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Abstract
In many scientific fields, simulators for dynamical systems generate a massive amount of data. One of the crucial tasks scientists are spending their precious time on is comparing observational data to the aforementioned simulations in order to infer physically relevant parameters and their uncertainties based on the model embedded in the simulator. This poses a problem because the likelihood function for realistic simulations of complex physical systems is intractable. Simulation-based inference techniques attack this problem using machine learning tools and probabilistic programming. I will give an overview of the problem and introduce an application of the method to the analysis of multi-type cortical circuits.
Message form the Program Committee
The last decade has seen strings of breakthroughs in both neuroscience and artificial intelligence research. While there have long been fruitful exchanges between these fields, there is increased recent interest in the borderlands, using computational ideas and techniques to inform biology, and vice versa. This symposium brings together researchers working on a diversity of topics circling around questions related to the mind. We hope to catalyze new ideas, future collaborations, and an appreciation for the diversity of views on the topic. Please join us.
References for the poster presentation
Enrico Rinaldi
Research Scientist
My research interests include artificial intelligence and quantum computing applied to particle physics and quantum many-body systems.