Computational neuroscience brings together approaches from neuroscience, mathematics, physics, computer science, and engineering to understand how the brain processes information. Its development has relied not only on scientific breakthroughs, but also on sustained funding, dedicated research centers, and opportunities for researchers from different disciplines to work together.

In his NeuroView article, “How private foundations and governments helped build theoretical neuroscience,” published in Neuron in October 2026, Xiao-Jing Wang (New York University, USA) traces the development of theoretical and computational neuroscience from the 1980s onwards. In the United States, early milestones included the establishment of the first graduate program in Computational and Neural Systems at the California Institute of Technology (Caltech), as well as summer courses at the Cold Spring Harbor Laboratory in Long Island and the Marine Biological Laboratory in Woods Hole. Having entered the field himself in the late 1980s, Wang reflects on the role that visionary funders, research institutions, and collaborative programs played in its development.

Among the initiatives Wang discusses is the Bernstein Network Computational Neuroscience, established in Germany in 2004 through a funding initiative of the German Federal Ministry of Education and Research (BMBF). Initially comprising four centers in Berlin, Freiburg, Munich, and Göttingen, the network later expanded to include centers in Heidelberg-Mannheim and Tübingen. Andreas Herz (Bernstein Center for Computational Neuroscience Munich) recalls the highly competitive nature of the selection process. As a key requirement, the institutions applying for the initial five-year BMBF funding had to guarantee long-term support for their planned centers, with a dedicated building and new permanent professorships for the faculty positions requested in the grant proposals. “The psychology of this process was fascinating,” Herz says. “As one didn’t know how much institutional long-term support was needed for a competitive bid, all of us were able to secure substantial investments from our universities.”

Today, the network connects over 600 individual researchers across more than 20 countries. Through initiatives such as the Valentin Braitenberg Award for Computational Neuroscience and the Brains for Brains Young Researcher Award, as well as its annual Bernstein Conference, it supports scientific exchange, recognizes outstanding research, and fosters the next generation of computational neuroscientists. Wang highlights the network’s contribution to establishing Germany as an important location for computational neuroscience and points to researchers who began their careers within the network and went on to academic positions at institutions around the world.

A central theme of Wang’s article is the importance of creating environments in which theoretical and experimental researchers can learn from one another and develop shared research questions. Dedicated research centers and collaborative programs can provide more than financial support: they create communities in which ideas, methods, and expertise can be exchanged. They also help researchers trained in quantitative disciplines such as mathematics and physics transition into neuroscience, while enabling experimental researchers to integrate computational approaches into their work. This exchange is essential to making interdisciplinary research possible.

The history Wang describes also illustrates why sustained investment in fundamental research matters. As he argues, the impact of investments in theoretical neuroscience extends well beyond the development of the field itself — advances in computational neuroscience have contributed to research directions that connect basic brain research with broader technological and societal challenges, including computational psychiatry, artificial intelligence, and brain-machine interfaces. As the field continues to evolve, long-term support for interdisciplinary research communities remains essential to advancing our understanding of the brain and enabling future discoveries.