Editorial: Functional microcircuits in the brain and in artificial intelligent systems

Jung H. Lee, Yoonsuck Choe, Salva Ardid, Reza Abbasi-Asl, Michelle McCarthy, Brian Hu

Abstract: Fundamental principles underlying human cognitive functions remain elusive, but recent breakthroughs in neurophysiology and deep learning offer new perspectives. First, experimental studies have uncovered neural circuit motifs consisting of various neuron types. For example, inhibitory neuron types expressing exclusive genes have specific targets and distinct functions (Pfeffer et al., 2013). Furthermore, diverse neuron types and their connectomes were identified in cortical columns (Jiang et al., 2015). Second, deep neural networks (DNNs), inspired by structures of the brain, have been trained to perform complex functions similar to human perception/cognition (Lecun et al., 2015).Computational models that can shed light on the links between neural circuits and cognitive functions 'Local' microcircuits, the building blocks of the brain, are embedded in larger networks, and thus their functions, rather than being intrinsic, strongly depend on interactions with various other parts within these networks. These intricate network structures pose great challenges when studying the role of local microcircuits in cognition. Computational modeling provides an effective way to study how the local microcircuits contribute to the brain's high-level functions (e.g., perception and decision-making). Two studies in this Research Topic involve computational modeling focusing on the functional roles of inhibitory neuron types.One of the essential tasks for visual perception is to distingui...