Neuromorphic engineering

As early as 2006, researchers at Georgia Tech published a field programmable neural array.[11] This chip was the first in a line of 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.[12][13]

In June 2012, spintronic researchers at Purdue presented a paper on the design of a neuromorphic chip using lateral spin valves and memristors. They argue that the architecture works similarly to neurons and can therefore be used to test methods of reproducing the brain's processing. In addition, these chips are significantly more energy-efficient than conventional ones.[14]

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,[15] a biologically-inspired device that mimics behavior found in neurons.[15] 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.[16]

Neurogrid, built by Brains in Silicon at Stanford University,[17] is an example of hardware designed using neuromorphic engineering principles. The circuit board is composed of 16 custom-designed chips, referred to as NeuroCores. Each NeuroCore's analog circuitry is designed to emulate neural elements for 65536 neurons, maximizing energy efficiency. The emulated neurons are connected using digital circuitry designed to maximize spiking throughput.[18][19]

A research project with implications for neuromorphic engineering is the Human Brain Project 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.[20] Henry Markram, the project's co-director, has stated that the project proposes to establish a 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.[21] $1.3 billion has been allocated to the project by The European Commission.[22]

Other research with implications for neuromorphic engineering involves the BRAIN Initiative[23] and the TrueNorth chip from IBM.[24] Neuromorphic devices have also been demonstrated using nanocrystals, nanowires, and conducting polymers.[25]

Intel unveiled its neuromorphic research chip, called “Loihi”, in October 2017. The chip uses an asynchronous spiking neural network (SNN) to implement adaptive self-modifying event-driven fine-grained parallel computations used to implement learning and inference with high efficiency.[26][27]

IMEC, a Belgium-based nanoelectronics research center, demonstrated the world's first self-learning neuromorphic chip. The brain-inspired chip, based on OxRAM technology, has the capability of self-learning and has been demonstrated to have the ability to compose music.[28] IMEC released the 3--second tune composed by the prototype. The chip was sequentially loaded with songs in the same time signature and style. The songs were old Belgian and French flute minuets, from which the chip learned the rules at play and then applied them.[29]

While the interdisciplinary concept of neuromorphic engineering is relatively new, many of the same ethical considerations apply to neuromorphic systems as apply to human-like machines and artificial intelligence in general. However, the fact that neuromorphic systems are designed to mimic a human brain gives rise to unique ethical questions surrounding their usage.

However, the practical debate is that neuromorphic hardware as well as artificial "neural networks" are immensely simplified models of how the brain operates or processes information at a much lower complexity in terms of size and functional technology and a much more regular structure in terms of connectivity. Comparing neuromorphic chips to the brain is a very crude comparison similar to comparing a plane to a bird just because they both have wings and a tail. The fact is that neural cognitive systems are many orders of magnitude more energy and compute efficient than current state-of-the-art AI and neuromorphic engineering is an attempt to narrow this gap by inspiring from the brain's mechanism just like many engineering designs have bio-inspired features.

The Joint Artificial Intelligence Center, a branch of the U.S. military, is a center dedicated to the procurement and implementation of AI software and neuromorphic hardware for combat use. Specific applications include smart headsets/goggles and robots. JAIC intends to rely heavily on neuromorphic technology to connect "every fighter every shooter" within a network of neuromorphic-enabled units.

Skeptics have argued that there is no way to apply the electronic personhood, the concept of personhood that would apply to neuromorphic technology, legally. In a letter signed by 285 experts in law, robotics, medicine, and ethics opposing a European Commission proposal to recognize “smart robots” as legal persons, the authors write, “A legal status for a robot can’t derive from the Natural Person model, since the robot would then hold human rights, such as the right to dignity, the right to its integrity, the right to remuneration or the right to citizenship, thus directly confronting the Human rights. This would be in contradiction with the Charter of Fundamental Rights of the European Union and the Convention for the Protection of Human Rights and Fundamental Freedoms.”[34]

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.[36] For example, a neuromemristive system may replace the details of a cortical microcircuit's behavior with an abstract neural network model.[37]

There exist several neuron inspired threshold logic functions[6] implemented with memristors that have applications in high level pattern recognition applications. Some of the applications reported recently include speech recognition,[38] face recognition[39] and object recognition.[40] They also find applications in replacing conventional digital logic gates.[41][42]

For ideal passive memristive circuits, it is possible to derive a system of differential equations for evolution (Caravelli-Traversa-Di Ventra equation) of the internal memory of the circuit:[43]

${\displaystyle {\frac {d}{dt}}{\vec {W}}=\alpha {\vec {W}}-{\frac {1}{\beta }}(I+\xi \Omega W)^{-1}\Omega {\vec {S}}}$

as a function of the properties of the physical memristive network and the external sources. In the equation above, ${\displaystyle \alpha }$ is the "forgetting" time scale constant, ${\displaystyle \xi =r-1}$ and ${\displaystyle r={\frac {R_{\text{off}}}{R_{\text{on}}}}}$ is the ratio of off and on values of the limit resistances of the memristors, ${\displaystyle {\vec {S}}}$ is the vector of the sources of the circuit and ${\displaystyle \Omega }$ is a projector on the fundamental loops of the circuit. The constant ${\displaystyle \beta }$ has the dimension of a voltage and is associated to the properties of the memristor; its physical origin is the charge mobility in the conductor. The diagonal matrix and vector ${\displaystyle W=\operatorname {diag} ({\vec {W}})}$ and ${\displaystyle {\vec {W}}}$ respectively, are instead the internal value of the memristors, with values between 0 and 1. This equation thus requires adding extra constraints on the memory values in order to be reliable.

Wikimedia Commons has media related to Neuromorphic Engineering.

• Telluride Neuromorphic Engineering Workshop
• CapoCaccia Cognitive Neuromorphic Engineering Workshop
• Institute of Neuromorphic Engineering
• INE news site.
• Frontiers in Neuromorphic Engineering Journal
• Computation and Neural Systems department at the California Institute of Technology.
• Human Brain Project official site
• Building a Silicon Brain: Computer chips based on biological neurons may help simulate larger and more-complex brain models. May 1, 2019. SANDEEP RAVINDRAN