Interplay of multiple synaptic plasticity features in filamentary memristive devices for neuromorphic computing

Forming-Free Tunable Analog Switching in WOx/TaOx Heterojunction for Emulating Electronic Synapses

In this work, the sputtered deposited WO_x/TaO_x switching layer has been studied for resistive random-access memory (RRAM) devices. Gradual SET and RESET behaviors with reliable device-to-device variability were obtained with DC voltage sweep cycling without an electroforming process. The memristor shows uniform switching characteristics, low switching voltages, and a high R_ON/R_OFF ratio (~10²). The transition from short-term plasticity (STP) to long-term potentiation (LTP) can be observed by increasing the pulse amplitude and number. Spike-rate-dependent plasticity (SRDP) and paired-pulse facilitation (PPF) learning processes were successfully emulated by sequential pulse trains. By reducing the pulse interval, the synaptic weight change increases due to the residual oxygen vacancy near the conductive filaments (CFs). This work explores mimicking the biological synaptic behavior and further development for next-generation neuromorphic applications.

Comparison of diverse resistive switching characteristics and demonstration of transitions among them in Al-incorporated HfO2-based resistive switching memory for neuromorphic applications

Diverse resistive switching behaviors are observed in the Pt/HfAlO_x/TiN memory device depending on the compliance current, the sweep voltage amplitude, and the bias polarity. We extensively compare three types of resistive switching characteristics in a Pt/HfAlO_x/TiN device in terms of endurance, ON/OFF ratio, linear conductance update, and read margin in a cross-point array structure for synaptic device applications. The bipolar resistive switching under positive set and negative reset shows better linear synaptic weight updates due to gradual switching than the bipolar resistive switching at the opposite polarity. The complementary resistive switching shows a higher read margin due to the current suppression at a low voltage regime. In addition, the potentiation and the depression can be adjusted at the same voltage polarity for a hardware neuromorphic system. Finally, we demonstrate the transition between bipolar resistive switching and complementary resistive switching, which could provide flexibility for different applications.

Diverse resistive switching behaviors are observed in the Pt/HfAlO_x/TiN memory device depending on the compliance current, the sweep voltage amplitude, and the bias polarity.

An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting

Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience. This network is designed to process an extracellular neural signal in an online and unsupervised fashion. The signal stream is continuously fed to the network and processed through several layers to output spike trains matching the truth after a short learning period requiring only few data. The network features an attention mechanism to handle the scarcity of action potential occurrences in the signal, and a threshold adaptation mechanism to handle patterns with different sizes. This method outperforms two existing spike-sorting algorithms at low signal-to-noise ratio (SNR) and can be adapted to process several channels simultaneously in the case of tetrode recordings. Such attention-based STDP network applied to spike-sorting opens perspectives to embed neuromorphic processing of neural data in future brain implants.

Short-Term Plasticity and Long-Term Potentiation in Artificial Biosynapses with Diffusive Dynamics

The development of electronic devices possessing the functionality of biological synapses is a crucial step toward replicating the capabilities of the human brain. Of the various materials that have been used to realize artificial synapses, renewable natural materials have the advantages of being abundant, inexpensive, biodegradable, and ecologically benign. In this study, we report a biocompatible artificial synapse based on a matrix of the biopolymer ι-carrageenan (ι-car), which exploits Ag dynamics. This artificial synapse emulates the short-term plasticity (STP), paired-pulse facilitation (PPF), and transition from STP to long-term potentiation (LTP) of a biological synapse. The above-mentioned characteristics are realized by exploiting the similarities between the Ag dynamics in the ι-car matrix and the Ca²⁺ dynamics in a biological synapse. By demonstrating a method that uses biomaterials and Ag dynamics to emulate synaptic functions, this study confirms that ι-car has the potential for constructing neuromorphic systems that use biocompatible artificial synapses.