Bioengineering Department, Imperial College London, SW72AZ, London, UK.
Nat Commun. 2019 Nov 7;10(1):5055. doi: 10.1038/s41467-019-12972-2.
Rewards influence plasticity of early sensory representations, but the underlying changes in circuitry are unclear. Recent experimental findings suggest that inhibitory circuits regulate learning. In addition, inhibitory neurons are highly modulated by diverse long-range inputs, including reward signals. We, therefore, hypothesise that inhibitory plasticity plays a major role in adjusting stimulus representations. We investigate how top-down modulation by rewards interacts with local plasticity to induce long-lasting changes in circuitry. Using a computational model of layer 2/3 primary visual cortex, we demonstrate how interneuron circuits can store information about rewarded stimuli to instruct long-term changes in excitatory connectivity in the absence of further reward. In our model, stimulus-tuned somatostatin-positive interneurons develop strong connections to parvalbumin-positive interneurons during reward such that they selectively disinhibit the pyramidal layer henceforth. This triggers excitatory plasticity, leading to increased stimulus representation. We make specific testable predictions and show that this two-stage model allows for translation invariance of the learned representation.
奖励影响早期感觉表象的可塑性,但其中的电路变化尚不清楚。最近的实验结果表明,抑制性电路可调节学习。此外,抑制性神经元受到多种远程输入的高度调制,包括奖励信号。因此,我们假设抑制性可塑性在调节刺激表象方面起着重要作用。我们研究了奖励的自上而下调制如何与局部可塑性相互作用,从而在电路中引起持久的变化。我们使用一个关于 2/3 层初级视觉皮层的计算模型,展示了中间神经元电路如何存储有关奖励刺激的信息,以便在没有进一步奖励的情况下指导兴奋性连接的长期变化。在我们的模型中,刺激调谐的生长抑素阳性中间神经元在奖励期间与钙调蛋白阳性中间神经元建立强连接,从而选择性地抑制此后的锥体细胞层。这引发了兴奋性可塑性,导致刺激表象增加。我们提出了具体的可测试预测,并表明该两阶段模型允许学习的代表性平移不变性。