Nicolangelo Iannella ^*

• Department of Biosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, SA, Norway

The underlying mechanisms and corresponding neural circuits involved in how cognition and perception emerge in the brain have been actively studied for over fifty years. Fully understanding how these traits emerge is complex and challenging, leading to the development of novel tools, both experimental and computational. Over the last three decades, computational models have played important roles in investigating the complex action of neural circuits involved in perception and cognitive function, while developing a cellular and network-level description of how external information is transformed and perceived in the brain, in an orderly fashion. Despite the complex nature of such information, the brain can easily decode information through the coordinated activity of neural populations and corresponding processes. Here, computational models have proved useful for understanding such processes via building network models of neural dynamics, inspired by the mammalian cortex's hierarchical (deep) organisation, which can mimic the activity of neural populations involved in some perceptual or cognitive task.Fifteen years have passed since the inception of Frontiers in Computational Neuroscience and this research topic is a celebration of the 15 th year anniversary of the journal and for the community to highlight new results focusing on computational perception and cognition. This research topic, as a part of a series aimed at showcasing recent advances in the field of computational perception and cognition, provides an outlet to discuss current challenges and exciting new developments in computational perception and cognition. The presented articles on this research topic provide a snapshot of recent research outcomes, that will hopefully inspire others to investigate the underlying neural correlates of cognition and perception.Investigations presented in this Topic, focus on several important aspects for perception and cognition, such as information coding and memory capacity. Wei and Li (2023) proposed that using directed graphs as abstractions of biological neural networks along with node-adaptive learning can encode, store, and retrieve information and further illustrated consistent memory performance, that outperformed Hopfield network in both memory retreival accuracy and storage capacity.