Networks of Spiking Neurons: The Third Generation of Neural Network Models

By Wolfgang Maass. 1996

Subfields: Model Architectures, Neuroscience

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Citations in the corpus

Dynamic Neural Networks: A Survey (2021, background): “Spiking neural networks (SNNs) [42], [43] also perform data-dependent inference by propagating pulse signals.”
Deep learning in neural networks: An overview (2014, neutral): “…Legenstein, & Maass, 2014; Izhikevich et al., 2003; Kasabov, 2014; Kempter, Gerstner, & Van Hemmen, 1999; Kistler, Gerstner, & van Hemmen, 1997; Maass, 1996, 1997; Maex & Orban, 1996; Nessler, Pfeiffer, Buesing, & Maass, 2013; Rezende & Gerstner, 2014; Seung, 2003; Song, Miller, & Abbott,…”
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network (2020, background): “These emerging class of networks with increased bio-ﬁdelity are known as Spiking Neural Networks (SNNs)[22].”
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection (2019, methodology): “Spiking neural networks (SNNs), which are the third-generation neural networks, were introduced to mimic how information is encoded and processed in the human brain by employing spiking neurons as computation units (Maass 1997).”
Spikformer: When Spiking Neural Network Meets Transformer (2022, background): “As the third generation of neural network (Maass, 1997), the Spiking Neural Network (SNN) is very promising for its low power consumption, event-driven characteristic, and biological plausibility (Roy et al., 2019).”
Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators (2016, background): “Spiking Neural Networks (SNNs) [57, 58, 59, 60] are popular in ASIC designs, largely due to their compact mapping onto fully-parallel hardware.”
VLP: A Survey on Vision-language Pre-training (2022, background): “work of non-Euclidean space manifold and how to put some geometric priors into the model[152, 153], which are relatively new research directions.”
Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks (2023, background): “Such conditions signify deficient or absent metaplastic control, as observed with traditional two-variable nonstochastic Hebbian-type rules [54,56,93], that plague natural and artificial macroscale networks with excessive synaptic densities, insufficient information-storage capacities, and numerous ”
Structure-Adaptable Digital Neural Networks (1999, unknown): “Spiking neurons are considered the third generation of neural models after the McCulloch-Pitts neurons and the neurons with weighted sum of inputs and continuous activation functions [125].”
An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems (2014, methodology): “The main limitation of the first generation ANNs is that they can only output digital results and therefore can process only Boolean functions.(12,48,68) The computational units in the second generation ANNs use an activation function with a continuous set of possible output values to a weighted sum”
Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks (2021, background): “In analogy to biological neurons, spiking neural networks (SNNs) were introduced decades ago [44,45].”
An artificial spiking afferent nerve based on Mott memristors for neurorobotics (2020, background): “Neuromorphic computing architecture based on spiking neural networks (SNNs) has been recognized as an attractive candidate for its promising energy ef ﬁ ciency and powerful computing capacity 6 – 8 .”
Evolution of Plastic Learning in Spiking Networks via Memristive Connections (2012, background): “, the MLP [15], shown in [16] and [17], and 2) current NC research focussing on spiking neurons as a basis of communication due to low power requirements and the ability to harness spikes as a learning mechanism (e.”