Memristive Spiking Neural Networks Trained with Unsupervised STDP

Abstract

Neuromorphic computing systems are promising alternatives in the fields of pattern recognition, image processing, etc. especially when conventional von Neumann architectures face several bottlenecks. Memristors play vital roles in neuromorphic computing systems and are usually used as synaptic devices. Memristive spiking neural networks (MSNNs) are considered to be more efficient and biologically plausible than other systems due to their spike-based working mechanism. In contrast to previous SNNs with complex architectures, we propose a hardware-friendly architecture and an unsupervised spike-timing dependent plasticity (STDP) learning method for MSNNs in this paper. The architecture, which is friendly to hardware implementation, includes an input layer, a feature learning layer and a voting circuit. To reduce hardware complexity, some constraints are enforced: the proposed architecture has no lateral inhibition and is purely feedforward; it uses the voting circuit as a classifier and does not use additional classifiers; all neurons can generate at most one spike and do not need to consider firing rates and refractory periods; all neurons have the same fixed threshold voltage for classification. The presented unsupervised STDP learning method is time-dependent and uses no homeostatic mechanism. The MNIST dataset is used to demonstrate our proposed architecture and learning method. Simulation results show that our proposed architecture with the learning method achieves a classification accuracy of 94.6%, which outperforms other unsupervised SNNs that use time-based encoding schemes.

A Spiking Neural Network Based on the Model of VO2–Neuron

“…The network training process is based on the standard STDP mechanism [14,16,[19][20][21]38]. This mechanism is an implementation of the Hebbian learning rule and causes a change in synaptic weight depending on the delay ∆t in-out between pre-synaptic and post-synaptic spikes [45].…”

“…In other cases, the resistance Rw_i, j increases. Typically, the STDP-based training procedure is used in SNN with unsupervised learning [19][20][21]42,44]. In this case, when training input data is supplied, the output neurons are randomly associated with input data patterns.…”

“…Although such SNNs currently provide impressive performance results [14], any emulation loses hardware implementation in performance and energy efficiency [15,16]. Therefore, the development of SNNs based on microelectronic elements attracts the active attention of researchers [16][17][18][19][20][21][22]. One of the most frequently used functional elements of SNN is a memristor [16], which is used for implementing customizable weights and as a functional element of a neuron.…”

“…The coding of information in the proposed network is performed by setting the delay time of the spikes in the input layer relative to the zero time moment (time to the first spike) [37]. Network training is performed according to the spike time-dependent plasticity (STDP) scheme [14,16,[19][20][21]38]. As a result, a model SNN, based on VO 2 neurons, which allows pattern recognition, is presented and investigated in this study.…”

VLSI Implementation of Restricted Coulomb Energy Neural Network with Improved Learning Scheme

“…Artificial intelligence (AI) has been widely used to optimize data-driven approaches in fields such as computer vision, speech recognition, robotics and medical applications [1,2]. Deep neural networks (DNNs), also referred to as deep learning, are a part of the broad field of AI, and deliver state-of-the-art accuracy on many AI tasks [3,4].…”