Implementation of memristive neural networks with spike-rate-dependent plasticity synapse
Abstract
The property of changing resistance according to applied currents of memristors makes them candidates for emulating synapses in artificial neural networks. In this paper, we introduce a memristive synapse design into neural network circuits. Combined with modified integrate-and-fire (I&F) complementary metal-oxide-semiconducter (CMOS) neurons, the memristive neural network shows similarities to its biological counterpart, in respect of biologically realistic, current-controlled spikes and adaptive synaptic plasticity. Then, the spike-rate-dependent plasticity (SRDP) of the synapse, an extended protocol of the Hebbian learning rule, is originally implemented by the circuit. And some advanced neural activities including learning, associative memory and forgetting are realized based on the SRDP rule. These activities are comprehensively validated on a neural network circuit inspired by famous Pavlov’s dog-experiment with simulations and quantitative analyses.
Yide Zhang
Postdoctoral Fellow
My research is interdisciplinary and focused on developing new types of optical imaging techniques that could advance the work of other researchers and medical personnel in a wide variety of fields. Currently, I am developing next-generation photoacoustic and ultrafast imaging techniques that can observe biological and physical phenomena that are too fast to be imaged with existing methods. The observation of the ultrafast phenomena could provide a better understanding of the fundamentals of life and physical sciences. I am also developing novel quantum imaging approaches that can investigate biological organisms with an imaging performance that cannot be achieved using classical optical imaging. In my free time, I enjoy cooking, hiking, cycling, and traveling.