A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding
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A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding. / Lorenz, Gabriel Matias; Martinez-Canada, Pablo; Panzeri, Stefano.
Brain Informatics: 16th International Conference, BI 2023, Hoboken, NJ, USA, August 1–3, 2023, Proceedings. Hrsg. / Feng Liu; Yu Zhang; Hongzhi Kuai; Emily P. Stephen; Hongjun Wang. 1. Aufl. Cham : Springer, Cham, 2023. S. 33-44 (Brain informatics).Publikationen: SCORING: Beitrag in Buch/Sammelwerk › SCORING: Beitrag in Sammelwerk › Forschung › Begutachtung
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TY - CHAP
T1 - A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding
AU - Lorenz, Gabriel Matias
AU - Martinez-Canada, Pablo
AU - Panzeri, Stefano
PY - 2023/9/13
Y1 - 2023/9/13
N2 - Spiking neural network models that have studied how oscillations are generated by recurrent cortical circuits and how they encode information have been focused on describing the encoding of information about external sensory stimuli carried by feed-forward inputs in a two-population circuit configuration that includes excitatory cells and fast-spiking interneurons. Here we extend these models to explore the contribution of different classes of cortical interneurons to cortical oscillations. We found that in our extended model, the feed-forward stimulus is still mainly encoded in the gamma frequency range, consistent with earlier works using a single interneuron type. However, we also found that the information carried by different regions of the gamma frequency range was larger than the sum of the information carried by the two individual frequencies. This shows that the power values at different frequencies carried information about the feedforward input in a synergistic way. This is in contrast to previous models with only one interneuron type, which mainly led to redundant information between frequencies in the gamma range. These results suggest that interneuron diversity has useful properties for enriching the encoding of information in the gamma frequency range.
AB - Spiking neural network models that have studied how oscillations are generated by recurrent cortical circuits and how they encode information have been focused on describing the encoding of information about external sensory stimuli carried by feed-forward inputs in a two-population circuit configuration that includes excitatory cells and fast-spiking interneurons. Here we extend these models to explore the contribution of different classes of cortical interneurons to cortical oscillations. We found that in our extended model, the feed-forward stimulus is still mainly encoded in the gamma frequency range, consistent with earlier works using a single interneuron type. However, we also found that the information carried by different regions of the gamma frequency range was larger than the sum of the information carried by the two individual frequencies. This shows that the power values at different frequencies carried information about the feedforward input in a synergistic way. This is in contrast to previous models with only one interneuron type, which mainly led to redundant information between frequencies in the gamma range. These results suggest that interneuron diversity has useful properties for enriching the encoding of information in the gamma frequency range.
U2 - 10.1007/978-3-031-43075-6_4
DO - 10.1007/978-3-031-43075-6_4
M3 - SCORING: Contribution to collected editions/anthologies
SN - 978-3-031-43074-9
T3 - Brain informatics
SP - 33
EP - 44
BT - Brain Informatics
A2 - Liu, Feng
A2 - Zhang, Yu
A2 - Kuai, Hongzhi
A2 - Stephen, Emily P.
A2 - Wang, Hongjun
PB - Springer, Cham
CY - Cham
ER -
