A Unified Phase-native Computational Principle Governs Hippocampal Spike Timing and Neural Coding

TL;DR

This paper introduces a unified computational principle called forced phase integration that explains hippocampal spike timing and neural coding, unifying phase locking and firing rate information through a complex-valued neuron model.

Contribution

The paper proposes the UCNeuron model based on forced phase integration, providing a mechanistic understanding of spike timing and coding in hippocampal neurons.

Findings

Reproduces biological spike-theta synchronization

Reevaluates slope-locking associations, attributing effects to oscillatory contamination

Establishes a unified phase-native principle of neural timing

Abstract

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.