  In this article, a novel neuro-inspired low-resolution online unsupervised
learning rule is proposed to train the reservoir or liquid of Liquid State
Machine. The liquid is a sparsely interconnected huge recurrent network of
spiking neurons. The proposed learning rule is inspired from structural
plasticity and trains the liquid through formation and elimination of synaptic
connections. Hence, the learning involves rewiring of the reservoir connections
similar to structural plasticity observed in biological neural networks. The
network connections can be stored as a connection matrix and updated in memory
by using Address Event Representation (AER) protocols which are generally
employed in neuromorphic systems. On investigating the 'pairwise separation
property' we find that trained liquids provide 1.36 $\pm$ 0.18 times more
inter-class separation while retaining similar intra-class separation as
compared to random liquids. Moreover, analysis of the 'linear separation
property' reveals that trained liquids are 2.05 $\pm$ 0.27 times better than
random liquids. Furthermore, we show that our liquids are able to retain the
'generalization' ability and 'generality' of random liquids. A memory analysis
shows that trained liquids have 83.67 $\pm$ 5.79 ms longer fading memory than
random liquids which have shown 92.8 $\pm$ 5.03 ms fading memory for a
particular type of spike train inputs. We also throw some light on the dynamics
of the evolution of recurrent connections within the liquid. Moreover, compared
to 'Separation Driven Synaptic Modification' - a recently proposed algorithm
for iteratively refining reservoirs, our learning rule provides 9.30%, 15.21%
and 12.52% more liquid separations and 2.8%, 9.1% and 7.9% better
classification accuracies for four, eight and twelve class pattern recognition
tasks respectively.
