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During my time in research I worked on theoretical and computational models of local cortical circuits. I was specifically interested in the non-random connectivity structures that emerge in these networks. I no longer work in academia.
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AUTHOR SUMMARY Understanding the specific connectivity of neural circuits is an important challenge of modern neuroscience. In this study we address an important feature of neural connectivity, the abundance of bidirectionally connected neuron pairs, which far exceeds what would be expected in a random network. Our theoretical analysis reveals a si...
The recurrent microcircuitry of the cortex has been shown to be highly nonrandom. One notable aspect of this is an overrepresentation of bidirectional connections between excitatory neurons as compared to a random graph. Using an established model of a self-organizing neural network with structural plasticity in its excitatory pool of neurons, we e...
Complete research code and generated data for the article to reproduce the figures and computations referenced. Please visit https://non-random-connectivity-comes-in-pairs.github.io/ for documentation of the code.
Supporting Information for "Non-random network connectivity comes in pairs". Felix Z. Hoffmann, Jochen Triesch, 2016.