  How perception and reasoning arise from neuronal network activity is poorly
understood. This is reflected in the fundamental limitations of connectionist
artificial intelligence, typified by deep neural networks trained via
gradient-based optimization. Despite success on many tasks, such networks
remain unexplainable black boxes incapable of symbolic reasoning and concept
generalization. Here we show that a simple set of biologically consistent
organizing principles confer these capabilities to neuronal networks. To
demonstrate, we implement these principles in a novel machine learning
algorithm, based on concept construction instead of optimization, to design
deep neural networks that reason with explainable neuron activity. On a range
of tasks including NP-hard problems, their reasoning capabilities grant
additional cognitive functions, like deliberating through self-analysis,
tolerating adversarial attacks, and learning transferable rules from simple
examples to solve problems of unencountered complexity. The networks also
naturally display properties of biological nervous systems inherently absent in
current deep neural networks, including sparsity, modularity, and both
distributed and localized firing patterns. Because they do not sacrifice
performance, compactness, or training time on standard learning tasks, these
networks provide a new black-box-free approach to artificial intelligence. They
likewise serve as a quantitative framework to understand the emergence of
cognition from neuronal networks.
