[5] 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.
Work has mostly focused on replicating the analog nature of biological computation and the role of neurons in cognition.
[citation needed] The biological processes of neurons and their synapses are dauntingly complex, and thus very difficult to artificially simulate.
However, the characteristics of these chemical signals can be abstracted into mathematical functions that closely capture the essence of the neuron's operations.
The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,[11] spintronic memories, threshold switches, transistors,[12][4] among others.
[15] 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.
[16][17] In June 2012, spintronic researchers at Purdue University 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.
[19] 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.
[20] Neurogrid, built by Brains in Silicon at Stanford University,[21] is an example of hardware designed using neuromorphic engineering principles.
Each NeuroCore's analog circuitry is designed to emulate neural elements for 65536 neurons, maximizing energy efficiency.
Since the simulation of a complete human brain will require a powerful supercomputer, the current focus on neuromorphic computers is being encouraged.
[26] Other research with implications for neuromorphic engineering involve the BRAIN Initiative[27] and the TrueNorth chip from IBM.
[30] In 2022, researchers at MIT have reported the development of brain-inspired artificial synapses, using the ion proton (H+), for 'analog deep learning'.
[33][34] 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.
The songs were old Belgian and French flute minuets, from which the chip learned the rules at play and then applied them.
The European Union funded a series of projects at the University of Heidelberg, which led to the development of BrainScaleS (brain-inspired multiscale computation in neuromorphic hybrid systems), a hybrid analog neuromorphic supercomputer located at Heidelberg University, Germany.
It was developed as part of the Human Brain Project neuromorphic computing platform and is the complement to the SpiNNaker supercomputer (which is based on digital technology).
The architecture used in BrainScaleS mimics biological neurons and their connections on a physical level; additionally, since the components are made of silicon, these model neurons operate on average 864 times (24 hours of real time is 100 seconds in the machine simulation) faster than that of their biological counterparts.
[50] For example, a neuromemristive system may replace the details of a cortical microcircuit's behavior with an abstract neural network model.
[61] 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.
JAIC intends to rely heavily on neuromorphic technology to connect "every sensor (to) every shooter" within a network of neuromorphic-enabled units.
However, the fact that neuromorphic systems are designed to mimic a human brain gives rise to unique ethical questions surrounding their usage.
The report cites increased public concern with robots that are able to mimic or replicate human functions.
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.