  The practical applications based on recurrent spiking neurons are limited due
to their non-trivial learning algorithms. The temporal nature of spiking
neurons is more favorable for hardware implementation where signals can be
represented in binary form and communication can be done through the use of
spikes. This work investigates the potential of recurrent spiking neurons
implementations on reconfigurable platforms and their applicability in temporal
based applications. A theoretical framework of reservoir computing is
investigated for hardware/software implementation. In this framework, only
readout neurons are trained which overcomes the burden of training at the
network level. These recurrent neural networks are termed as microcircuits
which are viewed as basic computational units in cortical computation. This
paper investigates the potential of recurrent neural reservoirs and presents a
novel hardware/software strategy for their implementation on FPGAs. The design
is implemented and the functionality is tested in the context of speech
recognition application.
