While the neurobiology of simple and habitual choices is relatively well known, our current understanding of goal-directed choices and planning in the brain is still limited. Theoretical work suggests that goal-directed computations can be productively associated to modelbased (reinforcement learning) computations, yet a detailed mapping between computational processes and neuronal circuits remains to be fully established. Here we report a computational analysis that aligns Bayesian nonparametrics and model-based reinforcement learning (MB-RL) to the functioning of the hippocampus (HC) and the ventral striatum (vStr)-a neuronal circuit that increasingly recognized to be an appropriate model system to understand goal-directed (spatial) decisions and planning mechanisms in the brain. We test the MB-RL agent in a contextual conditioning task that depends on intact hippocampus and ventral striatal (shell) function and show that it solves the task while showing key behavioral and neuronal signatures of the HC-vStr circuit. Our simulations also explore the benefits of biological forms of look-ahead prediction (forward sweeps) during both learning and control. This article thus contributes to fill the gap between our current understanding of computational algorithms and biological realizations of (model-based) reinforcement learning.
Model-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis
Public Library of Science,, San Francisco, CA , Stati Uniti d'America
PLOS computational biology (Online) 14 (2018). doi:10.1371/journal.pcbi.1006316
info:cnr-pdr/source/autori:Stoianov, Ivilin Peev; Pennartz, Cyriel M. A.; Lansink, Carien S.; Pezzulo, Giovani/titolo:Model-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis/doi:10.1371/journal.pcbi.1006316/riv