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Adenosine and dopamine receptors in striatal areas interact to regulate a number of different functions, including aspects of motor control and motivation. Recent studies indicate that adenosine A(2A) receptor antagonists can reverse the effects of dopamine (DA) D(2) antagonists on instrumental tasks that provide measures of effort-related choice behavior. The present experiments compared the ability of the adenosine A(2A) antagonist KW6002, the nonselective adenosine antagonist caffeine, and the adenosine A(1) receptor selective antagonist DPCPX, to reverse the behavioral effects of the DA D(2) antagonist haloperidol. For these studies, a concurrent choice procedure was used in which rats could select between lever pressing on a fixed ratio 5 schedule for a preferred food or approaching and consuming a less preferred lab chow that was concurrently available in the chamber. Under baseline or control conditions, rats show a strong preference for lever pressing, and eat little of the chow; IP injections of 0.1 mg/kg haloperidol significantly reduced lever pressing and substantially increased chow intake. The adenosine A(2A) antagonist KW6002 (0.125-0.5 mg/kg IP) and the nonselective adenosine antagonist caffeine (5.0-20.0 mg/kg) significantly reversed the effects of haloperidol. However, the adenosine A(1) antagonist DPCPX (0.1875-0.75 mg/kg IP) failed to reverse the effects of the D(2) antagonist. The rank order of effect sizes in the reversal experiments was KW6002>caffeine>DPCPX. None of these drugs had any effect on behavior when they were injected in the absence of haloperidol. These results indicate that the ability of an adenosine antagonist to reverse the effort-related effects of a D(2) antagonist depends upon the subtype of adenosine receptor being blocked. Together with other recent results, these experiments indicate that there is a specific interaction between DA D(2) and adenosine A(2A) receptors, which could be related to the co-localization of these receptors on the same population of striatal neurons.
Recent success in training artificial agents and robots derives from a combination of direct learning of behavioural policies and indirect learning through value functions1,2,3. Policy learning and value learning use distinct algorithms that optimize behavioural performance and reward prediction, respectively. In animals, behavioural learning and the role of mesolimbic dopamine signalling have been extensively evaluated with respect to reward prediction4; however, so far there has been little consideration of how direct policy learning might inform our understanding5. Here we used a comprehensive dataset of orofacial and body movements to understand how behavioural policies evolved as naive, head-restrained mice learned a trace conditioning paradigm. Individual differences in initial dopaminergic reward responses correlated with the emergence of learned behavioural policy, but not the emergence of putative value encoding for a predictive cue. Likewise, physiologically calibrated manipulations of mesolimbic dopamine produced several effects inconsistent with value learning but predicted by a neural-network-based model that used dopamine signals to set an adaptive rate, not an error signal, for behavioural policy learning. This work provides strong evidence that phasic dopamine activity can regulate direct learning of behavioural policies, expanding the explanatory power of reinforcement learning models for animal learning6.
Here we develop a policy learning account of the acquisition of classical trace conditioning in which behaviour is optimized to minimize the latency to collect reward once it is available, inspired by observations of this process in naive mice. A multidimensional dataset of behavioural changes during acquisition could be seen to drive improvements in reward collection performance, and a novel policy learning model quantitatively accounted for the diverse learned behaviour of individual animals. mDA activity predicted by the component of this model that sets an adaptive learning rate closely matched fibre photometry recordings of mDA activity made continuously throughout learning. Individual differences in initial phasic mDA responses predicted learning outcome hundreds of trials later in a manner consistent with dopamine modulating learning rate. Optogenetic manipulation of ventral tegmental area (VTA) dopamine neurons was calibrated to physiological signals and triggered in closed-loop with behaviour to provide a key test of the hypothesis that phasic mDA activity modulates learning rate as a distinct alternative to signalling signed errors. Together, these results define a novel function for mesolimbic dopamine in adapting the learning rate of direct policy learning (summarized in Extended Data Fig. 10).
There are many opportunities to extend the current ACTR model formulation to capture more biological reality and evaluate the biologically plausible, but so far incompletely tested, cellular and circuit mechanisms of posited ACTR learning rules. There is prior evidence for the capacity of mDA activity to capture eligibility traces and modulate synaptic plasticity58,59; however, our behavioural data and modelling call for further examination of multiple coordinated learning rules governing reactive-like and preparatory-like learning. Given that adaptive control over the magnitude of learning rate can be a key determinant of machine learning performance in deep neural networks2,28 and RNNs35, studying how adaptive control of learning rates is implemented in animal brains, and especially across diverse tasks, may provide additional algorithmic insights to those developed here. Recent evidence also suggests that other neuromodulators in the brain may play distinct, putatively complementary roles in controlling the rate of learning60. Here we effectively identify a key heuristic apparent in phasic mDA activity that adapts learning rates to produce more stable and performant learning; however, we focused on a single behavioural learning paradigm and dopamine is known to be critical for a broad range of putative behavioural policies. Our work provides a perspective for future work to expand on and identify other aspects of mDA activity that may be critical for the adaptive control of learning from action.
Figure 2. Schematic drawing summarizing the effects of various pharmacological manipulations on PROG/chow feeding choice performance. Interference with dopamine (DA) transmission by giving DA antagonists or tetrabenazine decreases PROG lever pressing but does not suppress chow intake. In fact, chow intake was significantly increased by the D1 antagonist ecopipam, and also increased in animals treated with haloperidol and tetrabenazine that had high baseline rates of lever pressing (Randall et al., 2012, 2014). In contrast, interfering with the unconditioned reinforcing properties of food by reinforcer devaluation (pre-feeding) or by administration of appetite suppressant drugs (CB1 receptor antagonists/inverse agonists) decreases both PROG lever pressing and chow intake. Finally, blockade of adenosine A2A receptors or inhibition of DA uptake results in increased levels of PROG lever pressing (Randall et al., 2015; Yohn et al., 2016c).
Be it sugar, social media or sex, the response in our brain is the same: It produces the "feel-good" neurochemical called dopamine, which brings on feelings of pleasure and motivation. "It may be even more important for motivation than for actual pleasure," says Dr. Anna Lembke, a Stanford Medical School psychiatrist, researcher and author of the new book, Dopamine Nation: Finding Balance in the Age of Indulgence.
But, in modern life, we live in a world of abundance rather than scarcity, and Lembke says our brains weren't evolved for the "fire hose of dopamine" of sugar, social media, TV, sex, drugs or any number of dopamine-triggering stimuli so easily available. In short, Lembke says, almost every behavior has become "drugified."
When we're repeatedly exposed to our pleasure-producing stimuli, our brains adjust and, eventually, we need more and more just to feel "normal," or not in pain. That's called a "dopamine deficit state," and the cycle that leads us there can actually lead to depression, anxiety, irritability and insomnia.
A dopamine hit brings about pleasure and is then quickly followed by pain, or a come-down, in order to keep us motivated, says psychiatrist Dr. Anna Lembke. Meredith Miotke for NPR hide caption
In her work treating people with addiction, Lembke says she sees the most success in long-term recovery when people can't lie. She explains that even though we're often terrified of being radically honest with others because we think they'll go running, the opposite actually happens: radical honesty promotes intimacy. "And intimacy is an incredibly valuable and potent source of dopamine," she says. "We know that when we make intimate human connections, oxytocin binds dopamine, releasing neurons in the reward pathway and dopamine is released and it feels really good."
The identification of causative factors responsible for the preferential vulnerability of dopaminergic neurons of SNpc is still an unsolved quest in PD research and its purported molecular determinants have been recently reviewed by Brichta and Greengard [7]. The remaining challenge is still in understanding why mutations in various proteins with different or unclear physiological functions converge to similar pathological phenotypes, which are also observed in idiopathic PD cases [8]. Conversely, familial, environmental and idiopathic PD forms present some differences from both the histopathological and clinical point of view. For example, PD patients carrying Parkin, Pink1 or Lrrk2 mutation do not always present LBs [8, 9]. Moreover, patients differ in terms of age of onset, disease severity, progression of the neurodegeneration and type of symptoms (motor and non-motor).
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