TY  - INPR
A1  - Gelens, Frank
A1  - Äijälä, Juho
A1  - Roberts, Louis
A1  - Komatsu, Misako
A1  - Uran, Cem
A1  - Jensen, Michael A.
A1  - Miller, Kai J.
A1  - Ince, Robin A. A.
A1  - Garagnani, Max
A1  - Vinck, Martin
A1  - Canales-Johnson, Andres
T1  - Distributed representations of prediction error signals across the cortical hierarchy are synergistic
T2  - bioRxiv
N2  - An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. brain signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded redundant and synergistic information during auditory prediction error processing. In both tasks, we observed multiple patterns of synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between auditory and frontal regions. Using a brain-constrained neural network, we simulated the spatio-temporal patterns of synergy and redundancy observed in the experimental results and further demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback and feedforward connections. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/83802
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-838026
UR  - https://www.biorxiv.org/content/10.1101/2023.01.12.523735v5
IS  - 2023.01.12.523735 Version 5
PB  - bioRxiv
ER  -