Biology subjects

Cinotti, F.

Publications and source records attributed to Cinotti, F..

3 recordsLinked to original sources

bioRxiv2022

Conserved reward-mediated, reinforcement-learning mechanisms in Pavlovian and instrumental tasks

Model-free and model-based computations are argued to distinctly update action values that guide decision-making processes. It is not known, however, if these model-free and model-based reinforcement learning mechanisms recruited in operationally based, instrumental tasks parallel those engaged by Pavlovian based behavioral procedures. Recently, computational work has suggested that individual differences in the attribution of incentive salience to reward predictive cues, i.e., sign- and goal-tracking behaviors, are also governed by variations in model-free and model-based value representations that guide behavior. Moreover, it is not appreciated if these systems that are characterized computationally using model-free and model-based algorithms are conserved across tasks for individual animals. In the current study, we used a within- subject design to assess sign-tracking and goal-tracking behaviors using a Pavlovian conditioned approach task, and, then characterized behavior using an instrumental multi-stage decision-making (MSDM) task in rats. We hypothesized that both Pavlovian and instrumental learning processes may be driven by common reinforcement-learning mechanisms. Our data confirm that sign-tracking behavior was associated with greater reward-mediated, model-free reinforcement learning and that it was also linked to model-free reinforcement learning in the MSDM task. Computational analyses revealed that Pavlovian model-free updating was correlated with model-free reinforcement learning in the MSDM task. These data provide key insights into the computational mechanisms mediating associative learning that could have important implications for normal and abnormal states. Significance StatementModel-free and model-based computations that guide instrumental, decision-making processes may also be recruited in Pavlovian based behavioral procedures. Here, we used a within-subject design to test the hypothesis that both Pavlovian and instrumental learning processes were driven by common reinforcement-learning mechanisms. Sign- tracking and goal-tracking behaviors were assessed in rats using a Pavlovian conditioned approach task, and, then instrumental behavior characterized using a multi- stage decision-making (MSDM) task. We report that sign-tracking behavior was associated with greater model-free, but not model-based, learning in the MSDM task. These data suggest that Pavlovian and instrumental behaviors are driven by conserved reinforcement-learning mechanisms.

animal behavior and cognition↗

bioRxiv2021

Bayesian mapping of the striatal microcircuit reveals robust asymmetries in the probabilities and distances of connections

The striatums complex microcircuit is made by connections within and between its D1- and D2-receptor expressing projection neurons and at least five species of interneuron. Precise knowledge of this circuit is likely essential to understanding striatums functional roles and its dysfunction in a wide range of movement and cognitive disorders. We introduce here a Bayesian approach to mapping neuron connectivity using intracellular recording data, which lets us simultaneously evaluate the probability of connection between neuron types, the strength of evidence for it, and its dependence on distance. Using it to synthesise a complete map of the mouse striatum, we find strong evidence for two asymmetries: a selective asymmetry of projection neuron connections, with D2 neurons connecting twice as densely to other projection neurons than do D1 neurons, but neither subtype preferentially connecting to another; and a length-scale asymmetry, with interneuron connection probabilities remaining non-negligible at more than twice the distance of projection neuron connections. We further show our Bayesian approach can evaluate evidence for wiring changes, using data from the developing striatum and a mouse model of Huntingtons disease. By quantifying the uncertainty in our knowledge of the microcircuit, our approach reveals a wide range of potential striatal wiring diagrams consistent with current data.

neuroscience↗

bioRxiv2018

Dopamine regulates the exploration-exploitation trade-off in rats

In a volatile environment where rewards are uncertain, successful performance requires a delicate balance between exploitation of the best option and exploration of alternative choices. It has theoretically been proposed that dopamine controls this exploration-exploitation trade-off, specifically that the higher the level of tonic dopamine, the more exploitation is favored. We demonstrate here that there is a formal relationship between the rescaling of dopamine positive reward prediction errors and the exploration-exploitation trade-off in simple non-stationary multi-armed bandit tasks. We further show in rats performing such a task that systemically antagonizing dopamine receptors greatly increases the number of random choices without affecting learning capacities. Simulations and comparison of a set of different computational models (an extended Q-learning model, a directed exploration model, and a meta-learning model) fitted on each individual confirm that, independently of the model, decreasing dopaminergic activity does not affect learning rate but is equivalent to an increase in exploration rate. This study shows that dopamine could adapt the exploration-exploitation trade-off in decision making when facing changing environmental contingencies.

neuroscience↗