  The paper presents Multi-layer Auto Resonance Networks (ARN), a new neural
model, for image recognition. Neurons in ARN, called Nodes, latch on to an
incoming pattern and resonate when the input is within its 'coverage.'
Resonance allows the neuron to be noise tolerant and tunable. Coverage of nodes
gives them an ability to approximate the incoming pattern. Its latching
characteristics allow it to respond to episodic events without disturbing the
existing trained network. These networks are capable of addressing problems in
varied fields but have not been sufficiently explored. Implementation of an
image classification and identification system using two-layer ARN is discussed
in this paper. Recognition accuracy of 94% has been achieved for MNIST dataset
with only two layers of neurons and just 50 samples per numeral, making it
useful in computing at the edge of cloud infrastructure.
