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.

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.

These images are taken from the last task, so there is no catastrophic interference.