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Large-scale neural hardware systems are trending increasingly towards the “neuromimetic” architecture: a general-purpose platform that specialises the hardware for neural networks but allows flexibility in model choice. Since the model is not hard-wired into the chip, exploration of different neural and synaptic models is not merely possible but provides a rich field for research: the possibility to use the hardware to establish useful abstractions of biological neural dynamics that could lead to a functional model of neural computation. Two areas of neural modelling stand out as central: 1) What level of detail in the neurodynamic model is necessary to achieve biologically realistic behaviour? 2) What is role and effect of different types of synapses in the computation? Using a universal event-driven neural chip, SpiNNaker, we develop a simple model, the Leaky-Integrate-and-Fire (LIF) neuron, as a tool for exploring the second of these questions, complementary to the existing Izhikevich model which allows exploration of the first of these questions. The LIF model permits the development of multiple synaptic models including fast AMPA/GABA-A synapses with or without STDP learning, and slow NMDA synapses, spanning a range of different dynamic time constants. Its simple dynamics make it possible to expand the complexity of synaptic response, while the general-purpose design of SpiNNaker makes it possible if necessary to increase the neurodynamic accuracy with Izhikevich (or even Hodgkin-Huxley) neurons with some tradeoff in model size. Furthermore, the LIF model is a universally-accepted “standard” neural model that provides a good basis for comparisons with software simulations and introduces minimal risk of obscuring important synaptic effects due to unusual neurodynamics. The simple models run thus far demonstrate the viability of both the LIF model and of various possible synaptic models on SpiNNaker and illustrate how it can be use- - d as a platform for model exploration. Such an architecture provides a scalable system for high-performance large-scale neural modelling with complete freedom in model choice.