization of Key Communication Pathways 95%
- Multi-sensory integration in the mouse cortical connectome using a network diffusion model 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.