  Spiking Neural Networks (SNN) are more closely related to brain-like
computation and inspire hardware implementation. This is enabled by small
networks that give high performance on standard classification problems. In
literature, typical SNNs are deep and complex in terms of network structure,
weight update rules and learning algorithms. This makes it difficult to
translate them into hardware. In this paper, we first develop a simple
2-layered network in software which compares with the state of the art on four
different standard data-sets within SNNs and has improved efficiency. For
example, it uses lower number of neurons (3 x), synapses (3.5 x) and epochs for
training (30 x) for the Fisher Iris classification problem. The efficient
network is based on effective population coding and synapse-neuron co-design.
Second, we develop a computationally efficient (15000 x) and accurate
(correlation of 0.98) method to evaluate the performance of the network without
standard recognition tests. Third, we show that the method produces a
robustness metric that can be used to evaluate noise tolerance.
