Serotonin's function in the brain is unclear. One challenge in testing the numerous hypotheses about serotonin's function has been observing the activity of identified serotonergic neurons in animals engaged in behavioral tasks. We recorded the activity of dorsal raphe neurons while mice experienced a task in which rewards and punishments varied across blocks of trials. We ‘tagged’ serotonergic neurons with the light-sensitive protein channelrhodopsin-2 and identified them based on their responses to light. We found three main features of serotonergic neuron activity: (1) a large fraction of serotonergic neurons modulated their tonic firing rates over the course of minutes during reward vs punishment blocks; (2) most were phasically excited by punishments; and (3) a subset was phasically excited by reward-predicting cues. By contrast, dopaminergic neurons did not show firing rate changes across blocks of trials. These results suggest that serotonergic neurons signal information about reward and punishment on multiple timescales.

Serotonin and dopamine are major neuromodulators. Here, we used a modified rabies virus to identify monosynaptic inputs to serotonin neurons in the dorsal and median raphe (DR and MR). We found that inputs to DR and MR serotonin neurons are spatially shifted in the forebrain, and MR serotonin neurons receive inputs from more medial structures. Then, we compared these data with inputs to dopamine neurons in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc). We found that DR serotonin neurons receive inputs from a remarkably similar set of areas as VTA dopamine neurons apart from the striatum, which preferentially targets dopamine neurons. Our results suggest three major input streams: a medial stream regulates MR serotonin neurons, an intermediate stream regulates DR serotonin and VTA dopamine neurons, and a lateral stream regulates SNc dopamine neurons. These results provide fundamental organizational principles of afferent control for serotonin and dopamine.

Reinforcement allows organisms to learn which stimuli predict subsequent biological relevance. Hebbian mechanisms of synaptic plasticity are insufficient to account for reinforced learning because neuromodulators signaling biological relevance are delayed with respect to the neural activity associated with the stimulus. A theoretical solution is the concept of eligibility traces (eTraces), silent synaptic processes elicited by activity which upon arrival of a neuromodulator are converted into a lasting change in synaptic strength. Previously we demonstrated in visual cortical slices the Hebbian induction of eTraces and their conversion into LTP and LTD by the retroactive action of norepinephrine and serotonin Here we show in vivo in mouse V1 that the induction of eTraces and their conversion to LTP/D by norepinephrine and serotonin respectively potentiates and depresses visual responses. We also show that the integrity of this process is crucial for ocular dominance plasticity, a canonical model of experience-dependent plasticity.

The cerebral cortex generates flexible behavior by learning. Reinforcement learning is thought to be driven by error signals in midbrain dopamine neurons. However, they project more densely to basal ganglia than cortex, leaving open the possibility of another source of learning signals for cortex. The locus coeruleus (LC) contains most of the brain’s norepinephrine (NE) neurons and project broadly to cortex. We measured activity from identified mouse LC-NE neurons during a behavioral task requiring ongoing learning from reward prediction errors (RPEs). We found two types of LC-NE neurons: neurons with wide action potentials (type I) were excited by positive RPE and showed an increasing relationship with change of choice likelihood. Neurons with thin action potentials (type II) were excited by lack of reward and showed a decreasing relationship with change of choice likelihood. Silencing LC-NE neurons changed future choices, as predicted from the electrophysiological recordings and a model of how RPEs are used to guide learning. We reveal functional heterogeneity of a neuromodulatory system in the brain and show that NE inputs to cortex act as a quantitative learning signal for flexible behavior.