How to Successfully Collaborate with a Computational Neuroscientist
The advancement and proliferation of high-throughput, multi-unit experimental tools, including calcium imaging and electrophysiology, ushered in an era in which neuroscientists produce massive amounts of data. These datasets are treasure troves of information, much of which lies untapped because there is just too much to analyze without powerful computational tools.
My lab at the Icahn School of Medicine at Mount Sinai uses computational approaches to understand how cognition and behaviors emerge from neural processes. This work would not be possible without close collaborators who provide us with experimental datasets to test our approaches. In return, we help them uncover novel insights that might not otherwise have been discovered. Collaborating with theoretical scientists can help wet lab groups get more out of their data — but fostering the relationship is key to its success. After years of building these partnerships, I am sharing four core principles that will promote a productive and mutually beneficial arrangement.
Go in with an open and collaborative mind
The most important part of a new collaboration isn’t the science topic: it’s the relationship between collaborators. Both parties must be open to new ideas and communicative throughout the process. Your new collaborator will likely have a different perspective and approach to neuroscience, so the proposed project may not align with your initial expectations. However, those unexpected directions are often the most valuable. I’ve found that by engaging in open and respectful dialogue, the optimal path will emerge.
Be generous with your time and knowledge. Try to remember that each person is probably not a native speaker in the other’s field. Collaborators should be willing to educate on their discipline’s challenges, approaches, and decision-making rationale. If one party feels confused or thinks that the other group doesn’t respect their expertise, the collaboration may be at risk. Therefore, recognize the areas where you are not an expert so that you can be humble, listen, and learn.
New collaborations go more smoothly when parties start working together at the very beginning — sometimes even before a proposal is funded! Loop in your collaborator during the design and planning process to ensure everyone feels a sense of ownership over the work. Starting early also offers an ability to troubleshoot issues ahead of time, rather than having to repeat work or reevaluate the hypothesis. Use pilot data to see if adjustments need to be made before starting a larger experiment.
No matter where you are in the process, regular check-ins and discussions are essential. I prefer video calls or in-person meetings when possible because reviewing the project together in real-time can prevent possible misinterpretations through email.
Organize and annotate
When starting any research project, it helps to be organized. But if you’re not accustomed to computational models and codes, you may not be aware of a few small tweaks that will help your theoretical counterparts work more efficiently:
Be consistent with trial design. We rely on programs to extract, process, and evaluate your data. While they can be updated, it's difficult and time-consuming to make tailored processes for each individual experiment. If you need to update parameters mid-way through data collection, connect with your collaborators to discuss the implications and align on next steps. A minor delay in data collection may outweigh the potential ramifications of even the slightest alteration in experimental design.
Create thoughtful and consistent filenames. While simple, this small practice can save a lot of time. Use metadata as part of the filename itself, while keeping the filename consistent for easy pattern matching. For example, a filename like 2021-01-07-id187-t19.csv makes it easy for both people and programs to select by year, subject ID, and trial number. On the other hand, a filename like kr-exp19-final2.csv is not helpful.
Convert data files from closed, proprietary formats to an open, non-proprietary format to ensure machine readability across time and computing setups. That said, always save your raw data and do not overwrite it with the new format. In the case of conversion errors or corruption, this extra file will help maintain data integrity.
Foster more than a one-off collaboration
Don’t approach a new collaboration as a simple exchange, where the wet lab scientist asks, ‘‘Can you analyze my data for me?” Similarly, computational scientists should make an effort to fully engage with wet lab researchers, rather than asking, “Can you send me your data?” Both sides should feel like there is a genuine, thoughtful partnership, rather than one party providing a core service to the other.
As the research moves forward, ensure that everyone agrees on interpretations, authorship, data management, and publication plans. For example, if an external researcher requests data from your joint study, is your collaborator okay with you sharing all the files? Are the files clear enough for others to understand them, or do they need more work before they are shareable? Setting up these norms decreases the chance of conflict or confusion down the line.
As we head into an unprecedented period where artificial intelligence and machine learning are not only staples in science, but also in society, so will computational approaches become commonplace in neuroscience. Working together to advance science will no longer be the exception, but the rule. When done right, a successful initial collaboration can evolve into a career-long partnership that benefits all parties — while simultaneously accelerating and expanding the discovery engine.
About the author
Kanaka Rajan, Ph.D. is a Computational Neuroscientist and Assistant Professor at the Friedman Brain Institute at the Icahn School of Medicine at Mount Sinai in New York. Her research seeks to understand how important cognitive functions — such as learning, remembering, and deciding — emerge from the cooperative activity of multi-scale neural processes. Using data from neuroscience experiments, Kanaka applies computational frameworks derived from machine learning and statistical physics to uncover integrative theories about the brain that bridge neurobiology and artificial intelligence. For more information visit the Rajan lab website and connect with her on Twitter and LinkedIn.
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