SpiFoG: an efficient supervised learning algorithm for the network of spiking neurons

Hussain, Irshed; Thounaojam, Dalton Meitei

doi:10.1038/s41598-020-70136-5

Cited by 10 publications

(5 citation statements)

References 40 publications

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“…The authors in 38 present a supervised method of SNN training, which they refer to as SpiFoG. The SpiFoG uses evolutionary optimization to train synapses by introducing random synaptic delays.…”

Section: Related Work and Problem Definitionmentioning

confidence: 99%

A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm

Siddique

Vai

Pun

2023

Sci Rep

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Spiking neural networks (SNNs) are more energy- and resource-efficient than artificial neural networks (ANNs). However, supervised SNN learning is a challenging task due to non-differentiability of spikes and computation of complex terms. Moreover, the design of SNN learning engines is not an easy task due to limited hardware resources and tight energy constraints. In this article, a novel hardware-efficient SNN back-propagation scheme that offers fast convergence is proposed. The learning scheme does not require any complex operation such as error normalization and weight-threshold balancing, and can achieve an accuracy of around 97.5% on MNIST dataset using only 158,800 synapses. The multiplier-less inference engine trained using the proposed hard sigmoid SNN training (HaSiST) scheme can operate at a frequency of 135 MHz and consumes only 1.03 slice registers per synapse, 2.8 slice look-up tables, and can infer about 0.03$$\times {\varvec{10}}^{\varvec{9}}$$ × 10 9 features in a second, equivalent to 9.44 giga synaptic operations per second (GSOPS). The article also presents a high-speed, cost-efficient SNN training engine that consumes only 2.63 slice registers per synapse, 37.84 slice look-up tables per synapse, and can operate at a maximum computational frequency of around 50 MHz on a Virtex 6 FPGA.

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Section: Related Work and Problem Definitionmentioning

confidence: 99%

A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm

Siddique

Vai

Pun

2023

Sci Rep

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“…The complicated dynamics of the biological spiking neuron has posed great difficulty in designing efficient SNNs capable of solving complex information processing problems 72 . Early models has relied on Spike Time Dependent Plasticity (STDP) that depends on pre-synaptic and post-synaptic information 73 , 74 , which results in non-differentiable models 73 , 74 . Surrogate gradients and multiple learning schemes has been proposed to tackle this challenge.…”

Section: Brain-inspired Computing Modelsmentioning

confidence: 99%

Memory-inspired spiking hyperdimensional network for robust online learning

Zou

Alimohamadi

Zakeri

et al. 2022

Sci Rep

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Recently, brain-inspired computing models have shown great potential to outperform today’s deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning. Despite the success, these two brain-inspired models have different strengths. While SNN mimics the physical properties of the human brain, HDC models the brain on a more abstract and functional level. Their design philosophies demonstrate complementary patterns that motivate their combination. With the help of the classical psychological model on memory, we propose SpikeHD, the first framework that fundamentally combines Spiking neural network and hyperdimensional computing. SpikeHD generates a scalable and strong cognitive learning system that better mimics brain functionality. SpikeHD exploits spiking neural networks to extract low-level features by preserving the spatial and temporal correlation of raw event-based spike data. Then, it utilizes HDC to operate over SNN output by mapping the signal into high-dimensional space, learning the abstract information, and classifying the data. Our extensive evaluation on a set of benchmark classification problems shows that SpikeHD provides the following benefit compared to SNN architecture: (1) significantly enhance learning capability by exploiting two-stage information processing, (2) enables substantial robustness to noise and failure, and (3) reduces the network size and required parameters to learn complex information.

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“…In addition, research on computational neural networks showed that, for classification problems, SNNs use fewer neurons than the second generation of artificial neural networks (ANNs) does [ 22 ]. In addition, the hardware implementation of SNNs demonstrated their efficacy in modelling conditional reflex formation [ 23 ] or in controlling the contraction of artificial muscles composed of shape memory alloy (SMA).…”

Section: Introductionmentioning

confidence: 99%

The Influence of the Number of Spiking Neurons on Synaptic Plasticity

2023

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The main advantages of spiking neural networks are the high biological plausibility and their fast response due to spiking behaviour. The response time decreases significantly in the hardware implementation of SNN because the neurons operate in parallel. Compared with the traditional computational neural network, the SNN use a lower number of neurons, which also reduces their cost. Another critical characteristic of SNN is their ability to learn by event association that is determined mainly by postsynaptic mechanisms such as long-term potentiation. However, in some conditions, presynaptic plasticity determined by post-tetanic potentiation occurs due to the fast activation of presynaptic neurons. This violates the Hebbian learning rules that are specific to postsynaptic plasticity. Hebbian learning improves the SNN ability to discriminate the neural paths trained by the temporal association of events, which is the key element of learning in the brain. This paper quantifies the efficiency of Hebbian learning as the ratio between the LTP and PTP effects on the synaptic weights. On the basis of this new idea, this work evaluates for the first time the influence of the number of neurons on the PTP/LTP ratio and consequently on the Hebbian learning efficiency. The evaluation was performed by simulating a neuron model that was successfully tested in control applications. The results show that the firing rate of postsynaptic neurons post depends on the number of presynaptic neurons pre, which increases the effect of LTP on the synaptic potentiation. When post activates at a requested rate, the learning efficiency varies in the opposite direction with the number of pres, reaching its maximum when fewer than two pres are used. In addition, Hebbian learning is more efficient at lower presynaptic firing rates that are divisors of the target frequency of post. This study concluded that, when the electronic neurons additionally model presynaptic plasticity to LTP, the efficiency of Hebbian learning is higher when fewer neurons are used. This result strengthens the observations of our previous research where the SNN with a reduced number of neurons could successfully learn to control the motion of robotic fingers.

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SpiFoG: an efficient supervised learning algorithm for the network of spiking neurons

Cited by 10 publications

References 40 publications

A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm

A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm

Memory-inspired spiking hyperdimensional network for robust online learning

The Influence of the Number of Spiking Neurons on Synaptic Plasticity

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