Asabuki Toshitake, Clopath Claudia
Department of Bioengineering, Imperial College London, London, United Kingdom.
RIKEN Center for Brain Science, RIKEN ECL Research Unit, Wako, Japan.
Elife. 2025 Jun 11;13:RP95243. doi: 10.7554/eLife.95243.
The brain learns an internal model of the environment through sensory experiences, which is essential for high-level cognitive processes. Recent studies show that spontaneous activity reflects such a learned internal model. Although computational studies have proposed that Hebbian plasticity can learn the switching dynamics of replayed activities, it is still challenging to learn dynamic spontaneous activity that obeys the statistical properties of sensory experience. Here, we propose a pair of biologically plausible plasticity rules for excitatory and inhibitory synapses in a recurrent spiking neural network model to embed stochastic dynamics in spontaneous activity. The proposed synaptic plasticity rule for excitatory synapses seeks to minimize the discrepancy between stimulus-evoked and internally predicted activity, while inhibitory plasticity maintains the excitatory-inhibitory balance. We show that the spontaneous reactivation of cell assemblies follows the transition statistics of the model's evoked dynamics. We also demonstrate that simulations of our model can replicate recent experimental results of spontaneous activity in songbirds, suggesting that the proposed plasticity rule might underlie the mechanism by which animals learn internal models of the environment.
大脑通过感官体验学习环境的内部模型，这对高级认知过程至关重要。最近的研究表明，自发活动反映了这样一种学习到的内部模型。尽管计算研究提出赫布可塑性可以学习重放活动的切换动态，但学习遵循感官体验统计特性的动态自发活动仍然具有挑战性。在这里，我们在循环脉冲神经网络模型中为兴奋性和抑制性突触提出了一对生物学上合理的可塑性规则，以将随机动态嵌入自发活动中。所提出的兴奋性突触的突触可塑性规则旨在最小化刺激诱发活动和内部预测活动之间的差异，而抑制性可塑性则维持兴奋-抑制平衡。我们表明，细胞集合的自发重新激活遵循模型诱发动态的转变统计。我们还证明，我们模型的模拟可以复制最近关于鸣禽自发活动的实验结果，这表明所提出的可塑性规则可能是动物学习环境内部模型的机制基础。
