We develop a method for selecting meaningful learning strategies based solely on the behavioral data of a single individual in a learning experiment. We use simple Activity-Credit Assignment algorithms to model the different strategies and couple them with a novel hold-out statistical selection method. Application on rat behavioral data in a continuous T-maze task reveals a particular learning strategy that consists in chunking the paths used by the animal. Neuronal data collected in the dorsomedial striatum confirm this strategy.

The rat dorsomedial (DMS) and dorsolateral striatum (DMS), equivalent to caudate nucleus and putamen in primates, are generally required for goal-directed and habit behaviour, respectively. This functional dichotomy is well established in instrumental conditioning, but it is less clear whether it also exists in non-instrumental learning. In this study we investigated this issue by recording DMS and DLS single neuron activity in rats performing a continuous alternation task in which no cue instruction is provided. We first applied a classical analytical approach to identify task-related activity based on the modifications of single neuron firing rate in relation to specific task events or maze trajectories. We then used an innovative approach based on Hawkes process to reconstruct a directed connectivity graph of neurons simultaneously recorded in each learning session, that was used to decode animal behavior. This approach enabled us to better unravel the role of DMS and DLS neural networks across learning stages, from the acquisition to the optimization of the behavioral strategy. We showed that DMS and DLS display different task-related activity throughout learning stages, and the proportion of coding neurons over time decreases in the DMS and increases in the DLS. Despite theses major differences, the decoding power of both networks increases during learning. These results suggest that both DMS and DLS are engaged during all learning stages, but the two neural networks gradually reorganize in different ways, in contrast with the common assumption of a gradual shift from DMS to DLS activity across learning stages.

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