Hippocampal neurons exhibit precise phase locking to network oscillations, but the computational principle governing this temporal precision is still unclear. Neural information is conveyed jointly by firing rates and spike timing, but existing models treat these dimensions separately, limiting mechanistic interpretation of spike-field coupling and its reported association with spectral features such as the aperiodic slope. Here we show that hippocampal phase locking emerges from a fundamental dynamical mechanism referred to as forced phase integration that separates neural information into orthogonal magnitude (what) and phase (when) coordinates. To formalize this principle, the unified complex-valued neuron (UCN) has been developed, a biologically grounded generative framework in which spike timing arises from phase accumulation while spike magnitude encodes instantaneous signal strength. This framework reproduces biological spike-theta synchronization and enables mechanistic re-evaluation of slope-locking associations, demonstrating that previously reported effects arise from oscillatory contamination rather than causal modulation. These findings establish a unified phase-native principle of neural timing and coding.

翻译：摘要：海马体神经元展现出与网络振荡精确的相位锁定，但控制这种时间精确性的计算原理仍不明确。神经信息由放电率和尖峰时序共同传递，但现有模型将这两个维度分开处理，限制了尖峰-场耦合及其与频谱特征（如非周期斜率）关联的机制性解释。本文证明，海马体相位锁定源自一种称为受迫相位整合的基本动力学机制，该机制将神经信息分离为正交的幅度（什么）和相位（何时）坐标。为形式化这一原理，我们开发了统一复值神经元模型，这是一个生物学基础的生成框架，其中尖峰时序源于相位累积，而尖峰幅度编码瞬时信号强度。该框架再现了生物尖峰-θ波同步，并实现了斜率-锁定关联的机制性重新评估，证明先前报告的效应源于振荡污染而非因果调制。这些发现确立了神经时序与编码的统一相位本原原理。