Neuromorphic engineering, also known as neuromorphic computing,
is a concept developed by Carver Mead, in the late 1980s, describing the use of
very-large-scale integration (VLSI) systems containing electronic analog
circuits to mimic neuro-biological architectures present in the nervous system.
In recent times the term neuromorphic has been used to describe analog,
digital, mixed-mode analog/digital VLSI, and software systems that implement
models of neural systems (for perception, motor control, or multisensory
integration). The implementation of neuromorphic computing on the hardware
level can be realized by oxide-based memristors*, threshold switches, and transistors.
A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.
Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.
As early as 2006, researchers at Georgia Tech published a field programmable neural array. This chip was the first in a line of many increasingly complex arrays of floating gate transistors that allowed programmability of charge on the gates of MOSFETs to model the channel-ion characteristics of neurons in the brain and was one of the first cases of a silicon programmable array of neurons.
In November 2011, a group of MIT researchers created a computer chip that mimics the analog, ion-based communication in a synapse between two neurons using 400 transistors and standard CMOS manufacturing techniques.
In June 2012, Spintronic Researchers at Purdue presented a paper on design for a neuromorphic chip using lateral spin valves and memristors. They argue that the architecture they have designed works in a similar way to neurons and can therefore be used to test various ways of reproducing the brain's processing ability. In addition, these chips are significantly more energy-efficient than conventional ones.
Research at HP Labs on Mott memristors has shown that while they can be non-volatile, the volatile behavior exhibited at temperatures significantly below the phase transition temperature can be exploited to fabricate a neuristor, a biologically-inspired device that mimics behavior found in neurons. In September 2013 they presented models and simulations that show how the spiking behavior of these neuristors can be used to form the components required for a Turing machine.
Neurogrid, built by Brains in Silicon atStanford
University
A research project with implications for neuromorphic engineering is the Human Brain Project, a 10-year collaboration that is attempting to simulate a complete human brain in a supercomputer using biological data. It is made up of a group of researchers in neuroscience, medicine, and computing. Henry Markram, the project's co-director, has stated that the project is proposing to establish a new foundation, to explore and understand the brain and its diseases, and to use that knowledge to build new computing technologies. The three primary goals of the project are to better understand how the pieces of the brain fit and work together, to understand how to objectively diagnose and treat brain diseases, and to use the understanding of the human brain to develop neuromorphic computers. That the simulation of a complete human brain will require a supercomputer a thousand times more powerful than today's encourages the current focus on neuromorphic computers. $1.3 billion has been allocated to the project by The European Commission.
Other research with implications for neuromorphic engineering involves the BRAIN Initiative and the TrueNorth chip from IBM. Neuromorphic devices have also been demonstrated using nanocrystals, nanowires, and conducting polymers.
Neuromemristive systems are a subclass of neuromorphic computing systems that focus on the use of memristors to implement neuroplasticity. While neuromorphic engineering focuses on mimicking biological behavior, neuromemristive systems focus on abstraction. For example, a neuromemristive system may replace the details of a cortical microcircuit's behavior with an abstract neural network model.
There exist several neuron inspired threshold logic functions implemented with memristors that have applications in high level pattern recognition applications. Some of the applications reported in recently include speech recognition, face recognition and object recognition. They also find applications in replacing conventional digital logic gates.
For ideal passive memristive circuits, it is possible to derive a differential equation for evolution of the internal memory of the circuit.
A memristor (a portmanteau of memory resistor) is a hypothetical non-linear passive two-terminal electrical component relating electric charge and magnetic flux linkage. It was envisioned, and its name coined, in 1971 by circuit theorist Leon Chua. According to the characterizing mathematical relations, the memristor would hypothetically operate in the following way: The memristor's electrical resistance is not constant but depends on the history of current that had previously flowed through the device, i.e., its present resistance depends on how much electric charge has flowed in what direction through it in the past; the device remembers its history — the so-called non-volatility property. When the electric power supply is turned off, the memristor remembers its most recent resistance until it is turned on again.
In 2008, a team at HP Labs claimed to have found Chua's missing memristor based on an analysis of a thin film of titanium dioxide thus connecting the operation of RRAM devices to the memristor concept. The HP result was published in the scientific journal Nature. Following this claim, Leon Chua has argued that the memristor definition could be generalized to cover all forms of two-terminal non-volatile memory devices based on resistance switching effects. There are, however, some serious doubts as to whether the memristor can actually exist in physical reality. Additionally, some experimental evidence contradicts Chua's generalization since a non-passive nanobattery effect is observable in resistance switching memory. Chua also argued that the memristor is the oldest known circuit element, with its effects predating the resistor, capacitor and inductor.
These devices are intended for applications in nanoelectronic memories, computer logic and neuromorphic/neuromemristive computer architectures. In 2013, Hewlett-Packard CTO Martin Fink suggested that commercial availability of memristor memory could arrive as early as 2018. In March 2012, a team of researchers from HRL Laboratories and theUniversity of Michigan
A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.
Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.
Examples
As early as 2006, researchers at Georgia Tech published a field programmable neural array. This chip was the first in a line of many increasingly complex arrays of floating gate transistors that allowed programmability of charge on the gates of MOSFETs to model the channel-ion characteristics of neurons in the brain and was one of the first cases of a silicon programmable array of neurons.
In November 2011, a group of MIT researchers created a computer chip that mimics the analog, ion-based communication in a synapse between two neurons using 400 transistors and standard CMOS manufacturing techniques.
In June 2012, Spintronic Researchers at Purdue presented a paper on design for a neuromorphic chip using lateral spin valves and memristors. They argue that the architecture they have designed works in a similar way to neurons and can therefore be used to test various ways of reproducing the brain's processing ability. In addition, these chips are significantly more energy-efficient than conventional ones.
Research at HP Labs on Mott memristors has shown that while they can be non-volatile, the volatile behavior exhibited at temperatures significantly below the phase transition temperature can be exploited to fabricate a neuristor, a biologically-inspired device that mimics behavior found in neurons. In September 2013 they presented models and simulations that show how the spiking behavior of these neuristors can be used to form the components required for a Turing machine.
Neurogrid, built by Brains in Silicon at
A research project with implications for neuromorphic engineering is the Human Brain Project, a 10-year collaboration that is attempting to simulate a complete human brain in a supercomputer using biological data. It is made up of a group of researchers in neuroscience, medicine, and computing. Henry Markram, the project's co-director, has stated that the project is proposing to establish a new foundation, to explore and understand the brain and its diseases, and to use that knowledge to build new computing technologies. The three primary goals of the project are to better understand how the pieces of the brain fit and work together, to understand how to objectively diagnose and treat brain diseases, and to use the understanding of the human brain to develop neuromorphic computers. That the simulation of a complete human brain will require a supercomputer a thousand times more powerful than today's encourages the current focus on neuromorphic computers. $1.3 billion has been allocated to the project by The European Commission.
Other research with implications for neuromorphic engineering involves the BRAIN Initiative and the TrueNorth chip from IBM. Neuromorphic devices have also been demonstrated using nanocrystals, nanowires, and conducting polymers.
Neuromemristive Systems
Neuromemristive systems are a subclass of neuromorphic computing systems that focus on the use of memristors to implement neuroplasticity. While neuromorphic engineering focuses on mimicking biological behavior, neuromemristive systems focus on abstraction. For example, a neuromemristive system may replace the details of a cortical microcircuit's behavior with an abstract neural network model.
There exist several neuron inspired threshold logic functions implemented with memristors that have applications in high level pattern recognition applications. Some of the applications reported in recently include speech recognition, face recognition and object recognition. They also find applications in replacing conventional digital logic gates.
For ideal passive memristive circuits, it is possible to derive a differential equation for evolution of the internal memory of the circuit.
= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
*Memristor
A memristor (a portmanteau of memory resistor) is a hypothetical non-linear passive two-terminal electrical component relating electric charge and magnetic flux linkage. It was envisioned, and its name coined, in 1971 by circuit theorist Leon Chua. According to the characterizing mathematical relations, the memristor would hypothetically operate in the following way: The memristor's electrical resistance is not constant but depends on the history of current that had previously flowed through the device, i.e., its present resistance depends on how much electric charge has flowed in what direction through it in the past; the device remembers its history — the so-called non-volatility property. When the electric power supply is turned off, the memristor remembers its most recent resistance until it is turned on again.
In 2008, a team at HP Labs claimed to have found Chua's missing memristor based on an analysis of a thin film of titanium dioxide thus connecting the operation of RRAM devices to the memristor concept. The HP result was published in the scientific journal Nature. Following this claim, Leon Chua has argued that the memristor definition could be generalized to cover all forms of two-terminal non-volatile memory devices based on resistance switching effects. There are, however, some serious doubts as to whether the memristor can actually exist in physical reality. Additionally, some experimental evidence contradicts Chua's generalization since a non-passive nanobattery effect is observable in resistance switching memory. Chua also argued that the memristor is the oldest known circuit element, with its effects predating the resistor, capacitor and inductor.
These devices are intended for applications in nanoelectronic memories, computer logic and neuromorphic/neuromemristive computer architectures. In 2013, Hewlett-Packard CTO Martin Fink suggested that commercial availability of memristor memory could arrive as early as 2018. In March 2012, a team of researchers from HRL Laboratories and the
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