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1. A Current-Mode Implementation of A Nearest Neighbor STDP Synapse

   Akwasi Akwaboah, Ralph Etienne-Cummings (June 2023, 21st IEEE Interregional NEWCAS Conference (NEWCAS))

   The Artificial Intelligence (AI) disruption continues unabated, albeit at extreme compute requirements. Neuromorphic circuits and systems offer a panacea for this extravagance. To this effect, event-based learning such as spike-timing-dependent plasticity (STDP) in spiking neural networks (SNNs) is an active area of research. Hebbian learning in SNNs fundamentally involves synaptic weight updates based on temporal correlations between pre- and post- synaptic neural activities. While there are broadly two approaches of realizing STDP, i.e. All-to-All versus Nearest Neighbor (NN), there exist strong arguments favoring the NN approach on the biologically plausibility front. In this paper, we present a novel current-mode implementation of a postsynaptic event-based NN STDP-based synapse. We leverage transistor subthreshold dynamics to generate exponential STDP traces using repurposed log-domain low-pass filter circuits. Synaptic weight operations involving addition and multiplications are achieved by the Kirchoff current law and the translinear principle respectively. Simulation results from the NCSU TSMC 180 nm technology are presented. Finally, the ideas presented here hold implications for engineering efficient hardware to meet the growing AI training and inference demands. 

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2. Spiking Domain Feature Extraction with Temporal Dynamic Learning

   https://doi.org/10.1109/ISQED57927.2023.10129326  

   Zheng, Honghao; Yi, Yang (April 2023, 2023 24th International Symposium on Quality Electronic Design (ISQED))

   Spiking neural network (SNN) has attracted more and more research attention due to its event-based property. SNNs are more power efficient with such property than a conventional artificial neural network. For transferring the information to spikes, SNNs need an encoding process. With the temporal encoding schemes, SNN can extract the temporal patterns from the original information. A more advanced encoding scheme is a multiplexing temporal encoding which combines several encoding schemes with different timescales to have a larger information density and dynamic range. After that, the spike timing dependence plasticity (STDP) learning algorithm is utilized for training the SNN since the SNN can not be trained with regular training algorithms like backpropagation. In this work, a spiking domain feature extraction neural network with temporal multiplexing encoding is designed on EAGLE and fabricated on the PCB board. The testbench’s power consumption is 400mW. From the test result, a conclusion can be drawn that the network on PCB can transfer the input information to multiplexing temporal encoded spikes and then utilize the spikes to adjust the synaptic weight voltage. 

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3. Nonlinear Ion Dynamics Enable Spike Timing Dependent Plasticity of Electrochemical Ionic Synapses

   https://doi.org/10.1002/adma.202418484  

   Huang, Mantao; Xu, Longlong; del_Alamo, Jesús_A; Li, Ju; Yildiz, Bilge (January 2025, Advanced Materials)

   Abstract Programmable synaptic devices that can achieve timing‐dependent weight updates are key components to implementing energy‐efficient spiking neural networks (SNNs). Electrochemical ionic synapses (EIS) enable the programming of weight updates with very low energy consumption and low variability. Here, the strongly nonlinear kinetics of EIS, arising from nonlinear dynamics of ions and charge transfer reactions in solids, are leveraged to implement various forms of spike‐timing‐dependent plasticity (STDP). In particular, protons are used as the working ion. Different forms of the STDP function are deterministically predicted and emulated by a linear superposition of appropriately designed pre‐ and post‐synaptic neuron signals. Heterogeneous STDP is also demonstrated within the array to capture different learning rules in the same system. STDP timescales are controllable, ranging from milliseconds to nanoseconds. The STDP resulting from EIS has lower variability than other hardware STDP implementations, due to the deterministic and uniform insertion of charge in the tunable channel material. The results indicate that the ion and charge transfer dynamics in EIS can enable bio‐plausible synapses for SNN hardware with high energy efficiency, reliability, and throughput. 

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4. Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems

   https://doi.org/10.1088/1361-6463/ac585b  

   Sueoka, Brandon; Zhao, Feng (March 2022, Journal of Physics D: Applied Physics)

   Abstract Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biological synapse with low power consumption, fast switching speed and simulation of synaptic plasticity, while natural organic materials are water soluble, renewable and environmental friendly. In this study, the potential of a natural organic material—honey-based memristor for SNNs was demonstrated. The device exhibited forming-free bipolar resistive switching, a high switching speed of 100 ns set time and 500 ns reset time, STDP and SRDP learning behaviours, and dissolving in water. The intuitive conduction models for STDP and SRDP were proposed. These results testified that honey-based memristive synaptic devices are promising for SNN implementation in green electronics and biodegradable neuromorphic systems. 

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5. Biologically inspired reinforcement learning for mobile robot collision avoidance

   Shim, Myung S; Li, Peng (May 2017, Proceedings of ... International Joint Conference on Neural Networks)

   Collision avoidance is a key technology enabling applications such as autonomous vehicles and robots. Various reinforcement learning techniques such as the popular Q-learning algorithms have emerged as a promising solution for collision avoidance in robotics. While spiking neural networks (SNNs), the third generation model of neural networks, have gained increased interest due to their closer resemblance to biological neural circuits in the brain, the application of SNNs to mobile robot navigation has not been well studied. Under the context of reinforcement learning, this paper aims to investigate the potential of biologically-motivated spiking neural networks for goal-directed collision avoidance in reasonably complex environments. Unlike the existing additive reward-modulated spike timing dependent plasticity learning rule (A-RM-STDP), for the first time, we explore a new multiplicative RM-STDP scheme (M-RM-STDP) for the targeted application. Furthermore, we propose a more biologically plausible feed-forward spiking neural network architecture with fine-grained global rewards. Finally, by combining the above two techniques we demonstrate a further improved solution to collision avoidance. Our proposed approaches not only completely outperform Q-learning for cases where Q-learning can hardly reach the target without collision, but also significantly outperform a baseline SNN with A-RMSTDP in terms of both success rate and the quality of navigation trajectories. 

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