Today I read a paper titled “Memristor-based Synaptic Networks and Logical Operations Using In-Situ Computing”
The abstract is:
We present new computational building blocks based on memristive devices.
These blocks, can be used to implement either supervised or unsupervised learning modules.
This is achieved using a crosspoint architecture which is an efficient array implementation for nanoscale two-terminal memristive devices.
Based on these blocks and an experimentally verified SPICE macromodel for the memristor, we demonstrate that firstly, the Spike-Timing-Dependent Plasticity (STDP) can be implemented by a single memristor device and secondly, a memristor-based competitive Hebbian learning through STDP using a $1\times 1000$ synaptic network.
This is achieved by adjusting the memristor’s conductance values (weights) as a function of the timing difference between presynaptic and postsynaptic spikes.
These implementations have a number of shortcomings due to the memristor’s characteristics such as memory decay, highly nonlinear switching behaviour as a function of applied voltage/current, and functional uniformity.
These shortcomings can be addressed by utilising a mixed gates that can be used in conjunction with the analogue behaviour for biomimetic computation.
The digital implementations in this paper use in-situ computational capability of the memristor.