Neuromorphic systems are inspired by the structure, function and plasticity of biological nervous systems.
They are artificial neural systems that mimic algorithmic behavior of the biological animal systems through efficient adaptive and intelligent control techniques.
They are designed to adapt, learn from their environments, and make decisions like biological systems and not to perform better than them.
There are no efforts to eliminate deficiencies inherent in biological systems.
This field, called neuromorphic engineering, is evolving a new era in computing with a great promise for future medicine, healthcare delivery and industry.
It relies on plenty of experiences which nature offers to develop functional, reliable and effective artificial systems.
Neuromorphic computational circuits, designed to mimic biological neurons, are primitives based on the optical and electronic properties of semiconductor materials.
Dr.
Carver Mead, professor emeritus of California Institute of Technology (Caltech), Pasadena pioneered this field.
He reasoned that biological evolutionary trends over millions of years have produced organisms that engineers can study to develop better artificial systems.
By giving senses and sensory-based behavior to machines, these systems can possibly compete with human senses and brings an intersection between biology, computer science and electrical engineering.
Neuromorphic systems depend on parallel collective computation, adaptation, learning and memory implemented locally at each stage of processing within the artificial neurons (the computational elements).
Analog circuits, electrical circuits operated with continuous varying signals, are used to implement these algorithmic processes with transistors operated in the sub-threshold or weak inversion region (a region of operation in which transistors are designed to conduct current though the gate voltage is slightly lower than the minimum voltage, called threshold voltage, required for normal conduction to take place) where they exhibit exponential current-voltage characteristics and low currents.
This circuit paradigm produces high density and low power implementations of some functions that are computationally intensive when compared with other paradigms (triode and saturation operational regions).
A triode region is operating transistor with gate voltage above the threshold voltage but with the drain-source voltage lower than the difference between the gate-source voltage and threshold voltage.
For saturation region, the gate voltage is still above the threshold voltage but with the drain-source voltage above the difference between the gate-source voltage and threshold voltage.
Transistor has four terminals: drain, gate, source and bulk.
Current flows between the drain and the source when enough voltage is applied through the gate that enables conduction.
The bulk is the body of the transistor.
Artificial neuromorphic systems are applied in the areas of vision, hearing, olfaction, touch, learning, decision-making, pattern recognition among others to develop autonomous systems in robotics, vehicle guidance and traffic control, pattern recognizers etc.
As the systems mature, human parts replacements would become a major application area.
The fundamental principle is by observing how biological systems perform these functions robust artificial systems are designed.
So the 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.
Neurons, which have common shape, produce electricity or chemical signals to communicate with other neighboring ones.
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.
In conclusion, it may not have changed the world, but the prospects of neuromorphics in medicine are many and could possibly herald the era of bio-grade artificial electronics human organs.