Application of Locally Connected Spiking Neural Network in Image Processing

This paper delves into the realm of Feedforward Neural Networks (FFN) and explores their application in image processing.The research begins with an introduction to FFNs, elucidating their fundamental concepts and underpinning principles.The FFN's architecture, comprising input, hidden, and output layers, along with interconnected nodes and weighted connections, is detailed to provide a comprehensive understanding of its structural framework.Moreover, the paper investigates the usage of FFNs in image processing, highlighting their role in various tasks such as image classification, object detection, image recognition, and image generation.Through a critical analysis, this research explores the advantages of employing FFNs in image processing, emphasizing their capability to learn complex patterns, adaptability to large datasets, and ability to generalize well to new data.Conversely, the paper also examines the limitations and disadvantages associated with FFNs in image processing, including issues related to overfitting, computational complexity, and dependency on extensive data for effective training.Furthermore, the study focuses on the implementation of FFNs in image processing applications.It discusses specific methodologies and techniques utilized to integrate FFNs into image-based tasks, showcasing their effectiveness in enhancing image quality, extracting features, and performing intricate image-based computations.Additionally, this paper presents case studies and realworld examples where FFNs have been successfully deployed in image processing applications.These instances illustrate the practical implications and the significance of FFNs in domains such as medical imaging, satellite image analysis, facial recognition, and autonomous vehicles.In conclusion, the research sheds light on the potential and challenges of using Feedforward Neural Networks for image processing tasks.By comprehensively analyzing their architecture, usage, advantages, and drawbacks, this paper aims to contribute to the deeper understanding and informed utilization of FFNs in the field of image processing.

In today’s world, the need for computers and their logics is critical for the development of any system. This paper is for those people who have little or no knowledge about Artificial Neural Networks (ANNs). Various advances have been made in creating intelligent systems, some enlivened by biological neural networks. Analysts from numerous logical orders are outlining artificial neural networks (ANNs) to take care of a variety of problems in design recognition, prediction, optimization, associative memory, and control. And will be discussing some of the algorithms of ANNs such as image segmentation using edge detection, image enhancement using multiscale retinex and technique combining sharpening and noise reduction and some practical applications such as diagnosing liver disease and face detection systems.

Building accurate and efficient deep neural network (DNN) models for intelligent sensing systems to process data locally is essential. Spiking neural networks (SNNs) have gained significant popularity in recent years because they are more biological-plausible and energy-efficient than DNNs. However, SNNs usually have lower accuracy than DNNs. In this paper, we propose to use SNNs for image sensing applications. Moreover, we introduce the DNN-SNN knowledge distillation algorithm to reduce the accuracy gap between DNNs and SNNs. Our DNNSNN knowledge distillation improves the accuracy of an SNN by transferring knowledge between a DNN and an SNN. To better transfer the knowledge, our algorithm creates two learning paths from a DNN to an SNN. One path is between the output layer and another path is between the intermediate layer. DNNs use real numbers to propagate information between neurons while SNNs use 1-bit spikes. To empower the communication between DNNs and SNNs, we utilize a decoder to decode spikes into real numbers. Also, our algorithm creates a learning path from an SNN to a DNN. This learning path better adapts the DNN to the SNN by allowing the DNN to learn the knowledge from the SNN. Our SNN models are deployed on Loihi, which is a specialized chip for SNN models. On the MNIST dataset, our SNN models trained by the DNN-SNN knowledge distillation achieve better accuracy than the SNN models on GPU trained by other training algorithms with much lower energy consumption per image.

Developmental plasticity plays a prominent role in shaping the brain's structure during ongoing learning in response to dynamically changing environments. However, the existing network compression methods for deep artificial neural networks (ANNs) and spiking neural networks (SNNs) draw little inspiration from brain's developmental plasticity mechanisms, thus limiting their ability to learn efficiently, rapidly, and accurately. This paper proposed a developmental plasticity-inspired adaptive pruning (DPAP) method, with inspiration from the adaptive developmental pruning of dendritic spines, synapses, and neurons according to the "use it or lose it, gradually decay" principle. The proposed DPAP model considers multiple biologically realistic mechanisms (such as dendritic spine dynamic plasticity, activity-dependent neural spiking trace, and local synaptic plasticity), with additional adaptive pruning strategy, so that the network structure can be dynamically optimized during learning without any pre-training and retraining. Extensive comparative experiments show consistent and remarkable performance and speed boost with the extremely compressed networks on a diverse set of benchmark tasks for deep ANNs and SNNs, especially the spatio-temporal joint pruning of SNNs in neuromorphic datasets. This work explores how developmental plasticity enables complex deep networks to gradually evolve into brain-like efficient and compact structures, eventually achieving state-of-the-art (SOTA) performance for biologically realistic SNNs.

Spiking neural networks (SNNs), inspired by the neuronal network in the brain, provide biologically relevant and low-power consuming models for information processing. Existing studies either mimic the learning mechanism of brain neural networks as closely as possible, for example, the temporally local learning rule of spike-timing-dependent plasticity (STDP), or apply the gradient descent rule to optimize a multilayer SNN with fixed structure. However, the learning rule used in the former is local and how the real brain might do the global-scale credit assignment is still not clear, which means that those shallow SNNs are robust but deep SNNs are difficult to be trained globally and could not work so well. For the latter, the nondifferentiable problem caused by the discrete spike trains leads to inaccuracy in gradient computing and difficulties in effective deep SNNs. Hence, a hybrid solution is interesting to combine shallow SNNs with an appropriate machine learning (ML) technique not requiring the gradient computing, which is able to provide both energy-saving and high-performance advantages. In this article, we propose a HybridSNN, a deep and strong SNN composed of multiple simple SNNs, in which data-driven greedy optimization is used to build powerful classifiers, avoiding the derivative problem in gradient descent. During the training process, the output features (spikes) of selected weak classifiers are fed back to the pool for the subsequent weak SNN training and selection. This guarantees HybridSNN not only represents the linear combination of simple SNNs, as what regular AdaBoost algorithm generates, but also contains neuron connection information, thus closely resembling the neural networks of a brain. HybridSNN has the benefits of both low power consumption in weak units and overall data-driven optimizing strength. The network structure in HybridSNN is learned from training samples, which is more flexible and effective compared with existing fixed multilayer SNNs. Moreover, the topological tree of HybridSNN resembles the neural system in the brain, where pyramidal neurons receive thousands of synaptic input signals through their dendrites. Experimental results show that the proposed HybridSNN is highly competitive among the state-of-the-art SNNs.

Spiking neural networks (SNNs) have attracted many researchers’ interests due to its biological plausibility and event-driven characteristic. In particular, recently, many studies on high-performance SNNs comparable to the conventional analog-valued neural networks (ANNs) have been reported by converting weights trained from ANNs into SNNs. However, unlike ANNs, SNNs have an inherent latency that is required to reach the best performance because of differences in operations of neuron. In SNNs, not only spatial integration but also temporal integration exists, and the information is encoded by spike trains rather than values in ANNs. Therefore, it takes time to achieve a steady-state of the performance in SNNs. The latency is worse in deep networks and required to be reduced for the practical applications. In this work, we propose a pre-charged membrane potential (PCMP) for the latency reduction in SNN. A variety of neural network applications (e.g., classification, autoencoder using MNIST and CIFAR-10 datasets) are trained and converted to SNNs to demonstrate the effect of the proposed approach. The latency of SNNs is successfully reduced without accuracy loss. In addition, we propose a delayed evaluation method (DE), by which the errors during the initial transient are discarded. The error spikes occurring in the initial transient is removed by DE, resulting in the further latency reduction. DE can be used in combination with PCMP for further latency reduction. Finally, we also show the advantages of the proposed methods in improving the number of spikes required to reach a steady-state of the performance in SNNs for energy-efficient computing.

Rethinking the performance comparison between SNNS and ANNS

Efficient fusion of spiking neural networks and FET-type gas sensors for a fast and reliable artificial olfactory system

Brain-inspired spiking neural networks (SNNs) are becoming a promising energy-efficient alternative to traditional artificial neural networks (ANNs). However, the performance gap between SNNs and ANNs has been a significant hindrance to deploying SNNs ubiquitously. To leverage the full potential of SNNs, in this paper we study the attention mechanisms, which can help human focus on important information. We present our idea of attention in SNNs with a multi-dimensional attention module, which infers attention weights along the temporal, channel, as well as spatial dimension separately or simultaneously. Based on the existing neuroscience theories, we exploit the attention weights to optimize membrane potentials, which in turn regulate the spiking response. Extensive experimental results on event-based action recognition and image classification datasets demonstrate that attention facilitates vanilla SNNs to achieve sparser spiking firing, better performance, and energy efficiency concurrently. In particular, we achieve top-1 accuracy of 75.92% and 77.08% on ImageNet-1 K with single/4-step Res-SNN-104, which are state-of-the-art results in SNNs. Compared with counterpart Res-ANN-104, the performance gap becomes -0.95/+0.21 percent and the energy efficiency is 31.8×/7.4×. To analyze the effectiveness of attention SNNs, we theoretically prove that the spiking degradation or the gradient vanishing, which usually holds in general SNNs, can be resolved by introducing the block dynamical isometry theory. We also analyze the efficiency of attention SNNs based on our proposed spiking response visualization method. Our work lights up SNN's potential as a general backbone to support various applications in the field of SNN research, with a great balance between effectiveness and energy efficiency.

Spiking Neural Networks (SNNs), widely known as the third generation of neural networks, encode input information temporally using sparse spiking events, which can be harnessed to achieve higher computational efficiency for cognitive tasks. However, considering the rapid strides in accuracy enabled by state-of-the-art Analog Neural Networks (ANNs), SNN training algorithms are much less mature, leading to accuracy gap between SNNs and ANNs. In this paper, we propose different SNN training methodologies, varying in degrees of biofidelity, and evaluate their efficacy on complex image recognition datasets. First, we present biologically plausible Spike Timing Dependent Plasticity (STDP) based deterministic and stochastic algorithms for unsupervised representation learning in SNNs. Our analysis on the CIFAR-10 dataset indicates that STDP-based learning rules enable the convolutional layers to self-learn low-level input features using fewer training examples. However, STDP-based learning is limited in applicability to shallow SNNs (≤4 layers) while yielding considerably lower than state-of-the-art accuracy. In order to scale the SNNs deeper and improve the accuracy further, we propose conversion methodology to map off-the-shelf trained ANN to SNN for energy-efficient inference. We demonstrate 69.96% accuracy for VGG16-SNN on ImageNet. However, ANN-to-SNN conversion leads to high inference latency for achieving the best accuracy. In order to minimize the inference latency, we propose spike-based error backpropagation algorithm using differentiable approximation for the spiking neuron. Our preliminary experiments on CIFAR-10 show that spike-based error backpropagation effectively captures temporal statistics to reduce the inference latency by up to 8× compared to converted SNNs while yielding comparable accuracy

Deep neural networks such as convolutional neural networks (CNNs) have achieved a great success in a broad range of fields. Spiking neural networks (SNNs) are designed as a solution for realizing ultra-low power consumption using spikebased neuromorphic hardware. To enable the mapping between conventional artificial neural networks (ANNs) and spike-based neuromorphic hardware, direct conversion from conventional ANNs into SNNs has recently been proposed. However, performance loss is hard to avoid after conversion. To reduce the loss, we analyze the encoding methods of SNNs and the optimization methods for converting. We use the rate coding and approximate the output of an activation function of CNNs by the number of spikes produced by a neuron within a given time window in SNNs. We propose a method for generating fixed uniform spike train whose number of spikes is as exact as we expect, and present an optimization method to reduce the loss by rescaling the threshold of each layer in SNNs. For evaluation, we train three different CNNs with MNIST and SVHN datasets separately and convert all of them into SNNs. The networks achieve a max accuracy of 99.17% on MNIST and of 93.36% on additional training dataset of SVHN. The results show the proposed fixed and uniform spike train not only outperforms the Poisson distributed spike train but also costs less time to generate. Our threshold rescaling method greatly improves the performance of SNNs.

Spiking Neural Networks (SNN) is one special implementation of Artificial Neural Networks (ANN), where the input signals are encoded in the temporal relationship between consecutive spikes (spike trains) instead of real-numbered values. Nevertheless, SNN is believed to be a closer representation of the biological neural system, because it imitates the current spikes that are transmitted between neurons in real biological systems. Practical and simplified SNN models include the Leaky Integrate-and-Fire (LIF) function of the neurons and the Spiking Timing Dependent Plasticity (STDP) function of the synapses. While most ANN architectures can be implemented with digital logic gates, SNN is more suitable to be implemented in analog circuits. In this paper, we propose revised analog circuit implementations of SNN neurons with LIF and STDP functions. Compared with previous works by other researchers, our proposed analog designs use fewer components and can be cascaded to form a complete neural system. The circuits are designed and simulated with SMIC 55nm CMOS LP process. The simulated results demonstrate that the analog neural system can work under a very small current (less than 10 µA) and voltage supply (1.0 Volts), and consumes less power consumption than digital implementations.

Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed analysis of ECG recordings to pinpoint deviations that are indicative of heart anomalies. This traditional method, while effective, demands significant expertise and is susceptible to inaccuracies due to its manual nature. In the realm of computational analysis, Artificial Neural Networks (ANNs) have gained prominence across various domains, which can be attributed to their superior analytical capabilities. Conversely, Spiking Neural Networks (SNNs), which mimic the neural activity of the brain more closely through impulse-based processing, have not seen widespread adoption. The challenge lies primarily in the complexity of their training methodologies. Despite this, SNNs offer a promising avenue for energy-efficient computational models capable of displaying a high-level performance. This paper introduces an innovative approach employing SNNs augmented with an attention mechanism to enhance feature recognition in ECG signals. By leveraging the inherent efficiency of SNNs, coupled with the precision of attention modules, this model aims to refine the analysis of cardiac signals. The novel aspect of our methodology involves adapting the learned parameters from ANNs to SNNs using leaky integrate-and-fire (LIF) neurons. This transfer learning strategy not only capitalizes on the strengths of both neural network models but also addresses the training challenges associated with SNNs. The proposed method is evaluated through extensive experiments on two publicly available benchmark ECG datasets. The results show that our model achieves an overall accuracy of 93.8% on the MIT-BIH Arrhythmia dataset and 85.8% on the 2017 PhysioNet Challenge dataset. This advancement underscores the potential of SNNs in the field of medical diagnostics, offering a path towards more accurate, efficient, and less resource-intensive analyses of heart diseases.

Neuromorphic computing is a promising technology for future machine learning research, which is envisioned to provide lower power consumption, fault tolerance, and massive parallelism. However, the neuromorphic network, i.e., spike neural network (SNN), suffers from a broader range of its application compared with artificial neural networks (ANN). In this work, we propose a stochastic computation SC-based hybrid ANN and SNN scheme, which supports both ANN and SNN computation in SC domain. The ANN computation is converted to SC, which is implemented with simple logic gates. Moreover, we propose a new two-stage stochastic computation that is compatible with the sequence computation style of SNN. Consequently, the logic resource for the hybrid SNN and ANN can be shared. Compared to the state-of-the-art hybrid scheme, the proposed SC-based ANN and SNN exhibit much lower hardware costs and higher hardware efficiency.

This paper describes that under certain operations, the parallel pulse transmission behavior of unit-linking PCNN (pulse coupled neural network) for image processing, which we have developed from PCNN based on biological experimental results, is equal to the open operation of mathematic morphology. The open operation of unit-linking PCNN based on its parallel pulse transmission behavior can be efficiently used for image processing, such as reducing the pulse noise of binary images. A new pulse-noise-reducing approach based on the open operation of unit-linking PCNN is proposed in this paper. The results of computer simulations show that the visual effects of retrieval images of binary pulse-noise images based on the open operation of unit-linking PCNN are better than those based on the median filter, a tradition approach for reducing image pulse noise. For binary images polluted by any other kinds of noise, such as white Guassian noise, after having turned the original noisy images into binary noisy images, using the approach proposed in this paper also can reduce the noise. This paper bridges the gap between image processing based on mathematical morphology and image processing based on unit-linking PCNN.