Brain-inspired strategies for efficient artificial intelligence

Despite being constrained by a rigid skull and a fixed number of neurons, the brain excels in processing diverse information and making adaptive decisions with remarkable efficiency. This raises a central question at the intersection of neuroscience and artificial intelligence (AI): How does the brain achieve such high performance with limited physical resources? We addressed this question by examining the anatomical funneling architecture of the basal ganglia, a central hub for value-based decision-making. Through parallel processing, distinct circuits support cognitive flexibility and habitual stability, enabling efficient allocation of neural resources and context-sensitive engagement in specialized computations. In contrast, convergent processing compresses the input across circuits, allowing the efficient extraction of core information for decision-making, a form of quantitative efficiency that minimizes the number of neurons required. However, this compression can degrade the fidelity. To address this, the brain employs qualitative efficiency in which population-level neural patterns preserve fine-grained information and support generalization across similar contexts. Finally, we propose that the cortico-basal ganglia system achieves cognitive efficiency by funneling the anatomical structure and dimensionality reduction to optimize both performance and energy demands. These principles offer a biologically grounded framework for developing brain-inspired, resource-efficient artificial intelligence systems that balance generalization with precision.