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The deep neural nets of modern artificial intelligence (AI) have not achieveddefining features of biological intelligence, including abstraction, causallearning, and energy-efficiency. While scaling to larger models has deliveredperformance improvements for current applications, more brain-like capacitiesmay demand new theories, models, and methods for designing artificial learningsystems. Here, we argue that this opportunity to reassess insights from thebrain should stimulate cooperation between AI research and theory-drivencomputational neuroscience (CN). To motivate a brain basis of neuralcomputation, we present a dynamical view of intelligence from which weelaborate concepts of sparsity in network structure, temporal dynamics, andinteractive learning. In particular, we suggest that temporal dynamics, asexpressed through neural synchrony, nested oscillations, and flexiblesequences, provide a rich computational layer for reading and updatinghierarchical models distributed in long-term memory networks. Moreover,embracing agent-centered paradigms in AI and CN will accelerate ourunderstanding of the complex dynamics and behaviors that build useful worldmodels. A convergence of AI/CN theories and objectives will reveal dynamicalprinciples of intelligence for brains and engineered learning systems. Thisarticle was inspired by our symposium on dynamical neuroscience and machinelearning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.

A brain basis of dynamical intelligence for AI and computational neuroscience