The “papers” column shows that about half of the strongest bridges are not concepts or ideas, but tools: general-purpose statistical and machine-learning software used routinely across both fields—the BraTS tumor-segmentation challenge, the lme4 mixed-effects package, SciPy, ROC analysis and XGBoost rank among the works most cited from both fields. The other half are genuine bridge works, a classical canon of foundational neuroscience work that AI researchers regularly cite: Bi and Poo’s work on spike-timing-dependent plasticity (1998); Izhikevich’s spiking neuron models (2003); experimental and modeling work on sensory (primarily visual) perception: visual attention (and its saliency-based implementation), object recognition, and receptive fields and sparse coding; as well as working memory and short-term plasticity works. These are important conceptual bridges, regularly used as inspiration for the development of functional architectures; they are what the brain as inspiration for AI looks like in practical terms. The most recent of these bridge works is from 2003. This does not, however, indicate that the influence of neuroscience on AI is waning. AI is now citing more neuroscience than ever before. However, the past two decades have not seen a new landmark paper that both fields treat as essential.
The astonishing recent achievements in engineering intelligence owe neuroscience surprisingly little in its actual machinery and operating principles.
The central column highlights authors whose work is read across the borders. Note that a place in this rank is not earned by eminence in one field but by citation pull from both fields simultaneously. The ranking encompasses a well-balanced mix of prominent AI-leaning researchers (including Yoshua Bengio, Geoffrey Hinton and Antonio Torralba) and prominent computational neuroscientists (including Karl Friston, Peter Dayan, Wulfram Gerstner, Henry Markram, Christof Koch and Aude Oliva). These researchers’ body of work is the most read and cited across communities, actively building bidirectional connections. (Affiliations were hand-verified against ORCID, institutional pages and CVs; solid links mark current affiliations, and faded links mark past affiliations.)
The bridge is most skewed with respect to institutions. Two industry labs, Alphabet (Google + DeepMind) and Microsoft, top the ranking; they are also the most AI-leaning entities in the whole figure. Several elite universities follow (Massachusetts Institute of Technology; University of California, Berkeley; Harvard University; University College London and Stanford University). Bridge work concentrates far more by place than by person: The top 100 institutions absorb nearly 40 percent of all bridge weight; by contrast, the top 100 authors remain below 7 percent. (See the repository for more information.) The bridge is held up primarily by institutional support, resources and intellectual environments.
D
oes the analysis suggest we are really witnessing a renaissance of neuro-inspired AI? Is neuroscience regaining its original placement as a driver of AI research and development? Read together, these results show that the bridge is real and is gaining momentum. However, it is also narrow, tool-heavy and concentrated. Citation patterns suggest that AI continues to cite newer neuroscience papers. But these papers do not yet qualify as bridge works—foundational discoveries highly cited by both fields.
The brain still has much wisdom to offer. The human brain remains the only biological system that does what frontier AI labs are trying to build, and it does it on roughly 20 watts, learning from a handful of examples, effortlessly generalizing to new situations. Energy and sample efficiency, robustness and out-of-distribution generalization are real engineering targets, not analogies. For neuroscience to revitalize its core relevance to AI, we need to understand the mechanisms underneath them. We also need to make our tools legible: Opening black boxes and characterizing and understanding complex systems from the inside out is our daily bread. AI is now facing similar problems under similar constraints, so let us share what we have devised.
The suggestions in this essay can help strengthen the bridge. But it also needs to become wider. Institutional concentration tends to yield narrow consensus, which is unhealthy for any scientific venture. The problems we are tackling here are some of the oldest and most complex we have ever faced; doing it right requires different perspectives. Both fields need to realize what they bring to each other. The momentum is building; the community is maturing. Now it is up to us to give AI something newer to cite than the works we produced 30 years ago.