奖励学习中的神经变化
一些个体在完成有具体目标的任务时能够比其他个体更成功地获得奖励，但有可能调控这种以奖励为目的的学习活动的神经变化却不是很清楚。Tye等人训练大鼠来自己管理一种蔗糖奖励，发现奖励学习依赖于杏仁核（大脑中一个对情绪学习很重要的区域）中增加的活动及突触力量。不同动物所达到的学习水平与突触力量增强的程度有很强关联。增强对奖励学习过程中大脑变化的了解，将有助于为自然奖励学习缺陷或失常的奖励学习症状如药物上瘾或饮食失调等制定治疗干预方案。
Further, the greater the
proportion of neurons recruited to encode the reward-predictive cue,
the better the rat learned the cue–reward association, and the more
successful the rat was at earning rewards.
Because our in vivo recordings showed rapidly occurring changes
in cue-related firing in the LA during successful cue–reward learning,
we proposed that the mechanism underlying these changes was an
increase in synaptic strength of thalamic or cortical sensory afferents
onto LA neurons; we tested this hypothesis with ex vivo experimentation
(Supplementary Fig. 6). Rats were trained on a single session of
the same behavioural model and classified as learners (top 50%) or
non-learners (bottom 50%) as defined by our learning indices of task
efficiency and task accuracy (Supplementary Fig. 7).
These findings provide evidence of
a connection between LA synaptic plasticity and cue–reward learning,
potentially representing a key mechanism underlying goaldirected
behaviour.
Basolateral amygdala (BLA) neurons are phasically responsive to
reward-predictive cues8–11, which is consistent with the idea that cueevoked
neuronal firing emerges as a consequence of cue–reward
associations.
Recognizing that a cue predicts a reward enhances an
animal’s ability to acquire that reward; however, the cellular and
synaptic mechanisms that underlie cue–reward learning are
unclear. Here we show that marked changes in both cue-induced
neuronal firing and input-specific synaptic strength occur with the
successful acquisition of a cue–reward association within a single
training session.
but the results are generally consistent in showing that training results in activity changes within a network of
brain regions previously implicated in domain-general aspects of WM (e.g., dorsolateral prefrontal cortex,posterior parietal cortex, basal ganglia)
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Gläscher J, Daw N, Dayan P, O’Doherty JP. 2010. States versus
rewards: dissociable neural prediction error signals underlying
model-based and model-free reinforcement learning.
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