  Liquid State Machines are brain inspired spiking neural networks (SNNs) with
random reservoir connectivity and bio-mimetic neuronal and synaptic models.
Reservoir computing networks are proposed as an alternative to deep neural
networks to solve temporal classification problems. Previous studies suggest
2nd order (double exponential) synaptic waveform to be crucial for achieving
high accuracy for TI-46 spoken digits recognition. The proposal of long-time
range (ms) bio-mimetic synaptic waveforms is a challenge to compact and power
efficient neuromorphic hardware. In this work, we analyze the role of synaptic
orders namely: {\delta} (high output for single time step), 0th (rectangular
with a finite pulse width), 1st (exponential fall) and 2nd order (exponential
rise and fall) and synaptic timescales on the reservoir output response and on
the TI-46 spoken digits classification accuracy under a more comprehensive
parameter sweep. We find the optimal operating point to be correlated to an
optimal range of spiking activity in the reservoir. Further, the proposed 0th
order synapses perform at par with the biologically plausible 2nd order
synapses. This is substantial relaxation for circuit designers as synapses are
the most abundant components in an in-memory implementation for SNNs. The
circuit benefits for both analog and mixed-signal realizations of 0th order
synapse are highlighted demonstrating 2-3 orders of savings in area and power
consumptions by eliminating Op-Amps and Digital to Analog Converter circuits.
This has major implications on a complete neural network implementation with
focus on peripheral limitations and algorithmic simplifications to overcome
them.
