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Precise neural implementation of hippocampal, thalamocortical, and basal ganglia learning algorithms

Determine the precise neurobiological implementation of the distinct learning and processing algorithms in the hippocampus, the thalamocortical system (including the default mode and executive control networks), and the basal ganglia in the human brain.

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Background

The paper contrasts the architectural and algorithmic complexity of biological brains with present-day AI systems, emphasizing that creative cognition relies on multiple interacting brain systems with distinct learning mechanisms, including the thalamocortical system, basal ganglia, and hippocampus.

The author argues that faithfully implementing brain-inspired algorithms in AI is hindered by an incomplete understanding of how these learning and processing algorithms are realized biologically, highlighting a core open question in neuroscience relevant to creativity research and AI.

References

It is possible that future AI architectures will be more inspired by neural architecture, in which case this difference would be smaller, but implementing these specific algorithms in AI is not trivial, as we still do not know precisely how they are implemented in the brain.

— Artificial intelligence and the internal processes of creativity (2412.04366 - Aru, 5 Dec 2024) in Section: The internal processes underlying creativity