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https://doi.org/10.1371/journal.pcbi.1004858
Title: | High-Degree Neurons Feed Cortical Computations | Authors: | Timme N.M. Ito S. Myroshnychenko M. Nigam S. Shimono M. Yeh F.-C. Hottowy P. Litke A.M. Beggs J.M. |
Keywords: | animal cell animal experiment animal tissue Article brain cell brain cortex controlled study hippocampus information processing information science mouse nerve cell nonhuman partial information decomposition positive feedback slice culture spike transfer entropy action potential animal biological model biology C57BL mouse cytology feedback system multivariate analysis nerve cell network physiology synaptic transmission Action Potentials Animals Cerebral Cortex Computational Biology Feedback, Physiological Hippocampus Information Theory Mice Mice, Inbred C57BL Models, Neurological Multivariate Analysis Nerve Net Neurons Synaptic Transmission |
Issue Date: | 2016 | Citation: | Timme N.M., Ito S., Myroshnychenko M., Nigam S., Shimono M., Yeh F.-C., Hottowy P., Litke A.M., Beggs J.M. (2016). High-Degree Neurons Feed Cortical Computations. PLoS Computational Biology 12 (5) : e1004858. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1004858 | Rights: | Attribution 4.0 International | Abstract: | Recent work has shown that functional connectivity among cortical neurons is highly varied, with a small percentage of neurons having many more connections than others. Also, recent theoretical developments now make it possible to quantify how neurons modify information from the connections they receive. Therefore, it is now possible to investigate how information modification, or computation, depends on the number of connections a neuron receives (in-degree) or sends out (out-degree). To do this, we recorded the simultaneous spiking activity of hundreds of neurons in cortico-hippocampal slice cultures using a high-density 512-electrode array. This preparation and recording method combination produced large numbers of neurons recorded at temporal and spatial resolutions that are not currently available in any in vivo recording system. We utilized transfer entropy (a well-established method for detecting linear and nonlinear interactions in time series) and the partial information decomposition (a powerful, recently developed tool for dissecting multivariate information processing into distinct parts) to quantify computation between neurons where information flows 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. ? 2016 Timme et al. | Source Title: | PLoS Computational Biology | URI: | https://scholarbank.nus.edu.sg/handle/10635/161914 | ISSN: | 1553734X | DOI: | 10.1371/journal.pcbi.1004858 | Rights: | Attribution 4.0 International |
Appears in Collections: | Elements Staff Publications |
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