Resumen
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.
