The 2nd Memristor and Memristive Systems Symposium took place on Tuesday, February 2, 2010 at Sutardja Dai Hall, UC Berkeley. The 2010 symposium covered memristor technology updates, new device technologies (materials and fabrication), device models for CAD, novel circuits using memristors, and systems architecture harnessing memristor and memristive device properties. Read the rest of this entry »

Stanley Williams, whose team discovered the fourth fundamental circuit element, the memristor, gives a brief talk about how the device works. Read the rest of this entry »

The author of this post is Blaise L Mouttet.

The name Ted Hoff is familiar to many electrical and computer engineers for his work in developing the first microprocessor introduced by Intel. A lesser known contribution of Ted Hoff was the suggestion for a new type of circuit element called the memistor, an electrolytic memory element developed in 1960 which formed the basis for a neural circuit architecture called ADALINE (ADAptive LInear NEuron) developed by Stanford University professor Bernard Widrow. Read the rest of this entry »

Justin Mullins is the author of a nice post on the Memristor, appeard on 7/8/2009 on New Scientist. It does a nice job in describing the story of the memristor, from his theoretical discovery in 1971 by Leon Chua at the University of California, Berkeley, to his utilization by Stan Williams and Greg Snider at the HP Labs in Palo Alto, to the implementation of neural models, which involves the department that hosts the Neurdons!… Read the rest of this entry »

SyNAPSE is not a project DARPA undertook lightly. Many attempts at large-scale neuromorphic engineering have been made in the past. None met their goals. As such, SyNAPSE owes its existence to a number of recent game-changing developments. From HP Labs, the discovery of the memristor was one such keystone innovation. It took Greg Snider’s 2007 work in Nanotechnology, however, to establish memristors as a viable platform for the implementation of self-organizing recurrent neural networks.

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After running through the Businessweek article posted by Max, I am equally excited and nervous. Anyone has to be excited over the prospect of a new computing paradigm, though honestly I’m not sure what that looks like yet. These sorts of articles claim that computers will look more like brains, which is all well and good, because brains tend to do dominate the “competition”, i.e. computers, at messy things like object recognition and speech recognition. Conversely (and obviously), computers tend to dominate tasks amenable to decomposition into easily formalizable sequential steps, e.g. chess or even eye surgery. So, maybe we know what Deep Blue looks like, but what on Earth would a computer expert in messy things, a messy computer if you’ll excuse the phrase, even look like? We all agree that computers stink at these messy things, and if they didn’t stink at them it would be a huge boon to, well, humankind. So let’s make the computers more like brains so they can do what brains do so well! But how do we make computers, both in terms of hardware and software, more like brains?

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