Scientists have developed a new method that can analyse large volumes of data generated by thousands of neurons. This breakthrough in predicting neuronal behaviour in large networks is an important step towards understanding how an organism’s behaviour arises from the interaction between its nervous system, its body and its environment. The new method in question is a unifying framework that is capable of more accurately estimating a system’s fluctuations and its sensitivity to parameter changes. Developed with support from the EU-funded DIMENSIVE project, this tool is useful for devising methods for analysing large-scale, non-equilibrium biological and social dynamics. A study describing the framework has been published in the journal ‘Nature Communications’. “Only very recently have we had the technology to record thousands of individual neurons in animals while they interact with their environment, which is a tremendous stride forward from studying networks of neurons isolated in laboratory cultures or in immobilized or anaesthetized animals,” stated Dr Miguel Aguilera of DIMENSIVE project coordinator University of Sussex in a news item posted on the university’s website. “This is a very exciting advancement but we don’t have the methods yet to analyse and understand the massive amount of data created by non-equilibrium behaviour. Our contribution offers the possibility to advance the technology forward to find models that explain how neurons process information and generate behaviour,” continued Dr Aguilera, who is the study’s lead author.
Taking highly fluctuating systems into account
The most efficient way to learn how large systems function is with statistical models such as mean-field methods that are good in regimes with small fluctuations. “But these techniques often just work in very idealized conditions,” Dr Aguilera explained. In fact, a great number of biological systems function in critical, highly fluctuating regimes. “Brains are in constant change, development and adaptation, displaying complex fluctuating patterns and interacting with rapidly changing environments. Our model aims to capture precisely the fluctuations in these non-equilibrium situations that we expect from freely behaving animals in their natural surroundings.” The research team applied a geometric approach to mean-field approximation. According to co-author Dr S. Amin Moosavi of Japan’s Kyoto University, “information geometry provides us a clear path to systematically advance our methods and suggest novel approaches, resulting in more accurate data analysis tools.” A unified framework of mean-field theories enables the systematic construction of mean-field methods in line with the statistical properties of the systems researchers are exploring. Co-author Prof. Hideaki Shimazaki, also of Kyoto University, remarked: “In addition to providing advanced calculation methods for large systems, the framework unifies many existing approaches from which we can further advance neuroscience and machine learning. We are glad to offer such a unifying view that expresses a hallmark of scientific progress as a product of this intense international collaboration.” As reported in the news item, these methods will now be used to model thousands of neurons of zebrafish interacting with a virtual reality set-up. This is the next stage of the DIMENSIVE (Data-driven Inference of Models from Embodied Neural Systems In Vertebrate Experiments) project that ends in May 2022. For more information, please see: DIMENSIVE project web page
DIMENSIVE, neuron, unifying framework, mean-field method, neural system