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Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termedmultiscale neuraldynamicsneuralordinarydifferentialequation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes. 

Your brain can be divided into various areas, one of which is responsible for your sense of touch. This part of your brain can be divided into even smaller areas that communicate with each body part. We can use a special map of the human body, called a sensory homunculus, to help us understand the various sizes of these parts of the brain. We will explain how this map was created and tell you about research showing how these brain areas can change. One study showed that brain areas can be recycled, meaning that the brain areas that no longer receive messages from the body can be used by other functioning brain areas. Another study showed that these changes can even occur within a single day! These studies can help scientists to better understand the brain and to help people who have problems with the sense of touch.