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THE RELATIONSHIP BETWEEN THE DISTRIBUTION OF MOTOR UNIT MECHANICAL-PROPERTIES AND THE FORCES DUE TO RECRUITMENT AND TO rate coding FOR THE GENERATION OF MUSCLE FORCE

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BRAIN RESEARCH 1983年第2期264卷 311-315页

作者： HARRISON, PJ Dep. Physiol. Univ. Goteborg S-400 33 Goteborg Sweden

The relative contribution of recruitment and rate coding to the generation of muscle force appear to depend critically upon the distribution of motor unit mechanical properties. With this in mind it is reasoned that r... 详细信息

关键词： recruitment rate coding motor unit mechanical properties

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Optimum neural tuning curves for information efficiency with rate coding and finite-time window

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2015年第JUNE期9卷 67页

作者： Han, Fang Wang, Zhijie Fan, Hong Sun, Xiaojuan Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Donghua Univ Engn Res Ctr Dizitized Text Fash Technol Minist Educ Shanghai 201620 Peoples R China Donghua Univ Glorious Sun Sch Business Management Shanghai 201620 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100088 Peoples R China

An important question for neural encoding is what kind of neural systems can convey more information with less energy within a finite time coding window. This paper first proposes a finite-time neural encoding system, where the neurons in the system respond to a stimulus by a sequence of spikes that is assumed to be Poisson process and the external stimuli obey normal distribution. A method for calculating the mutual information of the finite-time neural encoding system is proposed and the definition of information efficiency is introduced. The values of the mutual information and the information efficiency obtained by using Logistic function are compared with those obtained by using other functions and it is found that Logistic function is the best one. It is further found that the parameter representing the steepness of the Logistic function has close relationship with full entropy, and that the parameter representing the translation of the function associates with the energy consumption and noise entropy tightly. The optimum parameter combinations for Logistic function to maximize the information efficiency are calculated when the stimuli and the properties of the encoding system are varied respectively. Some explanations for the results are given. The model and the method we proposed could be useful to study neural encoding system, and the optimum neural tuning curves obtained in this paper might exhibit some characteristics of a real neural system.

关键词： neural tuning curve information efficiency rate coding finite-time window logistic function

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Which spike train distance is most suitable for distinguishing rate and temporal coding?

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JOURNAL OF NEUROSCIENCE METHODS 2018年第0期299卷 22-33页

作者： Satuvuori, Eero Kreuz, Thomas CNR Inst Complex Syst Sesto Fiorentino Italy Univ Florence Dept Phys & Astron Sesto Fiorentino Italy Vrije Univ Amsterdam MOVE Res Inst Dept Human Movement Sci Amsterdam Netherlands

Background: It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses. One common way to assess similarity are spike train distances. These can be divided into spike-resolved, such as the Victor-Purpura and the van Rossum distance, and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance. New method: We use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates? Results: Spike-resolved distances always contain rate information even for parameters indicating time coding. This is an issue for reasonably high rates but beneficial for very low rates. In contrast, the operational range for detecting time coding of time-resolved distances is superior at normal rates, but these measures produce artefacts at very low rates. The RI-SPIKE-distance is the only measure that is sensitive to timing information only. Comparison with existing methods: While our results on rate-dependent expectation values for the spike-resolved distances agree with Chicharro et al. (2011), we here go one step further and specifically investigate applicability for very low rates. Conclusions: The most appropriate measure depends on the rates of the data being analysed. Accordingly, we summarize our results in one table that allows an easy selection of the preferred measure for any kind of data. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： Neuronal code rate coding Temporal coding Spike train distances Victor-Purpura distance van Rossum distance ISI-distance SPIKE-distance RI-SPIKE-distance rate dependence Floor effect

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Efficiency of rate and latency coding with respect to metabolic cost and time

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BIOSYSTEMS 2017年 161卷 31-40页

作者： Levakova, Marie Czech Acad Sci Inst Physiol Videnska 1083 Prague 14220 4 Czech Republic

Recent studies on the theoretical performance of latency and rate code in single neurons have revealed that the ultimate accuracy is affected in a nontrivial way by aspects such as the level of spontaneous activity of presynaptic neurons, amount of neuronal noise or the duration of the time window used to determine the firing rate. This study explores how the optimal decoding performance and the corresponding conditions change when the energy expenditure of a neuron in order to spike and maintain the resting membrane potential is accounted for. It is shown that a nonzero amount of spontaneous activity remains essential for both the latency and the rate coding. Moreover, the optimal level of spontaneous activity does not change so much with respect to the intensity of the applied stimulus. Furthermore, the efficiency of the temporal and the rate code converge to an identical finite value if the neuronal activity is observed for an unlimited period of time. (C) 2017 Elsevier B.V. All rights reserved.

关键词： rate coding Temporal coding Metabolic cost Fisher information

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Efficient Reservoir Encoding Method for Near-Sensor Classification with rate-coding Based Spiking Convolutional Neural Networks 16th

Efficient Reservoir Encoding Method for Near-Sensor Classifi...

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16th International Symposium on Neural Networks (ISNN)

作者： Yang, Xu Yu, Shuangming Liu, Liyuan Liu, Jian Wu, Nanjian Chinese Acad Sci Inst Semicond State Key Lab Superlattices & Microstruct Beijing 100083 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing 100083 Peoples R China

ISBN: (纸本)9783030228088;9783030228071

This paper proposes a general and efficient reservoir encoding method to encode information captured by spike-based and analog-based sensors into spike trains, which helps to realize near-sensor classification with rate-coding based spiking neural networks in real applications. The concept of reservoir is proposed to realize long-term residual information storage while encoding. This method has two configurable parameters, integration time and threshold, and they are determined optimal based on our analysis about encoding requirements. Trough different setting we proposed, reservoir encoding method can be configured as compression mode to compress sparse spike trains obtained from spike-based sensors, or conversion mode to convert pixel values captured by analog-based sensor into spike trains respectively. Verified on MNIST and SVHN dataset, the mapping relationship of information before and after encoding are linear, and the experimental results prove that rate-coding based spiking neural networks with our reservoir encoding method can realize high-accuracy and low-latency classification in two modes.

关键词： rate coding Reservoir encoding Near-sensor classification Spiking neural networks

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Distinct Cellular Mechanisms underlying rate-coding Principles in Primate Auditory Cortex Revealed by Intracellular Recordings in Awake Marmosets

Distinct Cellular Mechanisms underlying Rate-Coding Principl...

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第九届海内外华人神经科学家研讨会

作者： Lixia Gao Xiaoqin Wang Department of Biomedical Engineering Johns Hopkins University School of Medicine

A key computational principle for encoding time-varying signals in auditory and somatosensory cortices of monkeys is the opponent model of rate-coding by two distinct populations of neurons. However, the subthreshold mechanisms that give rise to this computation have not been revealed. Because the rate-coding neurons are only observed in awake conditions, it is especially challenging to probe their underlying cellular mechanisms. By using a novel intracellular recording technique that we developed in awake marmosets, we found that the two types of rate-coding neurons in auditory cortex exhibited distinct subthreshold responses. While the positive-monotonic neurons(monotonically increasing firing rate with increasing stimulus repetition frequency) displayed sustained depolarization at high repetition frequency, the negative-monotonic neurons(opposite trend) exhibited instead hyperpolarization at high repetition frequency but sustained depolarization at low repetition frequency. The combination of excitatory and inhibitory subthreshold events allows the cortex to represent time-varying signals through these two opponent neuronal populations.

关键词： temporal processing rate coding in vivo intracellular recording auditory cortex marmoset

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Distinct Cellular Mechanisms underlying rate-coding Principles in Primate Auditory Cortex Revealed by Intracellular Recordings in Awake Marmosets

Distinct Cellular Mechanisms underlying Rate-Coding Principl...

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第九届海内外华人神经科学家研讨会(The 9th Symposium for Chinese Neuroscientists Worldwide)

作者： LixiaGao Xiaoqin Wang Department of Biomedical Engineering Johns Hopkins University School of MedicineBaltimoreMD 21025USA

A key computational principle for encoding time-varying signals in auditory and somatosensory cortices of monkeys is the opponent model of rate-coding by two distinct populations of ***,the subthreshold mechanisms that give rise to this computation have not been *** the rate-coding neurons are only observed in awake conditions,it is especially challenging to probe their underlying cellular *** using a novel intracellular recording technique that we developed in awake marmosets,we found that the two types of rate-coding neurons in auditory cortex exhibited distinct subthreshold *** the positive-monotonic neurons(monotonically increasing firing rate with increasing stimulus repetition frequency)displayed sustained depolarization at high repetition frequency,the negative-monotonic neurons(opposite trend)exhibited instead hyperpolarization at high repetition frequency but sustained depolarization at low repetition *** combination of excitatory and inhibitory subthreshold events allows the cortex to represent time-varying signals through these two opponent neuronal populations.

关键词： temporal processing rate coding in vivo intracellular recording auditory cortex marmoset

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An Energy Efficient Residual Spiking Neural Network Accelerator With Ternary Spikes

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGrateD CIRCUITS AND SYSTEMS 2025年第1期44卷 395-400页

作者： Sun, Congyi Song, Wenqing Chen, Qinyu Dai, Chenyang Fu, Yuxiang Li, Li Nanjing Univ Sch Elect Sci & Engn Nanjing 210000 Peoples R China Leiden Univ Leiden Inst Adv Comp Sci NL-2300 RA Leiden Netherlands Nanjing Univ Sch Integrated Circuits Nanjing 210000 Peoples R China

Spiking neural networks (SNNs) use discrete binary spikes to transfer information between neurons, which is different from artificial neural networks (ANNs). Although event-based characteristics bring potential computation power and efficiency to SNNs, the long processing time window of discrete spikes leads to high latency. In this brief, a spike version of the residual network using ternary spikes is proposed. A shorter time window is required to achieve competitive performance because the ability to transfer information of the ternary spikes is strengthened. An SNN accelerator based on the proposed residual network with ternary spikes is designed and implemented with 28 nm CMOS technology, and the core area is 0.63 mm2. The proposed SNN accelerator achieves the classification accuracy of 92.07% on CIFAR-10 dataset with SResNet20 and only 6 time steps. The accelerator achieves 0.39 mJ energy consumption per frame with a throughput of 165.7 FPS when running at 500 MHz.

关键词： Neurons Residual neural networks Encoding Membrane potentials Computer architecture Convolutional codes Accuracy Hardware implementation rate coding residual network spiking neural network (SNN) ternary spikes

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SAR-TinySNN: A Lightweight Spiking Neural Network for SAR Target Recognition

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Wang, Junyu Sun, Hao Sun, Yuli Tang, Tao Lei, Lin Ji, Kefeng Natl Univ Def Technol State Key Lab Complex Electromagnet Environm Effec Changsha 410073 Peoples R China Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

Spiking neural networks (SNNs) are the third generation of neural networks that offer the advantages of low computational requirements, fast inference speed, and strong biological interpretability. This makes SNNs suitable for synthetic aperture radar (SAR) target recognition tasks, which are often constrained by limited computational power. This letter proposes SAR-TinySNN, a lightweight SNN architecture designed for SAR target recognition. Unlike existing SAR-related studies that predominantly rely on rate coding, SAR-TinySNN uses direct coding to encode SAR images, allowing for a more efficient coding method adapted to SAR images and achieving high target recognition accuracy, especially in scenarios with limited training samples. By integrating direct coding into a trainable SNN framework, SAR-TinySNN achieves competitive performance compared with traditional deep neural networks (DNNs) and deep SNNs on vehicle, aircraft, and ship SAR target recognition datasets, with faster inference times. The experimental results demonstrate the effectiveness of SAR-TinySNN for SAR target recognition.

关键词： Encoding Image coding Neurons Target recognition Training Radar polarimetry Synthetic aperture radar Computer architecture Computational modeling Membrane potentials Direct coding rate coding spiking neural network (SNN) synthetic aperture radar (SAR) target recognition

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Self-Rectifying Dynamic Memristor Circuits for Periodic LIF Refractory Period Emulation and TTFS/rate Signal Encoding

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SMALL 2025年第15期21卷 e2408233页

作者： You, Song-Xian Hong, Sheng-Jie Chen, Kuan-Ting Shih, Li-Chung Chen, Jen-Sue Natl Cheng Kung Univ Dept Mat Sci & Engn Tainan Taiwan Natl Cheng Kung Univ Acad Innovat Semicond & Sustainable Mfg Tainan Taiwan

Spiking Neural Networks (SNNs) have gained attention due to their potential to improve computational efficiency compared to traditional artificial neural networks. This study investigates the use of dynamic memristors Ta/IGZO/TaOx/Pt combined with peripheral circuits to emulate the leaky integrate-and-fire behavior of neurons, focusing on incorporating a refractory period. The refractory period is crucial as it prevents neurons from becoming overactive and ensures precise timing in signal processing. This improvement allows the memristor to mimic biological neuron behavior more accurately. The memristor's transient resistance exhibits nonlinear I-V hysteresis and changes in response to pulses, enabling functions of integration, leakage, and firing. Additionally, the memristor is configured as an encoder, converting external signals into voltage pulse sequences. Using coding methods, including rate coding and time-to-first-spike (TTFS) coding, the encoder demonstrates improved signal processing, with TTFS occurring within 21 to 62 ms and encoder frequencies from 2500 to 9500 Hz. Experimental results show that this approach enhances SNN performance, making it more suitable for real-time applications and complex temporal signal processing tasks. This research highlights the potential of dynamic memristors to bridge the gap between neurons and artificial neurons, paving the way for more efficient neuromorphic computing systems.

关键词： dynamic memristors encoders leaky integrate-and-fire (LIF) rate coding refractory period time-to-first-spike (TTFS)

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