Abstract
A fully-unsupervised learning algorithm for reaching self-organization in neuromorphic architectures is provided in this work. We experimentally demonstrate spike-timing dependent plasticity (STDP) in Oxide-based Resistive Random Access Memory (OxRAM) devices, and propose a set of waveforms in order to induce symmetric conductivity changes. An empirical model is used to describe the observed plasticity. A neuromorphic system based on the tested devices is simulated, where the developed learning algorithm is tested, involving STDP as the local learning rule. The design of the system and learning scheme permits to concatenate multiple neuromorphic layers, where autonomous hierarchical computing can be performed.
Highlights
The implementation of electronic synapses is nowadays one of the challenges of hardware-based neuromorphic engineering, which aims to design electronic circuits with a similar architecture and behavior to the one found in biological brains
Among the different technologies that have been proved to be suitable for synaptic applications, the oxide-based resistive random access memory (OxRAM) technology stands out when analog conductivity changes are required [1,2,3,4,5,6,7]
In order to get symmetrical spike-timing dependent plasticity (STDP) functions, instead of using identical pre and post-synaptic waveforms, we propose using the pair of synaptic pulse shapes shown in Figure 8a and Figure 8b waveforms, we propose using the pair of synaptic pulse shapes shown in Figure 8a and Figure, so the STDP function can be tuned in terms of biasing, according to the desired working
Summary
The implementation of electronic synapses is nowadays one of the challenges of hardware-based neuromorphic engineering, which aims to design electronic circuits with a similar architecture and behavior to the one found in biological brains. Within this context, the conductivity of an electronic device with memristive characteristics is identified as the weight or strength of a connection between two neurons (Figure 1), usually within a crossbar array which implements the synaptic matrix layer of an electronic neural network (Figure 2a). Among the different technologies that have been proved to be suitable for synaptic applications, the oxide-based resistive random access memory (OxRAM) technology stands out when analog conductivity changes are required [1,2,3,4,5,6,7]
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