Biology subjects

Dayan, P.

Publications and source records attributed to Dayan, P..

57 records · Page 4Linked to original sources

bioRxiv2017

The Long and the Short of Serotonergic Stimulation: Optogenetic activation of dorsal raphe serotonergic neurons changes the learning rate for rewards

Serotonin plays an influential, but computationally obscure, modulatory role in many aspects of normal and dysfunctional learning and cognition. Here, we studied the impact of optogenetic stimulation of dorsal raphe serotonin neurons in mice performing a non-stationary, reward-driven, foraging task. We report that activation of serotonin neurons significantly boosted learning rates for choices following long inter-trial-intervals that were driven by the recent history of reinforcement.

neuroscience↗

bioRxiv2017

Single-Trial Inhibition of Anterior Cingulate Disrupts Model-based Reinforcement Learning in a Two-step Decision Task.

The anterior cingulate cortex (ACC) is implicated in learning the value of actions, but it remains poorly understood whether and how it contributes to model-based mechanisms that use action-state predictions and afford behavioural flexibility. To isolate these mechanisms, we developed a multi-step decision task for mice in which both action-state transition probabilities and reward probabilities changed over time. Calcium imaging revealed ramps of choice-selective neuronal activity, followed by an evolving representation of the state reached and trial outcome, with different neuronal populations representing reward in different states. ACC neurons represented the current action-state transition structure, whether state transitions were expected or surprising, and the predicted state given chosen action. Optogenetic inhibition of ACC blocked the influence of action-state transitions on subsequent choice, without affecting the influence of rewards. These data support a role for ACC in model-based reinforcement learning, specifically in using action-state transitions to guide subsequent choice. HighlightsO_LIA novel two-step task disambiguates model-based and model-free RL in mice. C_LIO_LIACC represents all trial events, reward representation is contextualised by state. C_LIO_LIACC represents action-state transition structure, predicted states, and surprise. C_LIO_LIInhibiting ACC impedes action-state transitions from influencing subsequent choice. C_LI

neuroscience↗

bioRxiv2016

Modelling avoidance in pathologically anxious humans using reinforcement-learning

Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behaviour -avoiding social situations for fear of embarrassment, for instance-is a core feature of such anxiety. However, as for many other psychiatric symptoms, the biological mechanisms underlying avoidance remain unclear. Reinforcement-learning models provide formal and testable characterizations of the mechanisms of decision-making; here, we examine avoidance in these terms. One hundred and one healthy and pathologically anxious individuals completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. We show an increased reliance in the anxious group on a parameter of our reinforcement-learning model that characterizes a prepotent (Pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the anxious individuals were under stress. This formal description of avoidance within the reinforcement-learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of pathological anxiety.

animal behavior and cognition↗