Developing large-scale neural network models that mimic the brain鈥檚 activity is a major goal in the field of computational neuroscience. Existing models that accurately reproduce aspects of brain activity are notoriously complex, and fine-tuning model parameters often requires significant time, intuition, and expertise. New published research from an interdisciplinary group of researchers primarily based at 麻豆村 and the University of Pittsburgh presents a novel solution to mitigate some of these challenges. The machine learning-driven framework, Spiking Network Optimization using Population Statistics (SNOPS), can quickly and accurately customize models that reproduce activity to mimic what鈥檚 observed in the brain.

鈥淥ne way for neuroscientists to understand how the brain works is by constructing mathematical models of the brain to reproduce its activity,鈥� explained Shenghao Wu, a former graduate student in neural computation and machine learning at Carnegie Mellon. 鈥淭o date, building such models has been a manual process and usually requires a lot of energy and domain expertise. Our SNOPS method is not only faster and more powerful, but also, it finds a wider range of model configurations that are consistent with the brain鈥檚 activity, all automatically.鈥�

Chengcheng Huang, an assistant professor of neuroscience and mathematics at the University of Pittsburgh whose background is in circuit modeling elaborated, 鈥淏efore SNOPS, when a model got complicated and we wanted to explain a more complex phenomenon, it was difficult to find the right parameters to analyze the model鈥檚 full behavior. SNOPS is a useful tool to accelerate our progress and, ultimately, develop more realistic models of the brain.鈥�

Published in听, the group鈥檚 work to develop SNOPS uniquely combined the efforts of experimentalists, data-driven computational scientists, and modelers. 鈥淲e have very different backgrounds and ways of approaching things, and that鈥檚 in the spirit of the neuroscience we do at Carnegie Mellon,鈥� said听Matt Smith, professor of听biomedical engineering听and听Neuroscience Institute听and co-director of the听. 鈥淚鈥檓 excited about the way Shenghao combined all of our skills to build SNOPS, and also, how we can apply it to better understand how different parts of the brain work together.鈥澨�

In the days ahead, SNOPS, which is now available via open-source sharing, can guide the development of network models with the aim of enabling deeper insight into how networks of neurons give rise to brain function.

鈥淲e started with network models that have been widely used over decades.听There were certain aspects of the brain鈥檚 activity that we could not get the models to reproduce, no matter how we tuned it,鈥� added听Byron Yu, professor of biomedical engineering and听听at 麻豆村. 鈥淲ith SNOPS, we can quickly find a configuration that captures all the needed aspects of the brain鈥檚 activity. It gives us a lot of hope for putting together the big picture.鈥�

The group鈥檚 work was supported by the听National Institutes of Health and Simons Foundation.听Additional study authors include Adam Snyder, assistant professor at University of Rochester, and Brent Doiron, professor at The University of Chicago.
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For media inquiries, please contact Sara Pecchia at听pecchia@cmu.edu.