The fundamental philosophy of neuromorphic engineering is to utilize algorithmic inspiration of biological systems to engineer artificial systems. It is a kind of technology transfer from biology to engineering that involves the understanding of the functions and forms of the biological systems and consequent morphing into silicon chips.
For instance, the study of the structure of the muscle in an animal inspires the creation of locomotive robots that do not rely on heavy and power hungry servo motors. The fundamental thing is to understand how biological nerve tissues represent, communicate and process information. That would become the prelude to engineer electronic devices. Understanding the biologically algorithms of animals are vital and fundamental to reverse engineer the biological systems information representations and then develop systems that use these representations in their operations.
The fundamental biological unit mimicked in the design of neuromorphic systems is the neurons. Animal brain is composed of these individual units of computation, called neurons and the neurons are the elementary signaling parts of the nervous systems [9]. Neurons, which have common shape, produce electricity or chemical signals to communicate with other neighboring ones.
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Though these neurons are similar in shape, different connections with each other, muscles and receptors produce different computational results in biological systems: locomotive control, perception, sensory processing, auditory processing etc. Neuron is made of made up of input area (the dendrite) and output area (the axion) and is connected with other neurons by synapses.
Since neurons are the basic cells of the nervous systems of all kinds of animals, building silicon neurons (or neuromorphs) endowed with fundamental life-like characteristics, could enable the emulation or modeling of the neural networks in biological nervous systems.
By examining the retina for instance, artificial neurons that mimic the retinal neurons and chemistry are fabricated on silicon (most common material), gallium arsenide (GaAs) or possibly prospective organic semiconductor materials.
