Neuromorphic engineering
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain.
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
Neurological inspiration
Neuromorphic engineering is for now set apart by the inspiration it takes from what we know about the structure and operations of the brain. Neuromorphic engineering translates what we know about the brain's function into computer systems. Work has mostly focused on replicating the analog nature of biological computation and the role of neurons in cognition.
The biological processes of neurons and their
The goal of neuromorphic computing is not to perfectly mimic the brain and all of its functions, but instead to extract what is known of its structure and operations to be used in a practical computing system. No neuromorphic system will claim nor attempt to reproduce every element of neurons and synapses, but all adhere to the idea that computation is highly
Examples
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As early as 2006, researchers at Georgia Tech published a field programmable neural array.[12] 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
In June 2012,
Research at
Neurogrid, built by Brains in Silicon at Stanford University,[18] 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.[19][20]
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.[21] 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 powerful supercomputer encourages the current focus on neuromorphic computers.[22] $1.3 billion has been allocated to the project by The European Commission.[23]
Other research with implications for neuromorphic engineering involve the
), for 'analog deep learning'.[28][29]
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.[32] IMEC released the 30-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.[33]
The Blue Brain Project, led by Henry Markram, aims to build biologically detailed digital reconstructions and simulations of the mouse brain. The Blue Brain Project has created in silico models of rodent brains, while attempting to replicate as many details about its biology as possible. The supercomputer-based simulations offer new perspectives on understanding the structure and functions of the brain.
The European Union funded a series of projects at the University of Heidelberg, which led to the development of
In 2019, the European Union funded the project "Neuromorphic quantum computing"[35] exploring the use of neuromorphic computing to perform quantum operations. Neuromorphic quantum computing[36] (abbreviated as ‘n.quantum computing’) is an unconventional computing type of computing that uses neuromorphic computing to perform quantum operations.[37][38] It was suggested that quantum algorithms, which are algorithms that run on a realistic model of quantum computation, can be computed equally efficiently with neuromorphic quantum computing.[39][40][41][42][43] Both, traditional quantum computing and neuromorphic quantum computing are physics-based unconventional computing approaches to computations and don’t follow the von Neumann architecture. They both construct a system (a circuit) that represents the physical problem at hand, and then leverage their respective physics properties of the system to seek the “minimum”. Neuromorphic quantum computing and quantum computing share similar physical properties during computation.[43][44]
Neuromemristive systems
Neuromemristive systems is a subclass of neuromorphic computing systems that focuses on the use of
There exist several neuron inspired threshold logic functions
For (quasi)ideal passive memristive circuits, the evolution of the memristive memories can be written in a closed form (Caravelli-Traversa-
as a function of the properties of the physical memristive network and the external sources. The equation is valid for the case of the Williams-Strukov original toy model, as in the case of ideal memristors, . However, the hypothesis of the existence of an ideal memristor is debatable.[56] In the equation above, is the "forgetting" time scale constant, typically associated to memory volatility, while 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 constant 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 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.
It has been recently shown that the equation above exhibits tunneling phenomena and used to study Lyapunov functions.[57][55]
Neuromorphic sensors
The concept of neuromorphic systems can be extended to sensors (not just to computation). An example of this applied to detecting light is the retinomorphic sensor or, when employed in an array, the event camera. In 2022, researchers from the Max Planck Institute for Polymer Research reported an organic artificial spiking neuron that exhibits the signal diversity of biological neurons while operating in the biological wetware, thus enabling in-situ neuromorphic sensing and biointerfacing applications.[58][59]
Military applications
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 sensor (to) every shooter" within a network of neuromorphic-enabled units.
Ethical and legal 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.
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
Social concerns
Significant ethical limitations may be placed on neuromorphic engineering due to public perception.[60] 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 the function of the human brain.[61]
The social 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 that 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.[61]
Personhood
As neuromorphic systems have become increasingly advanced, some scholars[
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
See also
- AI accelerator
- Artificial brain
- Biomorphic
- Cognitive computer
- Computation and Neural Systems
- Differentiable programming
- Event camera
- Hardware for artificial intelligence
- Lithionics
- Neurorobotics
- Optical flow sensor
- Physical neural network
- SpiNNaker
- SyNAPSE
- Retinomorphic sensor
- Unconventional computing
- Vision chip
- Vision processing unit
- Wetware computer
- Zeroth (software)
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External links
- 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