  The human brain is the most powerful and efficient machine in existence
today, surpassing in many ways the capabilities of modern computers. Currently,
lines of research in neuromorphic engineering are trying to develop hardware
that mimics the functioning of the brain to acquire these superior
capabilities. One of the areas still under development is the design of
bio-inspired memories, where the hippocampus plays an important role. This
region of the brain acts as a short-term memory with the ability to store
associations of information from different sensory streams in the brain and
recall them later. This is possible thanks to the recurrent collateral network
architecture that constitutes CA3, the main sub-region of the hippocampus. In
this work, we developed two spike-based computational models of fully
functional hippocampal bio-inspired memories for the storage and recall of
complex patterns implemented with spiking neural networks on the SpiNNaker
hardware platform. These models present different levels of biological
abstraction, with the first model having a constant oscillatory activity closer
to the biological model, and the second one having an energy-efficient
regulated activity, which, although it is still bio-inspired, opts for a more
functional approach. Different experiments were performed for each of the
models, in order to test their learning/recalling capabilities. A comprehensive
comparison between the functionality and the biological plausibility of the
presented models was carried out, showing their strengths and weaknesses. The
two models, which are publicly available for researchers, could pave the way
for future spike-based implementations and applications.
