Seeking and consuming nutrients is essential to survival and the maintenance of life. Dynamic and volatile environments require that animals learn complex behavioral strategies to obtain the necessary nutritive substances. While this has been classically viewed in terms of homeostatic regulation, recent theoretical work proposed that such strategies result from reinforcement learning processes. This theory proposed that phasic dopamine (DA) signals play a key role in signaling potentially need-fulfilling outcomes. To examine links between homeostatic and reinforcement learning processes, we focus on sodium appetite as sodium depletion triggers state- and taste-dependent changes in behavior and DA signaling evoked by sodium-related stimuli. We find that both the behavior and the dynamics of DA signaling underlying sodium appetite can be accounted for by a homeostatically regulated reinforcement learning framework (HRRL). We first optimized HRRL-based agents to sodium-seeking behavior measured in rodents. Agents successfully reproduced the state and the taste dependence of behavioral responding for sodium as well as for lithium and potassium salts. We then showed that these same agents account for the regulation of DA signals evoked by sodium tastants in a taste- and state-dependent manner. Our models quantitatively describe how DA signals evoked by sodium decrease with satiety and increase with deprivation. Lastly, our HRRL agents assigned equal preference for sodium versus the lithium containing salts, accounting for similar behavioral and neurophysiological observations in rodents. We propose that animals use orosensory signals as predictors of the internal impact of the consumed good and our results pose clear targets for future experiments. In sum, this work suggests that appetite-driven behavior may be driven by reinforcement learning mechanisms that are dynamically tuned by homeostatic need.

Seeking and consuming nutrients is essential to survival and maintenance of life. Dynamic and volatile environments require that animals learn complex behavioral strategies to obtain the necessary nutritive substances. While this has been classically viewed in terms of homeostatic regulation, where complex nutrient seeking behaviors are triggered by physiological need, recent theoretical work proposed that such strategies are a result of reinforcement learning processes. This theory also proposed that phasic dopamine (DA) signals play a key role in signaling potentially need-fulfilling outcomes. To examine potential links between homeostatic and reinforcement learning processes, we focus on sodium appetite as sodium depletion triggers state and taste dependent changes in behavior and DA signaling evoked by sodium-related stimuli. We find that both the behavior and the dynamics of DA signaling underlying sodium appetite can be accounted for by extending principles of homeostatic regulation into a reinforcement learning framework (HRRL). We first optimized HRRL-based agents to model sodium-seeking behavior measured in rats. Agents successfully reproduced the state and the taste dependence of behavioral responding for sodium as well as for lithium and potassium salts. We then show that these same agents can account for the regulation of DA signals evoked by sodium tastants in a taste and state dependent manner. Our models quantitatively describe how DA signals evoked by sodium decrease with satiety and increase with deprivation suggesting that phasic DA signals and sodium consumption are down regulated prior to animals reaching satiety. Lastly, our HRRL agents also account for the behavioral and neurophysiological observations that suggest mice cannot distinguish between sodium and lithium containing salts. Our HRRL agents exhibited an equal preference for sodium versus lithium containing solutions, and underestimated the nutritional value of sodium when lithium was concurrently available. We propose that animals use orosensory signals as predictors of the internal impact of the consumed good and our results pose clear targets for future experiments. In sum, this work suggests that appetite-dirven behavior may be driven by reinforcement learning mechanisms that are dynamically tuned by homeostatic need.