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iMB Seminar Series: John Beggs

Friday, September 9, 2016 - 12:00pm

Class of '62 Auditorium (John Morgan Building)

John Beggs
Department of Biophysics
Indiana University

High-degree neurons feed cortical computations

Recent results have shown that functional connectivity among cortical neurons is highly varied, with a small percentage of neurons having many more connections than others. Also, new theoretical work makes it possible to quantify how neurons modify information from the connections they receive. These developments allow us to investigate how information modification, or computation, depends on the number of connections a neuron receives (in-degree) or sends out (out-degree). We used a high-density 512 electrode array to record spontaneous spiking activity from cortical slice cultures and transfer entropy to construct a network of information flow. We identified generic computations by the synergy produced wherever two information streams converged. We found that computations did not occur equally in all neurons throughout the networks. Surprisingly, neurons that computed large amounts of information tended to receive connections from high out-degree neurons. However, the in-degree of a neuron was not related to the amount of information it computed. To gain insight into these findings, we developed a simple feedforward network model. We found that a degree-modified Hebbian wiring rule best reproduced the pattern of computation and degree correlation results seen in the real data. Interestingly, this rule also maximized signal propagation in the presence of network-wide correlations, suggesting a mechanism by which cortex could deal with common random background input. These are the first results to show that the extent to which a neuron modifies incoming information streams depends on its topological location in the surrounding functional network.


Pizza will be served at 11:30am. The talk will begin at 12:00pm.