Jul 2026· International Journal of Uncertainty Fuzziness and Knowledge-Based Systems· 0 citations· 61 references
TL;DR
It is aimed at proving that SNNs have potential in such areas as computer vision, robotics, and speech recognition, and their role in overcoming the barrier between artificial and biological neural systems is proved.
Abstract
The third generation of neural networks is called Spiking Neural Networks (SNNs) and presents an energy-efficient and physiologically realistic alternative to the traditional Artificial Neural Networks (ANNs). This review discusses the latest advances in SNN models, learning algorithms, and their implementation on neuromorphic hardware. Besides reinforcement learning techniques such as REINFORCE, supervised techniques, such as SpikeProp and unsupervised Spike-Timing-Dependent Plasticity (STDP), are considered in the context of reward-based learning. The performance of the neurological systems technologies, including the IBM TrueNorth, the FPGA-based, and the Intel Loihi, is checked. Despite significant improvement in parallel processing and energy usage in these systems, there are still problems with accuracy of the synapses, training complexity, and scalability. FPGA-based systems are promising in real-time processing, though they have programmability issues that limit their deployment. The article outlines the key limitation of SNN models, namely their inability to handle large datasets and difficult learning tasks. To advance the performance of neuromorphic systems, research is underway on hybrid SNN-ANN models and memristor-based synaptic storage. This review is aimed at proving that SNNs have potential in such areas as computer vision, robotics, and speech recognition, and their role in overcoming the barrier between artificial and biological neural systems.
Reinforcement learning (RL) algorithms have made strides over the past decade applying them to a wide range of problems and control tasks. However, the deployment of RL on neuromorphic hardware for continuous control tasks remains under-validated. Namely it is unclear whether replacing a conventional actor network with a spiking neural network (SNN) affects the performance of an agent before any hardware-specific benefits manifest. We provide a systematic validation of a minimal, neuromorphically viable spiking actor variant of Soft Actor-Critic (SAC) on conventional hardware, establishing a baseline for future neuromorphic RL research. In this paper, we propose the Spiking Actor Network Soft Actor Critic (SANSAC) to address the use of RL frameworks in continuous environments, designed as a framework that can be implemented on neuromorphic hardware. We compare a traditional Soft Actor Critic (SAC) network to SANSAC in a traditional computer. We demonstrate the near equivalent performance of SANSAC and SAC, while addressing the impact of hidden dimensions. Our results demonstrate the viability of SNN based algorithms in complex continuous environments, as well as competitive performance to traditional neural networks in traditional computers, providing a basis to continue exploring the use of SNNs in continuous RL frameworks.
Jessica Hunter, M. Shuvo, Krishna Roy· 0 citations
This work proposes a noisy group neuron (NGN) model, which incorporates population-level synchronous resetting and neural stochasticity as fundamental computational mechanisms, and develops the NGN method as a framework that combines the NGN model with backpropagation learning based on mean-field dynamics.
Yajie Zhai, Yanmei Kang, Meng Li et al.· 0 citations
Neuromorphic computing is a paradigm based on the computational mechanisms of the human brain and hasreceived considerable attention as a real-time technique with low energy requirements. Present systems, however, arelimited in their ability to scale traditional processors to a neuromorphic architecture, leading to issues with latency,power consumption, and smooth data flow. To address these problems, this paper proposes the ACORISC-VbSNNframework, comprising a modular RISC-V architecture, Spiking Neural Networks (SNNs), and Ant ColonyOptimization (ACO). The system uses a shared-memory architecture to maximize communication between traditionaland neuromorphic processors, ensuring data is managed effectively. The postulated framework processes the sensorydata by pre-processing and encoding them using rate coding, and dynamically optimizing memory access. SNNs arealso used to process spike trains in real-time, whereas ACO is used to determine the best data paths to minimizebottlenecks. Experimental analysis shows that the system performs better, with ultra-low power consumption of 0.0095mW, very low latency of 0.000544 seconds, and 99.2 percent accuracy. These findings indicate that the ACORISCVbSNN model has the potential to advance the field of bio-inspired computing, providing a highly accurate, energyefficient, and low-latency system for real-world use.
Yamini Devi Ykuntam, M. V. Nageswara Rao, Leela Kumari. B.· International Journal of Com...· 0 citations
Understanding the neural mechanisms underlying general anesthesia remains a significant challenge in neuroscience and clinical practice. Traditional software-based simulations of large-scale brain networks are often constrained by high computational costs and fail to achieve real-time performance.
In this paper, we propose a high-performance hardware implementation of large-scale neuromorphic system to investigate anesthetic-induced neural dynamics. The system successfully models a cortical network comprising 10,000 spiking neurons (8,000 excitatory and 2,000 inhibitory) utilizing the biologically plausible Izhikevich neuron model. Deployed on a field-programmable gate array (FPGA), the proposed architecture exploits high parallelism to achieve real-time simulation speeds. By adjusting synaptic weights and network parameters to mimic the pharmacological eects of anesthetic agents, our system can continuously monitor and evaluate state transitions in neural synchronization and firing patterns.
The results demonstrate that the hardware-accelerated neuromorphic approach provides an efficient, scalable, and real-time platform for investigating large-scale neural dynamics.
Pending future validation against empirical clinical EEG data, this foundational framework paves the way for advanced brain–machine interfaces and closed-loop anesthetic delivery systems.
Chuan-Guang Wang, Xiaotian Pan, Si Chen et al.· Frontiers in Systems Neurosc...· 0 citations
The second-generation neural networks have evolved in recent years, which have become more complex architectures such as spiking neural networks and quantum neural networks. However, the computational resource restriction of neural networks on edge devices is still challenging. The thesis investigates stable learning and compute-resource efficiency on spiking neural networks and hybrid classical-quantum neural networks. Other common qualities like high performance (e.g. high accuracy, high reward), robustness, convergence, predictability, and fast running times were also considered in one or more studies. The contributions of the thesis have several folds. The first study was using audio data; one reason was to verify if a trend called temporal information concentration is present in the spiking neural network. I also gathered other findings, such as dataset complexity impacting Fisher information, related to temporal information dynamics. A second study on spiking neural networks revealed that temporal information concentration was not present in quantization aware training variants, but an increase in Fisher information was found in those variants. The third study on my Multimodal Simplified Spiking Neural Networks explored the effects of audio and image noise. The results show the multimodal model outperformed its unimodal counterparts, but certain configurations of image noises, audio noises, and noise levels performed better than others. In one of the hybrid classical-quantum neural network studies with reinforcement learning, in my quantization-aware training variant, I found higher initial reward growth, longer decreasing in standard deviation and policy entropy, and a few correlations as well related to average reward and policy entropy. In the second study on hybrid classical-quantum neural networks, structured pruning is found to sharpen decisiveness and reveal bad pruning paths, while overparameterization can help exploration. All these studies try to address maintaining or improving stable learning and if the models are computation-resource efficient enough to be realistic.
Phenomenological spiking neuron models such as Izhikevich, adaptive quadratic integrate-and-fire (aQIF), and Adaptive Exponential (AdEx) are widely used because of their simplicity and numerical efficiency. These models reproduce diverse neuronal dynamics through a slow self-inhibitory adaptation variable. Here we introduce their symmetric counterpart by replacing adaptation with slow self-excitation, motivated by intrinsic calcium-mediated membrane currents. This minimal modification enables robust persistent spiking and working-memory dynamics without compromising computational efficiency. These properties remain in excitatory spiking neural networks. We then derive and validate a mean-field neural mass model that remains stable while retaining working-memory functionality. Additionally, we implement the single-neuron model in a minimal memristor-based neuromorphic circuit and experimentally confirm its dynamics. These results provide scalable tools for large-scale brain simulations and neuromorphic applications in robotics, brain-machine interfaces, and edge AI devices.
D. Depannemaecker, Adrien D’hollande, G. Casagrande et al.· Nature Communications· 0 citations