A brain is a complex structure where computing and memory are tightly intertwined at very low power cost of operation, by analog signals across vast quantities of synapse-connected spiking neurons. Animal brains react intelligently to environmental events and perceptions. By developing similar Spiking Neural Networks (SNN) we can realize neuromorphic computation systems excellent for dealing with large amounts of noisy data and stimuli and very well suited for perception, cognition and motor tasks. But the current CMOS technologies perform very poorly for emulating the biological brains and their power consumption is large. Currently we cannot replicate biological neurons behaviours with existing design and manufacturing technology. This project aims to develop compact miniature material elements that will emulate closely the complex dynamic behaviour of neurons and synapses, to form SNNs with substantial reduction in footprint, complexity and energy cost for perception, learning and computation. We investigate the properties of metal halide perovskite that have produced excellent photovoltaic devices in the last decade. These perovskites have ionic/electronic conduction, hysteresis, memory effect and switchable and nonlinear behaviour, that make them ideally suited for the realization of devices in close fidelity to biological electrochemically gated membranes in neurons, and information-tracking synapses. We will use the methodology of impedance spectroscopy and equivalent circuit analysis to fabricate devices with dynamic responses emulating the natural neuronal coupling and synchronization. This method will produce the hardware that we need for a preferred spiking computational model, incorporating time, analog physical elements and dynamical complexity as computational tools. As illustration we will show visual object recognition from spiking data provided by a spiking retina by advanced neuristors and dynamic synapses.