Spiking neural networks (SNNs) have emerged as a promising paradigm for effective event-driven computation. However, CMOS-based SNN designs are limited by power consumption and complexity, while nonvolatile memory (NVM)-based SNN designs often lack biological characteristics and require active capacitive circuits to emulate neuronal dynamics. In this paper, we propose a thermally interplayed spin-orbit torque magnetic tunnel junction (TI-MTJ) macro that integrates core SNN functionalities. Our neuron array autonomously achieves leaky integrate-and-fire (LIF) model within the TI-MTJ device, thus improving power efficiency and simplifying circuit structure. Additionally, the proposed synaptic array provides adaptive in-situ responses based on a simplified spike-timing-dependent plasticity (STDP) rule. To enhance biological plausibility, our macro incorporates real-time spike monitoring and inhibition mechanisms. A comprehensive device-circuit-algorithm co-optimization framework validates the high performance of the TI-MTJ macro, achieving a synaptic energy consumption of 6.07fJ per spike, an inference accuracy of 97.76% on the MNIST dataset, and an energy efficiency of 22.8TOPS/W.