Working paper
Collecting reward to defend homeostasis: A homeostatic reinforcement learning theory
the preprint server for biology. код не известен, зарубежная публикация. Cold Spring Harbor Laboratory, 2014
In press
Efficient regulation of internal homeostasis and defending it against perturbations
requires complex behavioral strategies. However, the computational principles mediating brain’s
homeostatic regulation of reward and associative learning remain undefined. Here we use a
definition of primary rewards, as outcomes fulfilling physiological needs, to build a normative
theory showing how learning motivated behavior is modulated by the internal state of the animal.
The theory proves that seeking rewards is equivalent to the fundamental objective of
physiological stability, defining the notion of physiological rationality of behavior. We further
give a formal basis for temporal discounting of reward. It also explains how animals learn to act
predictively to preclude prospective homeostatic challenges, and attributes a normative
computational role to the modulation of midbrain dopaminergic activity by hypothalamic signals.
Publication based on the results of:
Активное и пассивное декодирование нейрональных процессов при выполнении когнитивных и нейроэкономических задач(2015)