Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity

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 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.While spontaneous activity in the brain is often seen as simple background noise, recent work has hypothesized that spontaneous activity instead reflects the brain’s learnt internal model. While several studies have proposed synaptic plasticity rules to generate structured spontaneous activities, the mechanism of learning and embedding transition statistics in spontaneous activity is still unclear. Using a computational model, we investigate the synaptic plasticity rules that learn dynamic spontaneous activity obeying appropriate transition statistics. Our results shed light on the learning mechanism of the brain’s internal model, which is a crucial step towards a better understanding of the role of spontaneous activity as an internal generative model of stochastic processes in complex environments.