面向同步边缘的最优输入混响与稳态自组织
Optimal input reverberation and homeostatic self-organization towards the edge of synchronization
AI总结：
针对神经元网络完全同步致病的问题，本文用映射神经元建模间隙连接密集网络，并引入局部突触耦合稳态机制，使网络鲁棒自组织到同步相变边缘以实现最优输入混响。
摘要：
瞬态或部分同步可用于计算，尽管完全同步的网络常与癫痫发作相关。在此，我们提出一种稳态机制，能够将神经元网络维持在同步转变的边缘，从而避免完全同步网络的有害后果。我们使用映射对神经元建模，因为它们在动力学上比积分发放模型更丰富，且比基于电导的方法计算效率更高。我们首先描述了一个由通过间隙连接耦合、具有不同紧张性放电频率的神经元组成的密集网络的同步相变。我们表明，在转变临界点，输入通过瞬态同步在网络活动中实现最优混响。随后，我们在突触耦合中引入局部稳态动力学，并表明它产生朝向该相变边缘的鲁棒自组织。我们讨论了这一自组织过程的潜在生物学后果，例如其与脑临界性假说的关系、其输入处理能力，以及其功能失调如何可能导致病理性同步。
英文摘要：
Transient or partial synchronization can be used to do computations, although a fully synchronized network is frequently related to epileptic seizures. Here, we propose a homeostatic mechanism that is capable of maintaining a neuronal network at the edge of a synchronization transition, thereby avoiding the harmful consequences of a fully synchronized network. We model neurons by maps since they are dynamically richer than integrate-and-fire models and more computationally efficient than conductance-based approaches. We first describe the synchronization phase transition of a dense network of neurons with different tonic spiking frequencies coupled by gap junctions. We show that at the transition critical point, inputs optimally reverberate through the network activity through transient synchronization. Then, we introduce a local homeostatic dynamic in the synaptic coupling and show that it produces a robust self-organization toward the edge of this phase transition. We discuss the potential biological consequences of this self-organization process, such as its relation to the Brain Criticality hypothesis, its input processing capacity, and how its malfunction could lead to pathological synchronization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
