In this study, we solved a noisy spatiotemporal spike pattern detection task on an analog neuromorphic chip using an unsupervised learning rule. Spike-timing-dependent plasticity (STDP) is the most widespread unsupervised learning rule implemented in Spiking Neural Networks (SNNs) and neuromorphic chips. It has been shown to perform well in conventional benchmark tasks such as spike pattern classification and image classification in SNN simulations. However, a significant performance gap exists between its ideal model simulation and neuromorphic implementation. The learning rate of STDP learning depends on the resolution of synaptic efficacy, high resolution efficacy leads to a small learning rate and stable performance. In computer simulation, synaptic efficacy is configured using 64-bit floating-point precision whereas in low-power neuromorphic chips the resolution is generally restricted to under 5-bit fixed point precision due to silicon area and power constraints. This leads to a degradation in the performance. To solve this problem we proposed a bioinspired learning rule named adaptive STDP learning in a previous study and demonstrated via numerical simulation that the performance of adaptive STDP learning  (using 4-bit fixed point synapses) is similar to STDP learning (using 64-bit floating-point precision) in a noisy spatiotemporal spike pattern detection task. In this study, we present the experimental results for the same. The experimental results are similar to those obtained in our simulation-based study. To our best knowledge, this is the first time that an unsupervised, noisy spatiotemporal spike pattern detection task has been demonstrated to perform well on a mixed-signal CMOS neuromorphic chip with low-resolution synaptic efficacy.