Abstract
Neural activity in dopaminergic areas such as the ventral tegmental area is influenced by timing processes, in particular by the temporal expectation of rewards during Pavlovian conditioning. Receipt of a reward at the expected time allows to compute reward-prediction errors which can drive learning in motor or cognitive structures. Reciprocally, dopamine plays an important role in the timing of external events. Several models of the dopaminergic system exist, but the substrate of temporal learning is rather unclear. In this article, we propose a neuro-computational model of the afferent network to the ventral tegmental area, including the lateral hypothalamus, the pedunculopontine nucleus, the amygdala, the ventromedial prefrontal cortex, the ventral basal ganglia (including the nucleus accumbens and the ventral pallidum), as well as the lateral habenula and the rostromedial tegmental nucleus. Based on a plausible connectivity and realistic learning rules, this neuro-computational model reproduces several experimental observations, such as the progressive cancelation of dopaminergic bursts at reward delivery, the appearance of bursts at the onset of reward-predicting cues or the influence of reward magnitude on activity in the amygdala and ventral tegmental area. While associative learning occurs primarily in the amygdala, learning of the temporal relationship between the cue and the associated reward is implemented as a dopamine-modulated coincidence detection mechanism in the nucleus accumbens.
Highlights
Dopamine (DA) is a key neuromodulator influencing processing and learning in many brain areas, such as the basal ganglia (Bolam et al, 2000; Haber et al, 2000), the prefrontal cortex (GoldmanRakic et al, 1992; Seamans and Yang, 2004) or the amygdala (Bissière et al, 2003; Pape and Pare, 2010)
If the reward is expected but omitted, ventral tegmental area (VTA) cells show a complete and long-lasting pause in firing shortly after the time when the US was expected; if the reward is delivered earlier than expected, VTA cells respond phasically as if it were not predicted, but do not show a dip at the expected time (Hollerman and Schultz, 1998). This phasic behavior linked to temporal expectation of reward indicates that timing mechanisms play an important role in dopaminergic activation
We present a neuro-computational model incorporating many areas involved in appetitive conditioning and reward processing, including the amygdala, the ventral basal ganglia and various forebrain nuclei projecting to VTA/substantia nigra pars compacta (SNc)
Summary
Dopamine (DA) is a key neuromodulator influencing processing and learning in many brain areas, such as the basal ganglia (Bolam et al, 2000; Haber et al, 2000), the prefrontal cortex (GoldmanRakic et al, 1992; Seamans and Yang, 2004) or the amygdala (Bissière et al, 2003; Pape and Pare, 2010). If the reward is expected but omitted, VTA cells show a complete and long-lasting pause (or dip) in firing shortly after the time when the US was expected; if the reward is delivered earlier than expected, VTA cells respond phasically as if it were not predicted, but do not show a dip at the expected time (Hollerman and Schultz, 1998) This phasic behavior linked to temporal expectation of reward (cancelation of US-related bursts after sufficient training, pause in firing after reward omission, normal bursts if the reward is delivered earlier) indicates that timing mechanisms play an important role in dopaminergic activation. The objective of this article is to propose a neuro-computational model incorporating the afferent structures to the dopaminergic system which are involved in appetitive conditioning and to better describe the neural mechanisms leading to the observed temporal behavior of dopaminergic neurons
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