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temporal-code to rate-code conversion by neuronal phase-locked loops

NEURAL COMPUTATION 1998年第3期10卷 597-650页

作者： Ahissar, E Weizmann Inst Sci Dept Neurobiol IL-76100 Rehovot Israel

Peripheral sensory activity follows the temporal structure of input signals. Central sensory processing uses also rate coding, and motor outputs appear to be primarily encoded by rate. I propose here a simple, efficient structure, converting temporal coding to rate coding by neuronal phase-locked loops (PLL). The simplest form of a PLL includes a phase detector (that is, a neuronal-plausible version of an ideal coincidence detector) and a controllable local oscillator that are connected in a negative feedback loop. The phase detector compares the firing times of the local oscillator and the input and provides an output whose firing rate is monotonically related to the time difference. The output rate is fed back to the local oscillator and forces it to phase-lock to the input. Every temporal interval at the input is associated with a specific pair of output rate and time difference values;the higher the output rate, the further the local oscillator is driven from its intrinsic frequency. Sequences of input intervals, which by definition encode input information, are thus represented by sequences of firing rates at the PLL's output. The most plausible implementation of PLL circuits is by thalamocortical loops in which populations of thalamic "relay" neurons function as phase detectors that compare the timings of cortical oscillators and sensory signals. The output in this case is encoded by the thalamic population rate. This article presents and analyzes the algorithmic and the implementation levels of the proposed PLL model and describes the implementation of the PLL model to the primate tactile system.

关键词： .loops represented Thalamocortical Thalamic relay Spike Negative feedback ring rate phase-locked oscillators temporal coding temporal structure input signals rate coding outputs PLL phase detector local oscillator time difference output rate intrinsic frequency input information

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An Energy Efficient STDP-Based SNN Architecture With On-Chip Learning

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2022年第12期69卷 5147-5158页

作者： Sun, Congyi Sun, Haohan Xu, Jin Han, Jianing Wang, Xinyuan Wang, Xinyu Chen, Qinyu Fu, Yuxiang Li, Li Nanjing Univ Sch Elect Sci & Engn Nanjing 210000 Peoples R China Univ Zurich Inst Neuroinformat CH-8057 Zurich Switzerland Swiss Fed Inst Technol CH-8057 Zurich Switzerland Nanjing Univ Sch Integrated Circuits Nanjing 210000 Peoples R China

In this paper, we propose a spike-time based unsupervised learning method using spiking-timing dependent plasticity (STDP). A simplified linear STDP learning rule is proposed for the energy efficient weight updates. To reduce unnecessary computations for the input spike values, a stop mechanism of the forward pass is introduced in the forward pass. In addition, a hardware-friendly input quantization scheme is used to reduce the computational complexities in both the encoding phase and the forward pass. We construct a two-layer fully-connected spiking neuron network (SNN) based on the proposed method. Compared to general rate-based SNNs trained by STDP, the proposed method reduces the complexity of network architecture (an extra inhibitory layer is not needed) and the computations of synaptic weight updates. According to the fixed-point simulation with 9-bit synaptic weights, the proposed SNN with 6144 excitatory neurons achieves 96% of recognition accuracy on MNIST dataset without any supervision. An SNN processor that contains 384 excitatory neurons with on-chip learning capability is designed and implemented with 28 nm CMOS technology based on the proposed low complexity methods. The SNN processor achieves an accuracy of 93% on MNIST dataset. The implementation results show that the SNN processor achieves a throughput of 277.78k FPS with 0.50 mu J/inference energy consuming in inference mode, and a throughput of 211.77k FPS with 0.66 mu J/learning energy consuming in learning mode.

关键词： On-chip learning spiking neural network spiking-timing dependent plasticity temporal coding unsupervised learning

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Analysis of spatiotemporal patterns in a model of olfaction

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NEUROCOMPUTING 2004年 58卷 1027-1032页

作者： Kliper, O Horn, D Quenet, B Dror, G Tel Aviv Univ Sch Comp Sci IL-69978 Tel Aviv Israel Tel Aviv Univ Sch Phys & Astron IL-69978 Tel Aviv Israel Ecole Super Phys & Chim Ind Ville Paris Elect Lab F-75005 Paris France Acad Coll Tel Aviv Yaffo Dept Comp Sci IL-64044 Tel Aviv Israel

We model spatiotemporal patterns in locust olfaction with the dynamic neural filter, a recurrent network that produces spatiotemporal patterns in reaction to sets of constant inputs. We specify, within the model, inputs corresponding to different odors and different concentrations of the same odor. Then we proceed to analyze the resulting spatiotemporal patterns of the neurons of our model. Using SVD we investigate three kinds of data: global spatiotemporal data consisting of neuronal firing patterns over the period of odor presentation, spatial data, i.e. total spike counts during this period, and local spatiotemporal data which are neuronal spikes in single temporal bins. (C) 2003 Elsevier B.V. All rights reserved.

关键词： olfaction temporal coding dynamic neural filter recurrent network

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No parallel fiber volleys in the cerebellar cortex: Evidence from cross-correlation analysis between Purkinje cells in a computer model and in recordings from anesthetized rats

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2003年第3期14卷 311-327页

作者： Jaeger, D Emory Univ Dept Biol Atlanta GA 30322 USA

Purkinje cells aligned on the medio-lateral axis share a large proportion of their similar to175,000 parallel fiber inputs. This arrangement has led to the hypothesis that movement timing is coded in the cerebellum by beams of synchronously active parallel fibers. In computer simulations I show that such synchronous activation leads to a narrow spike cross-correlation between pairs of Purkinje cells. This peak was completely absent when shared parallel fiber input was active in an asynchronous mode. To determine the presence of synchronous parallel fiber beams in vivo I recorded from pairs of Purkinje cells in crus IIa of anesthetized rats. I found a complete absence of precise spike synchronization, even when both cells were strongly modulated in their spike rate by trains of air-puff stimuli to the face. These results indicate that Purkinje cell spiking is not controlled by volleys of synchronous parallel fiber inputs in the conditions examined. Instead, the data support a model by which granule cells primarily control Purkinje cell spiking via dynamic population rate changes.

关键词： cerebellum temporal coding modeling rat in vivo excitation inhibition

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SpikeNet: real-time visual processing with one spike per neuron

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NEUROCOMPUTING 2004年 58卷 857-864页

作者： Thorpe, SJ Guyonneau, R Guilbaud, N Allegraud, JM VanRullen, R Univ Toulouse 3 UMR 5549 Ctr Rech Cerveau & Cognit F-31062 Toulouse France SpikeNet Technol SARL F-31250 Revel France

SpikeNet is an image-processing system that uses very large-scale networks of asynchronously firing neurons. The latest version allows very efficient object identification in real-time using a video input, and although this specific implementation is designed to run on standard computer hardware, there are a number of clear implications for computational neuroscience. Specifically, SpikeNet demonstrates the plausibility of visual processing based on a single feed-forward pass and very sparse levels of firing. Above all, it is one of the very few models compatible with the severe temporal constraints imposed by experimental data on processing speed in the visual system. (C) 2004 Elsevier B.V. All rights reserved.

关键词： spiking neurons visual processing temporal coding shunting inhibition

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Self-organization through spike-timing dependent plasticity using localized synfire-chain patterns

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NEURAL PROCESSING LETTERS 2007年第1期25卷 79-89页

作者： Akimitsu, Toshio Okabe, Yoichi Hirose, Akira Univ Tokyo Grad Sch Engn Bunkyo Ku Tokyo Japan Univ Air Chiba 2618586 Japan

Many experimental results suggest that more precise spike timing is significant in neural information processing. From this point of view, we construct a self-organizing model using the spatio-temporal patterns, where Spike-Timing Dependent Plasticity (STDP) tunes the conduction delays between neurons. STDP forms smoother map with the spatially random and dispersed patterns, whereas it causes spatially distributed and clustered firings for spatially continuous and synchronous inputs. These results suggest that STDP forms cell assemblies having a fine structure reflecting external stimuli.

关键词： STDP synfire-chain temporal coding topological map

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A synfire chain in layered coincidence detectors with random synaptic delays

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NEURAL NETWORKS 2003年第1期16卷 39-46页

作者： Ikeda, K Kyoto Univ Dept Syst Sci Grad Sch Informat Kyoto 6068501 Japan

In this paper we analyze a synfire chain in a spiking neuron network. We also employ a coincidence-detector model showing the characteristics of temporal information processing more directly than the integrate-and-fire (I&F) model often discussed in the literature. There are two sources of randomness in a feed-forward network, however, only randomness in input spikes has attracted the attention of researchers and the randomness in synaptic delays has largely been ignored. Theoretical analyses of the synfire chain in I&F neurons without randomness in synaptic delays have shown that the dynamics of pulse packets can be viewed as a shift of the membrane potential distribution made by random noise input spikes. We introduce jittered synaptic delays instead of random noise inputs in a network of coincidence detectors and show that the network has almost the same dynamics as that of the I&F neurons. The distribution of the output spikes can be approximately described by an ordinary differential equation useful in understanding the dynamics of the pulse packets. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： temporal coding synchronous firing coincidence detector synaptic delay stable propagation

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Time-encoded multiplication-free spiking neural networks: application to data classification tasks

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NEURAL COMPUTING & APPLICATIONS 2023年第9期35卷 7017-7033页

作者： Stanojevic, Ana Cherubini, Giovanni Wozniak, Stanislaw Eleftheriou, Evangelos IBM Res Zurich Ruschlikon Switzerland Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland Axelera AI Zurich Switzerland

Spiking neural networks (SNNs) are mimicking computationally powerful biologically inspired models in which neurons communicate through sequences of spikes, regarded here as sparse binary sequences of zeros and ones. In neuroscience it is conjectured that time encoding, where the information is carried by the temporal position of spikes, is playing a crucial role at least in some parts of the brain where estimation of the spiking rate with a large latency cannot take place. Motivated by the efficiency of temporal coding, compared with the widely used rate coding, the goal of this paper is to develop and train an energy-efficient time-coded deep spiking neural network system. To ensure that the similarity among input stimuli is translated into a correlation of the spike sequences, we introduce correlative temporal encoding and extended correlative temporal encoding techniques to map analog input information into input spike patterns. Importantly, we propose an implementation where all multiplications in the system are replaced with at most a few additions. As a more efficient alternative to both rate-coded SNNs and artificial neural networks, such system represents a preferable solution for the implementation of neuromorphic hardware. We consider data classification tasks where input spike patterns are presented to a feed-forward architecture with leaky-integrate-and-fire neurons. The SNN is trained by backpropagation through time with the objective to match sequences of output spikes with those of specifically designed target spike patterns, each corresponding to exactly one class. During inference the target spike pattern with the smallest van Rossum distance from the output spike pattern determines the class. Extensive simulations indicate that the proposed system achieves a classification accuracy at par with that of state-of-the-art machine learning models.

关键词： Spiking neural network temporal coding Low-complexity data classification Multiplication-free inference Backpropagation through time

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Spiking Neural Networks With Time-to-First-Spike coding Using TFT-Type Synaptic Device Model

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IEEE ACCESS 2021年 9卷 78098-78107页

作者： Oh, Seongbin Lee, Soochang Woo, Sung Yun Kwon, Dongseok Im, Jiseong Hwang, Joon Bae, Jong-Ho Park, Byung-Gook Lee, Jong-Ho Seoul Natl Univ Dept ECE Seoul 08826 South Korea Seoul Natl Univ ISRC Seoul 08826 South Korea Kookmin Univ Sch Elect Engn Seoul 02707 South Korea

In hardware-based spiking neural networks (SNNs), the conversion of analog input data into the arrival time of an input pulse is regarded as a good candidate for the encoding method due to its bioplausibility and power-efficiency. In this work, we trained an SNN encoded by time to first spike (TTFS) and performed an inference process using the behavior of the fabricated TFT-type flash synaptic device. The exponentially decaying synaptic current model required in the inference process was implemented by reading devices in the subthreshold region using triangle pulses. In a high-level system simulation, the TTFS-SNN (two-layer MLP with 512 hidden neurons) reached a high accuracy of 97.94%. Compared to conventional rate-encoded SNNs, TTFS-SNN made 2.9 times faster judgment and consumed similar to 10 times less energy in the inference process. Additionally, to use the network in a more stable condition, we propose a method to operate it using a rectangle pulse in the saturation region of the synaptic device. The distortion caused by this approximation was minimized by shortening the pulse width. As a result, the modified inference system showed an accuracy of 97.36%, and the prediction time and energy consumption were reduced 3.97- and 83.04-times when compared to those of the rate-SNN. Finally, we analyzed the sensitivity of the network performance due to unexpected issues that may occur in the hardware system and thus explained the competitiveness of the proposed synaptic behavior in the saturation region.

关键词： Time-to-first-spike (TTFS) coding temporal coding flash-based synaptic device hardware-based spiking neural networks neuromorphic systems

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Pattern grouping algorithm and de-convolution filtering of non-stationary correlated Poisson processes

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NEUROCOMPUTING 2001年 38卷 1709-1714页

作者： Tetko, IV Villa, AEP Univ Lausanne Inst Physiol Lab Neuroheurist CH-1005 Lausanne Switzerland Ukrainian Acad Sci IBPC Bogomoletz Inst Physiol Dept Physiol Brain Kiev Ukraine Ukrainian Acad Sci IBPC Dept Biomed Applicat Kiev Ukraine

The existence of precise temporal relations in sequences of spike intervals, referred to as "spatiotemporal patterns", is suggested by brain theories that emphasize the role of temporal coding. A pattern grouping algorithm was designed to identify and to evaluate the statistical significance of such patterns, particularly for data generated according to stationary Poisson processes. The experimental time series, however, can be characterized by considerable deviations from independent stationary Poisson processes. This article describes a filtering method that de-convolute time series according to their correlation functions and makes possible an application of the pattern grouping algorithm for such data too. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： spatiotemporal firing pattern burst filtering temporal coding correlation functions

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