To do so, we extend to neural activity the maximum occupancy principle (MOP) developed for behavior, and refer to this new neural principle asNeuroMOP.NeuroMOP posits thatthegoal ofthenervoussystem istomaximize future action-state entropy, a reward-free, intrinsic motivation that entails creating allpossible activity patterns while avoiding terminal ordangerous ones.

This work has the potential for wide-ranging applications in human-in-the-loop (e.g. We set the radius of agents to 0.3, the radius of The dataset will be made public. The only difference of our model's architecture to theirs is that we use agent-centric representations Then, we construct an edge from the agent that corresponds to the row to the "column agent" then compare this with the ground truth graph. The smaller the circle, the further it is into the future.

Understanding the structural relationships among different variables provides critical insights in manyreal-worldapplications, suchasmedicine,economics andeducation [42,62]. Thus,learning graphs from observed data, known as structure learning, has recently made remarkable progress [10,61,63,64]. Formanyapplications, variables inthedata can begathered into semantically meaningful groups, where useful insights are at group level. For example, in finance, one may be interested in how a financial situation influences different industries (i.e.