Nanotechnology's road to artificial brains

(Nanowerk Spotlight) If you think that building an artificial human brain is science fiction, you are probably right – for now. But don't think for a moment that researchers are not working hard on laying the foundations for what is called neuromorphic engineering – a new interdisciplinary discipline that includes nanotechnologies and whose goal is to design artificial neural systems with physical architectures similar to biological nervous systems.
One of the key components of any neuromorphic effort is the design of artificial synapses. The human brain contains vastly more synapses than neurons – by a factor of about 10,000 – and therefore it is necessary to develop a nanoscale, low power, synapse-like device if scientists want to scale neuromorphic circuits towards the human brain level.
Recently, we reported the development of a hybrid nanoparticle-organic transistor that can mimic the main functionalities of a synapse ("Scientists use nanotechnology to try building computers modeled after the brain").
New research now suggests that memristor devices are capable of emulating the biological synapses with properly designed CMOS neuron components. A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. It has the special property that its resistance can be programmed (resistor) and subsequently remains stored (memory). Researchers at the University of Michigan (UM) have now demonstrated that a memristor can connect conventional circuits and support a process that is the basis for memory and learning in biological systems. This work was part of a DARPA-sponsored project with HRL Laboratories being the lead institution on the UM team.
"In a mammalian brain the computing units, neurons, are connected to each other through programmable junctions called synapses," Wei Lu, an assistant professor in the Department of Electrical Engineering and Computer Science, explains to Nanowerk. "The synaptic weight modulates how signals are transmitted between neurons and can in turn be precisely adjusted by the ionic flow through the synapse. A memristor by definition is a resistive device with inherent memory. It is in fact very similar to a synapse – they are both two-terminal devices whose conductance can be modulated by external stimuli with the ability to store (memorize) the new information."
Reporting their findings in a recent issue of Nano Letters ("Nanoscale Memristor Device as Synapse in Neuromorphic Systems"), Lu and his group fabricated a nanoscale silicon-based memristor to mimic a synapse.
Nanoscale memristor characteristics and its application as a synapse
Nanoscale memristor characteristics and its application as a synapse. (a) Schematic illustration of the concept of using memristors as synapses between neurons. The insets show the schematics of the two-terminal device geometry and the layered structure of the memristor. (b) Schematic of a neuromorphic with CMOS neurons and memristor synapses in a crossbar configuration. (Reprinted with permission from American Chemical Society)
In this setup, the silicon memristor consists of a pair of electrodes sandwiching an amorphous-silicon layer doped with silver atoms, with high silver concentration near the top electrode and low silver concentration near the bottom electrode.
"When a positive voltage is applied across the memristor, silver ions in the silicon layer will drift to the bottom electrode and increase the overall conductance of the device, and vice versa," explains Lu. "The new conductance state is maintained until the next voltage pulse is applied. By controlling the silver doping profile and other device parameters, we were able to show that the change in the memristor conductance is proportional to the time integral of the voltage applied across it. In other words, the device state is not determined by the existing signals but by the history of the applied signals."
According to Lu, this device behavior is consistent with the flux-controlled memristor device model first proposed by Leon Chua in 1971 ("Memristor-The missing circuit element").
"Furthermore, this property allows us to precisely control the memristor conductance with external stimuli – the longer the voltage pulse is applied across the memristor the larger the conductance change is. These properties essentially enable the memristor to mimic synaptic action."
For example, in their paper the team demonstrated that an electrical circuit consisting of CMOS 'neurons' and memristor synapses can achieve spike-timing dependent plasticity (STDP), an important synaptic activity.
What is exciting about these results is that it shows that memristors can behave just like synapses – they respond to neuron spikes and store information like biological synapses and they can connect a large number of neurons together like biological synapses.
These findings show that it is now possible to build a brain-like computer using electronic components, namely, transistors and memristors. The key is to realize the similarity between synapses and memristors.
Although traditional, digital computers have consistently increased in speed and complexity, they are limited by what is known as the von Neumann bottleneck – the fact that digital computers, no matter how fast, rely on the sequential processing of instructions and a separation between the central processing unit (CPU) and memory; they are "a word-at-a-time" devices.
For instance, one of today's most sophisticated supercomputer, IBM's Blue Gene/P, can accomplish certain tasks with the brain functionality of a cat, but it's a massive machine with more than 147,000 CPUs, 144 terabytes of memory and a dedicated power supply. And it still performs 83 times slower than a cat's brain.
"The key to the high efficiency of biological systems is the large connectivity (approximately, 10000 in a mammalian cortex) between neurons that offers highly parallel processing power" says Lu. "The synaptic weight between two neurons can be precisely adjusted by the ionic flow through them and it is widely believed that the adaptation of synaptic weights enables the biological systems to learn and function."
Besides their potential use in neuromorphic circuits to build brain-like computers, memristors such as the ones developed by Lu's group could help keep build faster and better circuits in other ways. First, they can provide high density storage needed for memory applications in conventional circuits. Second, new approaches to build circuits may be developed so that the increase in computing power does not come from the increase in raw device speed (clock frequency) but comes from the increase in computing efficiency instead. For example, earlier this month Hewlett-Packard Labs published an interesting paper in Nature discussing building logic circuits using a new state variable – resistance of a memristor instead charge that has been used in conventional circuits ("?Memristive? switches enable ?stateful? logic operations via material implication").
Lu says that the next step for his team is to work with HRL and other researchers in his team to build larger circuits that consists of hundreds of CMOS neurons and memristor synapses.
Watch a 6-minute memristor guide where R. Stanley Williams, whose team discovered the memristor (the fourth fundamental circuit element), gives us a quick whiteboard talk about how the device works.
Michael Berger By – Michael is author of three books by the Royal Society of Chemistry:
Nano-Society: Pushing the Boundaries of Technology,
Nanotechnology: The Future is Tiny, and
Nanoengineering: The Skills and Tools Making Technology Invisible
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