Selective differential attention enhanced cartesian atomic moment machine learning interatomic potentials with cross-system transferability

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首先,Next, brain-body coupling remains a challenging engineering and scientific problem in the whole stack. A central difficulty is not merely running a neural simulation and a physics simulation side by side; it is deciding how firing rates or spikes in specific descending neurons should map onto changes in torques, joint trajectories, posture changes, or coordinated sequences of leg movements. At what rate should a particular sensory stimuli activate specific sensory neurons, and how much should a particular descending neuron activity influence, for example, turning speed? These mappings can be somewhat arbitrarily chosen by hand (as is our case), or learned with reinforcement learning, or mediated by lower-level controllers, but in all cases it is still an approximation of the true motor hierarchy. One solution towards this might be more imaging or electrophysiology to understand the specific transformation between DN firing rate and specific behavior.

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