P170 Competition between symmetric and antisymmetric connections in cortical networks
Dong Li*1,Claus C. Hilgetag1,2
1Institut für Computational Neuroscience, Universitätsklinikum Hamburg-Eppendorf (UKE), 20246 Hamburg, Germany
2Department of Health Sciences, Boston University, 02215 Boston, USA
*Email: d.li@uke.de
Introduction
The pairwise correlation of neural activity directly and significantly influences neural network performance across various cognitive tasks [1, 2]. While tasks such as working memory require low correlation levels [3], others, like motor actions, rely on higher correlation levels [1]. These correlation patterns are highly sensitive to network structure and neural plasticity [4-6]. However, understanding how neural networks dynamically balance tasks with differing correlation demands, and how distinct brain networks are structurally optimized for specific functions remains a major challenge.
Methods We simulate linear and spiking models to investigate the impact of symmetric and antisymmetric connections on neural network dynamics. The linear model, equipped with a control parameter that adjusts the relative intensity of these connections, captures fundamental mechanisms, which shape pairwise correlations and influence network performance across cognitive tasks. To quantify the competition between symmetric and antisymmetric connections, we introduce two indices from global and local perspectives. Using these indices, we further examine how synaptic plasticity modulates the relative intensity of these connections. Finally, we employ the spiking model to explore how bio-plausible neural networks implement this competition. Results Antisymmetric connections naturally reduce pairwise correlations, facilitating cognitive tasks that require maximal information processing, such as working memory. In contrast, symmetric connections enhance pairwise correlations, supporting other functions, such as enabling the network to generate reliable responses to external inputs. The competition between antisymmetric and symmetric connections can be easily modulated by spike-timing-dependent plasticity (STDP) with antisymmetric and symmetric kernels, respectively. In bio-plausible networks, this competition is particularly shaped by the structured, non-random organization of excitatory and inhibitory connections. Discussion Every connection matrix can be decomposed into symmetric and antisymmetric components with varying relative intensities. This work reveals how the competition between these components modulates neural correlations and facilitates distinct functions. Temporally, this competition is dynamically regulated by synaptic plasticity. Spatially, in comparison to indirect experimental evidence, our analysis actually also allows the discussion of the layer-specific distribution of these relative intensities. These findings provide a new perspective on how brain functions are segregated across both time and space.
Acknowledgements This work was in part founded by DFGTRR-169 (A2) andSFB 936 (A1/Z3). References [1]https://doi.org/10.1038/nrn1888 [2]Von Der Malsburg, C. (1994). The correlation theory of brain function. In Models of neural networks: Temporal aspects of coding and information processing in biological systems (pp. 95-119). New York, NY: Springer New York. [3]https://doi.org/10.1016/j.dcn.2025.101541 [4]https://doi.org/10.1016/0893-6080(94)00108-X [5]https://doi.org/10.1126/science.1211095 [6]https://doi.org/10.1162/NECO_a_00451
