Biology subjects

Hoang, I. B.

Publications and source records attributed to Hoang, I. B..

3 recordsLinked to original sources

bioRxiv2023

A novel hypothalamic-midbrain circuit for model-based learning

Behavior is often dichotomized into model-free and model-based systems 1, 2. Model-free behavior prioritizes associations that have high value, regardless of the specific consequence or circumstance. In contrast, model-based behavior involves considering all possible outcomes to produce behavior that best fits the current circumstance. We typically exhibit a mixture of these behaviors so we can trade-off efficiency and flexibility. However, substance use disorder shifts behavior more strongly towards model-free systems, which produces a difficulty abstaining from drug-seeking due to an inability to withhold making the model-free high-value response 3-10. The lateral hypothalamus (LH) is implicated in substance use disorder 11-17 and we have demonstrated that this region is critical to Pavlovian cue-reward learning 18, 19. However, it is unknown whether learning occurring in LH is model-free or model-based, where the necessary teaching signal comes from to facilitate learning in LH, and whether this is relevant for learning deficits that drive substance use disorder. Here, we reveal that learning occurring in the LH is model-based. Further, we confirm the existence of an understudied projection extending from dopamine neurons in the ventral tegmental area (VTA) to the LH and demonstrate that this input underlies model-based learning in LH. Finally, we examine the impact of methamphetamine self-administration on LH-dependent model-based processes. These experiments reveal that a history of methamphetamine administration enhances the model-based control that Pavlovian cues have over decision-making, which was accompanied by a bidirectional strengthening of the LH to VTA circuit. Together, this work reveals a novel bidirectional circuit that underlies model-based learning and is relevant to the behavioral and cognitive changes that arise with substance use disorders. This circuit represents a new addition to models of addiction, which focus on instrumental components of drug addiction and increases in model-free habits after drug exposure 3-10.

neuroscience↗

bioRxiv2022

The cognitive basis of intracranial self-stimulation of midbrain dopamine neurons

Recently there has been a reckoning in the dopamine field. This has suggested that the dopamine prediction error may function as a teaching signal, without endowing preceding events with value. We studied the cognitive basis of intracranial self-stimulation (ICSS), a setting where dopamine appears to be valuable. Physiological frequencies seen during reinforcement learning did not support robust ICSS or promote behavior that would indicate the stimulation was represented as a meaningful reward in a specific or general sense. This was despite demonstrating that this same physiologically-relevant signal could function as a teaching signal. However, supraphysiological frequencies supported robust ICSS where the stimulation was represented as a specific sensory event, which acted as a goal to motivate behavior. This demonstrates that dopamine neurons only support ICSS at supraphysiological frequencies, and in a manner that does not reflect our subjective experience with endogenous firing of dopamine neurons during reinforcement learning. One sentence summaryDopamine neurons only support ICSS at supraphysiological frequencies and in a manner not reflecting dopamines role in learning.

neuroscience↗

bioRxiv2022

"Learning in reverse: Dopamine errors drive excitatory and inhibitory components of backward conditioning in an outcome-specific manner".

For over two decades, midbrain dopamine was considered synonymous with the prediction error in temporal-difference reinforcement learning. Central to this proposal is the notion that reward-predictive stimuli become endowed with the scalar value of predicted rewards. When these cues are subsequently encountered, their predictive value is compared to the value of the actual reward received allowing for the calculation of prediction errors. Phasic firing of dopamine neurons was proposed to reflect this computation, facilitating the backpropagation of value from the predicted reward to the reward-predictive stimulus, thus reducing future prediction errors. There are two critical assumptions of this proposal: 1) that dopamine errors can only facilitate learning about scalar value and not more complex features of predicted rewards, and 2) that the dopamine signal can only be involved in anticipatory learning in which cues or actions precede rewards. Recent work has challenged the first assumption, demonstrating that phasic dopamine signals across species are involved in learning about more complex features of the predicted outcomes, in a manner that transcends this value computation. Here, we tested the validity of the second assumption. Specifically, we examined whether phasic midbrain dopamine activity would be necessary for backward conditioning--when a neutral cue reliably follows a rewarding outcome. Using a specific Pavlovian-to-Instrumental Transfer (PIT) procedure, we show rats learn both excitatory and inhibitory components of a backward association, and that this association entails knowledge of the specific identity of the reward and cue. We demonstrate that brief optogenetic inhibition of VTADA neurons timed to the transition between the reward and cue, reduces both of these components of backward conditioning. These findings suggest VTADA neurons are capable of facilitating associations between contiguously occurring events, regardless of the content of those events. We conclude that these data are in line with suggestions that the VTADA error acts as a universal teaching signal. This may provide insight into why dopamine function has been implicated in a myriad of psychological disorders that are characterized by very distinct reinforcement-learning deficits. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/475719v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@192012forg.highwire.dtl.DTLVardef@8bdf86org.highwire.dtl.DTLVardef@ffe1faorg.highwire.dtl.DTLVardef@14d0152_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗