Memristors have been seen as the device that will finally get neural networks off of digital computer simulations, and onto their own real hardware. Although everything from metal oxides, ferroelectrics, organic polymers, and even slime molds has been tried, no suitable substrate has been found for making memristor networks with the required precision. Researchers have now found a way to micro-fabricate complete neural networks without using CMOS technology at all — or for that matter, without any transistors.

The secret is to build thin stacks of the same old oxide memristor material as before (aluminum and titanium) — only this time, use a low temperature sputtering process that enables monolithic three-dimensional integration. A memristor, named for ‘memory-resistor,’ has an electrical resistance that depends on the history of the current that has flowed through the device — namely, how much current and in what direction. In general, the more current that goes through it, the easier it will travel through in the future. When power to the device is turned off, it ‘remembers’ its most recent resistance value until power is turned back on.

As simple analog memory devices, many researchers have sought to use memristors to represent the synapses between neurons in artificial networks. A team of researchers from UC Santa Barbara and Stony Brook University has now used their new micro-fabrication methods to build a 12 x 12 memristive crossbar array, which implements a single layer perceptron. Perceptrons are old-school neural networks that can perform crude classifications and also do some simple pattern recognition. By training such networks on a set of example patterns (such as letters), and tuning the weights of the ‘synaptic’ connections, additional patterns or letters can then be recognized.

Typically, this recognition process involves updating the network connections through successive iterations of some synaptic weighting function, until the network finally settles into a stable state. The researcher’s perceptron had ten inputs, three outputs, and full interconnection among its nodes through 30 synapses with variable weights. As a test, their network successfully classified 3 × 3-pixel black-and-white images into three classes, with the result indicated by the state of the three output neurons. While these results are rather modest, the researchers anticipate that networks with a density of 100 billion synapses per square centimeter in each layer should soon be possible by shrinking the memristors down to 30nm across.

Scaling down to small dimensions isn’t the problem memristors face. The problem is scaling up to large networks. A perspective article accompanying the main paper claims that large memristor networks will do no less than ‘affect the future of computing.’ Others expect that massive memristor networks will enhance everything from laptops and phones, to mobile robots. But there is a huge fallacy lurking beneath these idle speculations.

Simple memristors won’t ever mimic the energy-lite features of brains (and therefore make for energy-lite machines) because the power of neurons is not their spikes, and the power of synapses is not their weights. The power is everything else neurons do — all the intangibles that shape-shifting cells constantly pull off to rebuild networks anew each day, minute, and second, as they self-evolve through time to become ever-smarter.

In other words, contrary to what many neuro bean-counters might estimate, the bulk of the energy spent by neurons is not in pumping ions to send fleeting pulses to each other, but rather, the real energy is spent in building physical relationships with other cells. As we argued recently in our post on subcellular information processing in cytoskeletal networks, each neuron is itself a universal machine (or at least the once-feral descendant of a protist that was), and is fully capable of what we might call the 4F behavioral suite — feeding, fighting, fleeing, and fornicating. On the other hand, each memrister is clearly not so well endowed. Once minted, very little, or zero, energy is earmarked for finessing memristor shape or function on the fly.

The idea that the brain is spending all this energy (20 watts or so) just to compute by integrating electrical pulses and sending along the result to its neighbors is no longer tenable. Neurons are actually much more efficient, as far as their perceived information processing functions, then we have given them credit. Many of the underlying presumptions about how neurons metabolize have been constructed on a rapidly crumbling foundation. For example, neuroscientists believed for decades that neurons didn’t have a sweet tooth. Instead they thought that the glial cells or astrocytes took up all the glucose that was needed from the blood and processed it for the neurons. New results coming out from labs around the world now suggest that this so-called ‘astrocyte-to-neuron lactate shuttle’ is no longer the best explanation for how neurons get their fuel.

Furthermore, while there have been some amateurish attempts to try to convert spikes into bits, and bits into spikes, futuristic ‘neuristor’ networks that can do something useful using spikes are nowhere on the roadmap of computing concepts. We are still basically at the single perceptron level of thought, and likely to stay there until we figure out where the power of the single cell to solve the basic problems of existence comes from. Only then will we be equipped to ask how they might be solving problems as societies of cells.

On a more positive note, memristors don’t necessarily have to be turned into full-blown brains to be of use. They still have promising potential applications as analog memory devices, or even multifunction logic elements. For example, one new kind of memristor based on amorphous SrTiO3 constructed using low (room) temperature niobium doping has just been shown to have superior switching and energy efficiency. At this point there are still many contenders for the perfect device material. But once found, high density integration to make fully functional memristor chips will likely be the next step.