Editorial to the Special Issue “The Resonant Brain: A Themed Issue Dedicated to Professor Stephen Grossberg”

This Special Issue offers a collection of research and model approaches to fundamental principles, mechanisms, and model architectures closely linked to the conceptual foundations of contemporary neural network research laid down by Stephen Grossberg [1] and his colleagues. Their pioneering conceptual work and models have since inspired and been explored further in state-of-the-art interdisciplinary research [2,3,4,5,6,7]. Analyzing and explaining brain mechanisms and data is considered a key to understanding cognition, with the overarching goal of explaining how the human mind arises from cooperative and competitive mechanisms through the step-by-step integration of information into brain representations. Information may be defined, in the context of this topical issue, in terms of either external physical information or internally encoded signals and emotions. Integration is achieved by mechanisms that may be pre-conscious, sub-conscious, and, ultimately, fully conscious [7,8], achieving cognitive representations across hierarchically organized layers of brain processing. The articles in this collection are directly relevant to topics ranging from the quest for explainable and predictive Artificial Intelligence (AI) [9] to the conscious representation of complex visual configurations in variable contexts [10] by the human brain. A unified understanding of how our brains see [11], hear [12], and feel [5,6] in order to learn about and know the world and to effectively interact with it clarifies how autonomous adaptive intelligence is achieved [13]. The research collected in this Special Issue is mostly concerned with mechanistic approaches to adaptive behavior. These can help solve large-scale problems in machine learning, AI, and robotics [14]. Brains embody universal developmental codes and laws found in all living cells and synapses [15], from the most primitive to the most advanced. These laws govern networks of interacting cells to support development and learning in all species, from mollusks to humans. Brain evolution across species has culminated in humans’ unique ability to study their own minds and to understand how minds derive meaning from an inherently ambiguous world—a skill that helps us adapt to changing environments. Despite the general advocacy for developing AI systems, significant limitations exist. A major one is the missing conceptual link between cognitive neuroscience and AI. While breakthroughs in AI often are claimed to be inspired by cognitive neuroscience, this is, however, most often not the case. Leveraging the most recent advances in cognitive neuroscience to emulate technology for novel solutions in machine learning and AI was a major part of Grossberg’s work, despite an inevitable contradiction: the pursuits of cognitive neuroscience and AI are, by nature, distinct. Cognitive neuroscience aims at clarifying mechanisms of cognition in living brains by studying neural activities linked to specific mental and behavioral states [16]. AI, on the other hand, is a black box approach aimed at predicting outcomes on the basis of algorithms that most often do not even remotely take into account the workings of the biological brain [17].