The impact of reciprocal links on learning performance of critical neural networks

Topological features significantly influence a network’s behavior and functional performance, particularly in terms of information transmission efficiency, learning, adaptation, and resource utilization. This paper focuses on the impact of network connectivity, specifically the role of reciprocal links, on the ability of neural networks in critical states to learn Boolean rules. Notably, the prevalence of reciprocal links in the cerebral cortex suggests that they play an important role in information processing. Our findings demonstrate that reciprocal links markedly enhance learning performance. As the backbone structure of the network becomes more complex with the addition of reciprocal links, there is an increase in alternative paths. This, in turn, facilitates more efficient signal transmission from input to output sites, ultimately leading to a higher learning success rate. Reciprocal links not only optimize information transmission pathways but also diminish the influence of irrelevant neurons, thus enhancing resource utilization efficiency.