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Spatial embedding promotes a specific form of modularity with low entropy and heterogeneous spectral dynamics
Understanding how biological constraints shape neural computation is a central goal of computational neuroscience.
Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience
As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging.
Building artificial neural circuits for domain-general cognition: a primer on brain-inspired systems-level architecture
There is a concerted effort to build domain-general artificial intelligence in the form of universal neural network models with sufficient computational flexibility to solve a wide variety of cognitive tasks but without requiring fine-tuning on individual problem spaces and domains.
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