The dorsal raphe nucleus (DRN) is involved in organizing reward-related behaviours; however, it remains unclear how genetically defined neurons in the DRN of a freely behaving animal respond to various natural rewards. Here we addressed this question using fibre photometry and single-unit recording from serotonin (5-HT) neurons and GABA neurons in the DRN of behaving mice. Rewards including sucrose, food, sex and social interaction rapidly activate 5-HT neurons, but aversive stimuli including quinine and footshock do not. Both expected and unexpected rewards activate 5-HT neurons. After mice learn to wait for sucrose delivery, most 5-HT neurons fire tonically during waiting and then phasically on reward acquisition. Finally, GABA neurons are activated by aversive stimuli but inhibited when mice seek rewards. Thus, DRN 5-HT neurons positively encode a wide range of reward signals during anticipatory and consummatory phases of reward responses. Moreover, GABA neurons play a complementary role in reward processing.
How do we learn about what to learn about? Specifically, how do the neural elements in our brain generalize what has been learned in one situation to recognize the common structure of – and speed learning in – other, similar situations? We know this happens; we and our mammalian kith become better at solving new problems – learning and deploying schemas
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– as we go through life. However, we have little insight into this process. Here we show that using prior knowledge to facilitate learning is accompanied by the evolution of a neural schema in the orbitofrontal cortex (OFC). Single units were recorded from rats deploying a schema to learn a succession of odor sequence problems. With learning, OFC ensembles converged on a low-dimensional neural manifold across both problems and subjects; this manifold represented the common structure of the problems and its evolution accelerated across their learning. These results demonstrate the formation and use of a schema in a prefrontal brain region to support a complex cognitive operation. Our results not only reveal an important new role for the OFC in learning but also have significant implications for using ensemble analyses to tap into complex cognitive functions.
Characterizing time-evolving networks is a challenging task, but it is crucial for understanding the dynamic behavior of complex systems such as the brain. For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood. A traditional approach in neuroimaging data analysis is to make simplifications through the assumption of static spatial networks. In this paper, without assuming static networks in time and/or space, we arrange the temporal data as a higher-order tensor and use a tensor factorization model called PARAFAC2 to capture underlying patterns (spatial networks) in time-evolving data and their evolution. Numerical experiments on simulated data demonstrate that PARAFAC2 can successfully reveal the underlying networks and their dynamics. We also show the promising performance of the model in terms of tracing the evolution of task-related functional connectivity in the brain through the analysis of functional magnetic resonance imaging data.
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