Contemporary implementations of spiking bio-inspired neural networks
arXiv:2412.17926 · doi:10.64214/jmsn.01.01005
Abstract
The extensive development of the field of spiking neural networks has led to many areas of research that have a direct impact on people's lives. As the most bio-similar of all neural networks, spiking neural networks not only allow the solution of recognition and clustering problems (including dynamics), but also contribute to the growing knowledge of the human nervous system. Our analysis has shown that the hardware implementation is of great importance, since the specifics of the physical processes in the network cells affect their ability to simulate the neural activity of living neural tissue, the efficiency of certain stages of information processing, storage and transmission. This survey reviews existing hardware neuromorphic implementations of bio-inspired spiking networks in the "semiconductor", "superconductor" and "optical" domains. Special attention is given to the possibility of effective "hybrids" of different approaches
arXiv comment30 pages, 24 figures
References in corpus (25)
- Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors
- Parallel convolution processing using an integrated photonic tensor core
- Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
- Ultra-Low-Power Superconductor Logic
- A silicon photonic modulator neuron
- Coherent quantum phase slip
- Filamentary Switching: Synaptic Plasticity through Device Volatility
- Beyond Moore's technologies: operation principles of a superconductor alternative
- Toward Fast Neural Computing using All-Photonic Phase Change Spiking Neurons
- Dynamical laser spike processing
- Josephson junction simulation of neurons
- A Photonic In-Memory Computing primitive for Spiking Neural Networks using Phase-Change Materials
- Solitary and Coupled Semiconductor Ring Lasers as Optical Spiking Neurons
- AC-Biased Shift Registers as Fabrication Process Benchmark Circuits and Flux Trapping Diagnostic Tool
- Superconductor Electronics Fabrication Process with MoN Kinetic Inductors and Self-Shunted Josephson Junctions
- A superconducting nanowire spiking element for neural networks
- Circuit designs for superconducting optoelectronic loop neurons
- Inductance and mutual inductance of superconductor integrated circuit features with sizes down to 120 nm. Part I
- How to enhance the dynamic range of excitatory-inhibitory excitable networks
- Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications
- Fan-out and Fan-in properties of superconducting neuromorphic circuits
- Dynamical attractors of memristors and their networks
- Frequency pulling and mixing of relaxation oscillations in superconducting nanowires
- BioSFQ circuit family for neuromorphic computing: Bridging digital and analog domains of superconductor technologies
- An On-Chip Trainable Neuron Circuit for SFQ-Based Spiking Neural Networks