  The deep neural nets of modern artificial intelligence (AI) have not achieved
defining features of biological intelligence, including abstraction, causal
learning, and energy-efficiency. While scaling to larger models has delivered
performance improvements for current applications, more brain-like capacities
may demand new theories, models, and methods for designing artificial learning
systems. Here, we argue that this opportunity to reassess insights from the
brain should stimulate cooperation between AI research and theory-driven
computational neuroscience (CN). To motivate a brain basis of neural
computation, we present a dynamical view of intelligence from which we
elaborate concepts of sparsity in network structure, temporal dynamics, and
interactive learning. In particular, we suggest that temporal dynamics, as
expressed through neural synchrony, nested oscillations, and flexible
sequences, provide a rich computational layer for reading and updating
hierarchical models distributed in long-term memory networks. Moreover,
embracing agent-centered paradigms in AI and CN will accelerate our
understanding of the complex dynamics and behaviors that build useful world
models. A convergence of AI/CN theories and objectives will reveal dynamical
principles of intelligence for brains and engineered learning systems. This
article was inspired by our symposium on dynamical neuroscience and machine
learning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.
