Neuromorphic engineering
Neuromorphic engineering, also known as neuromorphic computing,[1][2][3] is a concept developed by Carver Mead,[4] 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.[5] 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,[6], spintronic memories,[7] threshold switches, and transistors.[8]
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.[9]
Examples
As early as 2006, researchers at Georgia Tech published a field programmable neural array.[10] 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.[11][12]
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.[13]
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,[14] a biologically-inspired device that mimics behavior found in neurons.[14] 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.[15]
Neurogrid, built by Brains in Silicon at Stanford University,[16] 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.[17][18]
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.[19] 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.[20] $1.3 billion has been allocated to the project by The European Commission.[21]
Other research with implications for neuromorphic engineering involves the BRAIN Initiative[22] and the TrueNorth chip from IBM.[23] Neuromorphic devices have also been demonstrated using nanocrystals, nanowires, and conducting polymers.[24]
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.[25][26]
Brainchip holdings will release an NSoC (neuromophic system on chip) processor called Akida in late 2019.[27]
Ethical Considerations
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.
Democratic Concerns
Significant ethical limitations may be placed on neuromorphic engineering due to public perception. Special Eurobarometer 382: Public Attitudes Towards Robots, a survey conducted by the European Commission, found that 60% of European Union citizens wanted a ban of robots in the care of children, the elderly, or the disabled. Furthermore, 34% were in favor of a ban on robots in education, 27% in healthcare, and 20% in leisure. The European Commission classifies these areas as notably “human.” The report cites increased public concern with robots that are able to mimic or replicate human functions. Neuromorphic engineering, by definition, is designed to replicate a human function: the function of the human brain.[28]
The democratic concerns surrounding neuromorphic engineering are likely to become even more profound in the future. The European Commission found that EU citizens between the ages of 15 and 24 are more likely to think of robots as human-like (as opposed to instrument-like) than EU citizens over the age of 55. When presented an image of a robot the had been defined as human-like, 75% of EU citizens aged 15-24 said it corresponded with the idea they had of robots while only 57% of EU citizens over the age of 55 responded the same way. The human-like nature of neuromorphic systems, therefore, could place them in the categories of robots many EU citizens would like to see banned in the future.[28]
Personhood
As neuromorphic systems have become increasingly advanced, some scholars have advocated for granting personhood rights to these systems. If the brain is what grants humans their personhood, to what extent does a neuromorphic system have to mimic the human brain to be granted personhood rights? Critics of technology development in the Human Brain Project, which aims to advance brain-inspired computing, have argued that advancement in neuromorphic computing could lead to machine consciousness or personhood.[29] If these systems are to be treated as people, critics argue, then many tasks humans perform using neuromorphic systems, including the act of termination of neuromorphic systems, may be morally impermissible as these acts would violate the autonomy of the neuromorphic systems.[30]
However, skeptics of this position 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.”[31]
Ownership and Property Rights
There is significant legal debate around property rights and artificial intelligence. In Acohs Pty Ltd v. Ucorp Pty Ltd, Justice Christopher Jessup of the Federal Court of Australia found that the source code for Material Safety Data Sheets could not be copyrighted as it was generated by a software interface rather than a human author.[32] The same question may apply to neuromorphic systems: if a neuromorphic system successfully mimics a human brain and produces a piece of original work, who, if anyone, should be able to claim ownership of the work?
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.[33] For example, a neuromemristive system may replace the details of a cortical microcircuit's behavior with an abstract neural network model.[34]
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,[35] face recognition[36] and object recognition.[37] They also find applications in replacing conventional digital logic gates.[38][39]
For ideal passive memristive circuits, it is possible to derive a differential equation for evolution of the internal memory of the circuit:[40]
as a function of the properties of the physical memristive network and the external sources. In the equation above, is the "forgetting" time scale constant, and is the ratio of off and on values of the limit resistances of the memristors, is the vector of the sources of the circuit and is a projector on the fundamental loops of the circuit. The diagonal matrix and vector and 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.
See also
References
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- ^ Zhao, W. S.; Agnus, G.; Derycke, V.; Filoramo, A.; Bourgoin, J. -P.; Gamrat, C. (2010). "Nanotube devices based crossbar architecture: Toward neuromorphic computing". Nanotechnology. 21 (17): 175202. Bibcode:2010Nanot..21q5202Z. doi:10.1088/0957-4484/21/17/175202. PMID 20368686.
- ^ The Human Brain Project SP 9: Neuromorphic Computing Platform on YouTube
- ^ Mead, Carver. "carver mead website". carvermead.
- ^ Mead, Carver (1990). "Neuromorphic electronic systems". Proceedings of the IEEE. 78 (10): 1629–1636. doi:10.1109/5.58356.
- ^ a b Maan, A. K.; Jayadevi, D. A.; James, A. P. (January 1, 2016). "A Survey of Memristive Threshold Logic Circuits". IEEE Transactions on Neural Networks and Learning Systems. PP (99): 1734–1746. arXiv:1604.07121. doi:10.1109/TNNLS.2016.2547842. ISSN 2162-237X. PMID 27164608.
- ^ "A Survey of Spintronic Architectures for Processing-in-Memory and Neural Networks", JSA, 2018
- ^ Zhou, You; Ramanathan, S. (August 1, 2015). "Mott Memory and Neuromorphic Devices". Proceedings of the IEEE. 103 (8): 1289–1310. doi:10.1109/JPROC.2015.2431914. ISSN 0018-9219.
- ^ Boddhu, S. K.; Gallagher, J. C. (2012). "Qualitative Functional Decomposition Analysis of Evolved Neuromorphic Flight Controllers". Applied Computational Intelligence and Soft Computing. 2012: 1–21. doi:10.1155/2012/705483.
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- ^ Poon, Chi-Sang; Zhou, Kuan (2011). "Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities". Frontiers in Neuroscience. 5: 108. doi:10.3389/fnins.2011.00108. PMC 3181466. PMID 21991244.
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- ^ a b Pickett, M. D.; Medeiros-Ribeiro, G.; Williams, R. S. (2012). "A scalable neuristor built with Mott memristors". Nature Materials. 12 (2): 114–7. Bibcode:2013NatMa..12..114P. doi:10.1038/nmat3510. PMID 23241533.
- ^ Matthew D Pickett and R Stanley Williams 2013 Nanotechnology 24 384002
- ^ Boahen, Kwabena (April 24, 2014). "Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations". Proceedings of the IEEE. 102 (5): 699–716. doi:10.1109/JPROC.2014.2313565.
- ^ Waldrop, M. Mitchell (2013). "Neuroelectronics: Smart connections". Nature. 503 (7474): 22–4. Bibcode:2013Natur.503...22W. doi:10.1038/503022a. PMID 24201264.
- ^ Benjamin, Ben Varkey; Peiran Gao; McQuinn, Emmett; Choudhary, Swadesh; Chandrasekaran, Anand R.; Bussat, Jean-Marie; Alvarez-Icaza, Rodrigo; Arthur, John V.; Merolla, Paul A.; Boahen, Kwabena (2014). "Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations". Proceedings of the IEEE. 102 (5): 699–716. doi:10.1109/JPROC.2014.2313565.
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- ^ "The Human Brain Project and Recruiting More Cyberwarriors". Retrieved February 22, 2013.
- ^ Neuromorphic computing: The machine of a new soul, The Economist, 2013-08-03
- ^ Modha, Dharmendra (August 2014). "A million spiking-neuron integrated circuit with a scalable communication network and interface". Science. 345 (6197): 668–673. Bibcode:2014Sci...345..668M. doi:10.1126/science.1254642. PMID 25104385.
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- ^ Lim, Daniel (June 1, 2014). "Brain simulation and personhood: a concern with the Human Brain Project". Ethics and Information Technology. 16 (2): 77–89. doi:10.1007/s10676-013-9330-5. ISSN 1572-8439.
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- ^ C. Merkel and D. Kudithipudi, "Neuromemristive extreme learning machines for pattern classification," ISVLSI, 2014.
- ^ Maan, A.K.; James, A.P.; Dimitrijev, S. (2015). "Memristor pattern recogniser: isolated speech word recognition". Electronics Letters. 51 (17): 1370–1372. doi:10.1049/el.2015.1428.
- ^ Maan, Akshay Kumar; Kumar, Dinesh S.; James, Alex Pappachen (January 1, 2014). "Memristive Threshold Logic Face Recognition". Procedia Computer Science. 5th Annual International Conference on Biologically Inspired Cognitive Architectures, 2014 BICA. 41: 98–103. doi:10.1016/j.procs.2014.11.090.
- ^ Maan, A.K.; Kumar, D.S.; Sugathan, S.; James, A.P. (October 1, 2015). "Memristive Threshold Logic Circuit Design of Fast Moving Object Detection". IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 23 (10): 2337–2341. arXiv:1410.1267. doi:10.1109/TVLSI.2014.2359801. ISSN 1063-8210.
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External links
![](http://upload.wikimedia.org/wikipedia/en/thumb/4/4a/Commons-logo.svg/30px-Commons-logo.svg.png)
- 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