Almost two decades ago, Larry Abbott laid out principles and foundations for the coalescing field of theoretical neuroscience in a perspective in Neuron titled “Theoretical neuroscience rising.” One idea that stuck with me was that rigorous models—expressed in equations rather than words—can be formulated, explored and often rejected at a pace that no experimental program can match. Equations, Abbott argued, “force a model to be precise, complete, and self-consistent,” and this precision, combined with speed, acts as an intellectual filter, winnowing possibilities before expensive experiments begin. Abbott painted a picture in which modelers rapidly cycle through phases of model exploration and rejection.
In practice, this doesn’t quite happen. Translating a word model into equations, translating equations into code and integrating a model with data all represent engineering work that is invisible to the scientific narrative. These costs create a bottleneck that shapes what models are explored, adopted and published, ultimately throttling the pace of theoretical exploration, which Abbott identified as theory’s core advantage.
Agentic coding frameworks—systems that can write, debug and integrate code through natural language interaction—are changing that story, fast. Not by replacing the human work that goes into conceiving of models and weighing their merits but by eliminating the engineering scaffolding that has, until now, limited what theories we fully explore, which models get tested and how fast we can reject them. A theoretical neuroscientist can now specify a model in conversation—sketch out the assumptions, describe the data, outline the inference procedure—and have working code in days instead of months. This is exactly what the field should want.
How is this reshaping the landscape of theoretical and computational neuroscience? I outline below four main directions along which I think tectonic change is coming. To get a sense of how others in the field view this change, I also asked six neuroscientists to weigh in.
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irst, agentic models are removing technological hurdles, enabling experimentalists to implement and access models that previously required collaboration with a quantitative neuroscientist. In the former era, I would spend months fitting established but “above novice level” models in service of a collaboration—wrangling data, implementing model variants and debugging convergence. Most experimental labs were not able to implement these models because the technical barrier was just beyond their threshold. Instead, they relied on collaborators as guides, an often mutually beneficial process, but also one that did not require a modeler to live at the frontier of technical innovation. Agentic coding frameworks eliminate that friction. An experimentalist can now ask an artificial-intelligence (AI) agent: “I have this data. Can you fit a hierarchical generalized linear model to it?” and have a well-fit model with an accompanying explanation within an afternoon. Reducing this small but real technical barrier places these models directly in the hands of experimental researchers, parallelizing a formerly bottlenecked process.Second, theorists can finally accelerate their own exploratory work at the pace Abbott envisioned. Abbott argued that theorists could “formulate, explore and often reject models” faster than experimentalists could test them. But that advantage was always throttled by the time required to translate word models into equations and equations into code. In the former era, ideas that might bud remained kernels jotted in notebooks, simply because the cost of implementation was not worth the payoff, assuming most ideas were dead ends. Alternatively, this friction encouraged researchers to build simple models that could be analyzed and coded quickly. They were less likely to develop and explore complex models, which require more engineering overhead. Sometimes this simplicity offered deeper insight, and sometimes it failed to capture critical biological realism. In an agentic-framework era, a theorist can sketch a bold new theoretical idea—a modified recurrent neural network, a novel learning rule, a different population coding scheme—and quickly have it implemented, tested on synthetic data and refined. The theoretical frontier expands significantly, enabling exploration of more models, and more complex models, from seed to fruit. Many ideas will fail, which is a good thing. Abbott’s vision—rapid exploration and rejection of models—finally becomes operational.
Third, and most disorienting, is what agentic systems can discover on their own. Evolutionary algorithms guided by large language models (LLMs), such as Google’s AlphaEvolve, can explore model space in ways human intuition might not naturally traverse, and at a scale impossible for even the most caffeinated graduate student. We might not think to try, or have the infinite bandwidth to exhaustively attempt, particular combinations of nonlinearities, or a specific coupling between neural populations, even if the precursors to those combinations were sitting on the shelf waiting to be combined. Through sheer brute force, AI systems can explore these combinations, creating millions of potential models that include long-established ideas and unexplored new directions.
History offers insight into the potential pitfalls of rapid, technology-driven scientific discovery: The arrival of the spectroscope, for example, introduced a wave of new hypothetical chemical elements in the 1860s that included both real discoveries, such as helium, and phantoms, such as coronium—a hypothetical element rooted in a real observation, which took decades of theoretical work in physics and chemistry to dissolve. For AI-guided search, some discoveries will be scientifically meaningful; others will be artifacts of the search process. We’ll have to develop new skills for understanding models we didn’t directly build and continue to lean on and develop models based on first principles to separate the heliums from the coroniums.
Fourth, agentic coding frameworks enable more mathematically sophisticated models. Some of the most acclaimed and mathematically rigorous models in computational neuroscience, such as mean-field theories from statistical physics, employ advanced techniques that require years of focused training to wield. As we move into a “vibe proving” era, the outlook for equation-based, AI-assisted theoretical work is changing. A system that can manipulate symbolic mathematics and executable code simultaneously opens new research directions in mathematically sophisticated models by supporting researchers who are very comfortable at the computer terminal but less comfortable at the blackboard.
It’s worth acknowledging that this acceleration and expansion of theoretical possibilities comes with a real risk, particularly for trainees. For theorists, the struggle to translate an idea into equations and then into code isn’t merely friction; it’s a form of disciplined thinking. When a theorist builds a model piece by piece, they develop an intimacy with the model that forces understanding. The slow, painstaking construction is how the modeler comes to see gaps in their own reasoning, where vague intuitions confront reality. Automating this struggle away risks creating a world in which theorists never develop that intimate understanding of their own models. For trainees, this is especially dangerous. Advisers face a heavy responsibility to decide which struggles are worth preserving to protect the conditions under which learning occurs.
But there’s a deeper risk still. It’s precisely this struggle that often sparks genuine theoretical insight. Nights spent staring at a model behavior you can’t explain are when theorists make conceptual leaps that lead to genuine discovery. If we lose the struggle, we may lose not just understanding but inspiration itself. The field risks becoming prolific but shallow, generating models faster than we can generate insights. As Tim Requarth recently argued regarding AI-assisted writing, “if the struggle to articulate an idea is part of how you come to understand it, then tools that bypass that struggle might degrade … the kind of thinking that matters most for actual discovery.”
Agentic coding frameworks help us realize Abbott’s vision: the freedom to explore and reject ideas rapidly, the removal of technical barriers that reduce model adoption, and the ability for more researchers to explore complex and mathematically rigorous models. A North Star as we navigate this era is remembering that most ideas should fail, and, to develop and maintain this discretion, we may need to periodically step away from the AI and return to the time-tested resource of our own minds.
