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High-degree neurons feed cortical computations Beggs, John
Description
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 modication, 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 ow. We identied 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 ndings, we developed a simple feedforward network model. We found that a degree- modied 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 rst results to show that the extent to which a neuron modies incoming information streams depends on its topological location in the surrounding functional network. Co-authors: Nick Timme and Sunny Nigam
Item Metadata
Title |
High-degree neurons feed cortical computations
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2015-12-09T19:10
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Description |
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 modication, 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
ow. We identied 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 ndings, we developed a simple
feedforward network model. We found that a degree- modied 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 rst results to show that
the extent to which a neuron modies incoming information streams depends on its topological location
in the surrounding functional network. Co-authors: Nick Timme and Sunny Nigam
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Extent |
35 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: Indiana University
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Series | |
Date Available |
2016-06-09
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0304871
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Faculty
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International