Hybrid neuromorphic computing, integrating Artificial Neural Networks (ANNs) and Spiking Neural Networks (SNNs), is a key approach to advancing Artificial General Intelligence (AGI). Current hybrid platforms are limited to Leaky Integrate-and-Fire (LIF) based SNNs, missing crucial biological neuron behaviors like bursting and adaptation. We propose a hybrid platform based on the TianjicX chip, enabling heterogeneous integration of multiple SNN models (LIF, Quadratic Integrate-and-Fire (QIF), and Izhikevich) alongside ANNs. Our platform employs a co-design strategy for computing and storage mechanisms, minimizing data movement. Simulations show that the co-design approach reduces energy consumption by 8.11% (48.67 mW) compared to TianjicX. The platform also demonstrates superior computational performance across SNN models. It achieves 95% classification accuracy on the MNIST dataset (3000 images, each being 28 pixel×28 pixel and single presentation), surpassing Open Date Index Name (ODIN) by 10.5%. This is achieved with a two-layer fully-connected Izhikevich network (784×800×10), where each synapse operates at 8-bit precision. The network processes 33900 images per second, using only 35 cores (21.88% of 160 cores) and delivering 896 billion operations per second. Furthermore, on ResNet-50, our platform shows a 3.12% increase in computing speed and 40.85 mW/frame reduction in energy consumption compared to the TianjicX chip.