Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures

• Published in: PLoS computational biology 2021-07, Vol.17 (7), p.e1009196-e1009196
• Main Authors: Sherrill, Samantha P, Timme, Nicholas M, Beggs, John M, Newman, Ehren L
• Format: Article
• Language: English
• Subjects: Analysis; Artificial neural networks; Biology and Life Sciences; Computer and Information Sciences; Data processing; Decomposition; Feedback; Firing pattern; Information flow; Information processing; Integration; Methods; Neural networks; Neurons; Physical Sciences
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1009196

The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration–a type of computation. We established previously that synergistic integration varies directly with the strength of feedforward information flow. However, the relationships between both recurrent and feedback information flow and synergistic integration remain unknown. To address this, we analyzed the spiking activity of hundreds of neurons in organotypic cultures of mouse cortex. We asked how empirically observed synergistic integration–determined from partial information decomposition–varied with local functional network structure that was categorized into motifs with varying recurrent and feedback information flow. We found that synergistic integration was elevated in motifs with greater recurrent information flow beyond that expected from the local feedforward information flow. Feedback information flow was interrelated with feedforward information flow and was associated with decreased synergistic integration. Our results indicate that synergistic integration is distinctly influenced by the directionality of local information flow.