After Transistors, Memristors

In the late 1940's pioneering work by John Bardeen and Walter Brattain at Bell Labs led to the creation of an electronic component that changed the landscape of the future.  This component is fundamental to nearly all electronic devices in existence today.  It lead to cheaper and more efficient devices, integrated circuits in the 1970's, and eventually modern computers where they are still used.  This device was the transistor.

Thirty years later in the 1970's, still another device was conceived by Leon Chua.  The device related magnetic flux linkage to electric charge.  It's inventor claims it's the oldest known circuit element, and predate the resistor, capacitor and inductor.  The element has the remarkable property that current flowing in one direction increases its resistance, where current flowing the opposite direction decreases it.  The name of this device is the memristor.  Memristor's weren't even physically built until 2008.

Neuromorphic computing is an attempt to mimic biological neurons with electronic devices.  Scientists and engineers seeking to build neuromorphic systems are very interested in the device because of its base property.  Here's why.  Take any neural sensory preceptor (pressure, taste, etc) it produces a response that begins to diminish over time.  In other words, when you first apply pressure to your skin you feel it right away, but if you continue to apply constant pressure, the feeling diminishes.  This is called training.

Now imagine a electronic pressure sensor that produces a current when you apply pressure to it.  Placing a memristor wired into the circuit that is oriented so that it's resistance increases over time when a current is applied decreases the output current over time.  This simulates the diminishing of the response to the stimulus.  In other words, it is an electronic analog of training a neuron.

The memristor can also be wired in reverse to train for reinforcing behaviour as well.  By arranging memristors in a pattern and applying different inputs with positive and negative reinforcement in combination along with using ordinary resistors to apply weighting to the inputs, it can already be seen what a powerful building block we have.  Given the combination memristors, resistors, and economy of scale, it is certainly possible to have powerful problem solving, and maybe even sentient, neuromorphic systems.  Especially when you consider that there are billions of transistors on a modern commercial processor.