Matt Mosso, Ph.D. Candidate
May 2026
(8 Minutes)

The Cost of Slow Experiments

Modern neuroscience has evolved the capability to manipulate neural activity at unprecedented scale, yet the time from experimental conception to interpretable data still spans months to a year. Tackling difficult questions surrounding brain function often involves a multi-layered approach that lies at the intersection of multiple analytical frameworks including biochemistry, biophysics, cellular biology, anatomy, computation, and psychology. In mouse models, viral and genetic strategies for visualizing and manipulating neurons are paired with techniques for recording neural activity at high temporal resolution. These approaches have begun to clarify principles of connectivity and function of neurons that underpin the complex operations of brain function. Although over the last 15-20 years these tools have led to a rapid shift in our understanding of brain function, the experimental designs themselves can have a lead time of months. Between breeding the mice for the correct genetics and expressing genes using viruses, a few months may have passed before the actual experiment can begin. Once the necessary components of setting up an experiment have been arranged, the experiment itself along with data analysis and post-mortem tissue processing can extend a few more months. Thus, the time from experimental conceptualization to acquiring data can extend up to six months to a year.

A Hidden Bottleneck

Although laborious and time consuming, these steps are largely necessary for producing the kind of data that has rapidly advanced the field over the last decade. If these lengthy experimental protocols are necessary, how else can neuroscience optimize itself to shorten the time it takes to make critical discoveries that advance the field?

A worthwhile focus has been placed on optimizing experimental bottlenecks for enabling rapid acquisition and processing of data. However, a subtle and unappreciated bottleneck exists in this complex experimental space. This bottleneck lies in formulating the precise experimental questions these tools are designed to address. Just as a tool is only as good as the application it is used for, these complex experimental protocols can only go as far as the strength of the underlying question.

Because these experimental designs are inherently slow, a well formulated question can go a long way toward shortening the time required to address critical gaps in knowledge. As an example, biologists have been interested in identifying functional features of proteins. With the advent of fast gene sequencing methods and the construction of large publicly accessible databases like the Basic Local Alignment Search Tool (BLAST), biologists have been able to identify conserved regions across species or between other protein sequences. Without doing a single benchtop experiment, researchers can narrow down the experimental problem space and refine their experimental design to ultimately accelerate discovery. Similarly, the Protein Data Bank (PDB), a collection of publicly available protein structures, allows researchers developing therapeutics to engage in rational drug design strategies without having to engage in difficult experimental protocols like x-ray crystallography. Now in a new age of artificial intelligence, AlphaFold has provided a means for developing therapeutics without anybody having to do the lengthy leg work required to resolve the protein structure of interest.

A Fragmented Understanding of an Interconnected Brain

In neuroscience the theoretical frameworks that encapsulate function within and between innumerable brain areas are still in their infancy. While there is no shortage of data that has been collected, the central challenge is formulating the incisive question that produces maximal information. It takes years as a researcher to develop domain expertise in relation to a specific brain area. Yet, the deep dive into a specific brain region narrows thought into siloed perspectives and approaches. As someone who studies neural circuits in sensory cortex, a brain region notable for its role in learning about sensory inputs from the external world, I have shaped my perspective around the findings of this domain. Given limited time, any individual can only maintain superficial knowledge of most of the brain. This represents a major cost and inefficiency because the brain is veritably interconnected and thus interdependent. While the brain is highly interconnected, the literature describing it is fragmented. This interdependence, while normally eschewed for the sake of simplicity, is vital to consider in order to refine the experimental questions that ultimately lead to major discoveries.

With the explosion of data in this burgeoning field comes a level of systemic disorganization. While chemistry and biology have developed large databases for collating results across these domains, neuroscience remains disparate and disjointed. What neuroscience lacks is a way to organize and visualize data across a structured problem space that spans multiple layers of analysis. Ultimately, in a field where the lead time between conceptualization and dissemination is vast, a clear framework for thinking about a problem enables an elegant experiment. This, I would argue, is a key driver for increasing the efficiency of experimental effort.  Thus, I argue that the absence of a structured representation of functional neuroscience knowledge limits the quality and efficiency of experimental design.

Although disparate and disjointed, some effort towards the collation of data across molecular and systems level has just begun. The Allen Institute has developed large-scale resources to accelerate discovery by providing visualization tools for investigating different brain areas. They have combined search tools for identifying the anatomy and molecular makeup of neurons across brain areas. Certainly, in my own research, the knowledge of neuronal subtypes in my region of interest has refined my own research questions and has led to pertinent discoveries. Although large in scale, this institute represents only a fraction of the work being done to explain brain function. What remains missing is the systematic collation of functional findings across this literature to pair with clear connectome and transcriptomic models.

Envisioning a Testable Path Forward

With the emergence of large language models, the time is ripe to mine results spread across the literature into an accessible database and integrating them with existing resources like the Allen Brain Atlas. This effort, I hypothesize, will lead to the generation of more elegant, incisive research designs that ultimately boost the speed of discovery. As a toy example, say I have discovered that neuron X in brain region A seems to change during learning. Through my domain expertise I am aware that neuron X receives distant connections from brain regions B to Z consisting of neuron types X, Y, and Z. I could do lengthy experiments to test each brain area and neuron type individually to see what is driving this effect or spend substantial time navigating an immense body of literature. A much more efficient method would be to generate a synthesis of the literature, filtered by the connectivity profile, molecular makeup, and functional discoveries across an array of analytical frameworks. I argue that this interactive tool would better generate the optimal experiment to address the fundamental gap that prior experimental work has identified. Moreover, this interactive tool should be able to strongly suggest which neuron types and brain regions are most promising targets across a variety of experimental questions.

If this bottleneck is real, then improving access to structured knowledge should measurably improve experimental design. A pilot experiment could involve testing whether access to a structured synthesis of neuroscience literature improves the quality and efficiency of experimental design compared to standard literature review practices. This could be tested by comparing the quality of a grant-style proposal between two groups of researchers on a discrete gap in knowledge. One group is given standard tools for literature review, like PubMed, PDB, Allen atlases. A separate group is given access to a prototype system that extracts and organizes findings from literature and allows structured queries that link connectivity, molecular identity, and functional outcomes across various analytical perspectives. A blind review of the proposals would be performed by domain experts to evaluate how effective a tool like this is. Outcome measures would include quality of experimental design, creativity, and efficiency of proposal development.  

While techniques for identifying the functional readout of neurons have rapidly evolved, an architecture for developing the theoretical framework that underlies strong experimental design is lagging. As we progress in neuroscience, a melding of analytical frameworks that consider brain function across biochemistry, biophysics, cellular biology, anatomy, computation, and psychology becomes increasingly necessary. The sheer complexity of this problem necessitates an infrastructure for collating data across these subdomains to more efficiently design what are otherwise inevitably stretched experiments.

This essay argues that a bottleneck in generating precise enough experimental questions exists at a systemic level but that it can be addressed. This approach builds on lessons from fields where aggregating, organizing, and disseminating large bodies of information has improved experimental design. By constructing an infrastructure that aggregates dispersed findings into a structured knowledge base that can be queried and visualized, we can improve how experiments are conceived. The proposed pilot experiment offers a way to test this claim directly. Whether the hypothesis succeeds or fails, the result will be informative. If the hypothesis is correct, it points toward a new class of scientific infrastructure. If not, it will clarify the limits of current approaches and better inform how we may address this bottleneck. Either outcome advances our understanding of how scientific discovery can progress more efficiently.