Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural encoding. We believe that our study shed lights on the mechanism underlying the efficient neural information processing via adaptation.

Luozheng Li

Yuanyuan Mi

Wenhao Zhang

Da-Hui Wang

Si Wu

Frontiers in Computational Neuroscience

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 是一本一流的电子期刊，致力于推广大脑功能的理论建模，促进理论和实验神经科学之间的跨学科互动。对大脑惊人能力的理解进展仍然有限，我们相信只有深入的理论思考和不同学科和方法之间的相互促进合作才能实现。因此，我们邀请对广泛主题的原创贡献，以展示这种合作的成果，或为未来的联盟提供刺激。我们的目标是为神经系统的前沿理论研究提供一个互动论坛，并向更广泛的神经科学界传播最好的理论研究。从生物物理驱动的神经元和突触的逼真模拟到推理和决策的高级抽象模型，各种风格和所有级别的模型都受到欢迎。虽然该杂志主要侧重于基于理论和驱动的研究，但我们欢迎验证和测试理论结论的实验研究。另外：COMP NEURO

Frontiers in Computational Neuroscience is a leading electronic journal dedicated to advancing theoretical modeling of brain functions and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Our understanding of the brain's remarkable capabilities remains limited, and we believe that only through deep theoretical reflection and collaborative efforts across various disciplines and methods can we achieve breakthroughs. Therefore, we invite original contributions on a wide range of topics to showcase the results of such collaboration or to inspire future alliances. Our goal is to provide an interactive forum for cutting-edge theoretical research on the nervous system and to disseminate the best theoretical studies to the broader neuroscience community. From biophysically driven realistic simulations of neurons and synapses to advanced abstract models of reasoning and decision-making, a variety of styles and all levels of models are welcome. While the journal primarily focuses on theory-driven research, we also welcome experimental studies that validate and test theoretical conclusions.

《Frontiers in Computational Neuroscience》是一本顶尖的电子期刊，致力于推动大脑功能的理论建模，促进理论与实验神经科学之间的跨学科互动。尽管我们对大脑的惊人能力的理解仍然有限，但我们相信，只有通过深入的理论思考和不同学科及方法之间的相互促进合作，才能取得突破。因此，我们邀请对广泛主题的原创贡献，以展示这种合作的成果，或为未来的联盟提供启发。我们的目标是为神经系统的前沿理论研究提供一个互动论坛，并向更广泛的神经科学界传播最优秀的理论研究。从生物物理驱动的神经元和突触的逼真模拟，到推理和决策的高级抽象模型，各种风格和所有级别的模型都受到欢迎。虽然该期刊主要侧重于基于理论和驱动的研究，但我们也欢迎验证和测试理论结论的实验研究。

Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

This study builds a large-size network with chemical synapses between neurons and demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural encoding.

Category	Quartile
医学	4
医学, 数学与计算生物学	3
医学, 神经科学	4

Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

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