Theoretical analysis of spike-timing-dependent plasticity learning with memristive devices
Abstract
Several recent works, described in chapters of the present series, have shown that memristive devices can naturally emulate variations of the biological spike-timing-dependent plasticity (STDP) learning rule and can allow the design of learning systems. Such systems can be built with memristive devices of extremely diverse physics and behaviors and are particularly robust to device variations and imperfections. The present work investigates the theoretical roots of their STDP learning. It is suggested, by revisiting works developed in the field of computational neuroscience, that STDP learning can approximate the machine learning algorithm of Expectation-Maximization, the neural network operation implementing “Expectation” steps, while STDP itself implementing “Maximization” steps. This process allows a system to perform Bayesian inference among the values of a latent variable present in the input. This theoretical analysis allows interpreting how STDP differs for several device physics and why it is robust to devicemismatch. It can also provide guidelines for designing STDP-based learning systems. � Springer (India) Pvt. Ltd. 2017.