This paper proposes Spiking Fourier Graph Operators (SpikF-GO) for multivariate time series forecasting using Spiking Neural Networks (SNNs), introducing Hard Concrete frequency gates and Complex LIF…

The paper demonstrates that quadratic integrate-and-fire (QIF) neurons are superior to leaky integrate-and-fire (LIF) neurons for gradient descent training in spiking neural networks because their con…

The paper introduces Spike-PTSD, a novel, biologically inspired adversarial attack framework that successfully compromises the robustness of Spiking Neural Networks (SNNs) by modeling abnormal neural…

This paper introduces Mega, a digital architecture for Convolutional Spiking Neural Networks (SNNs) that addresses underutilization of parallelism and inflexibility in existing SNN accelerators throug…

The paper introduces SpikeWFM, a novel hybrid architecture combining spiking neural networks (SNNs) and transformers, which significantly improves the robustness and accuracy of wireless foundation mo…

This paper presents a spiking neural network architecture for generating polar trajectories using a winner-take-all architecture and accessory populations, achieving significant reductions in step tim…

The paper evaluates 27 different Spiking Neural Network (SNN) configurations to determine the optimal design for network intrusion detection, finding that the LeakyParallel neuron combined with latenc…

The paper systematically evaluates 27 Spiking Neural Network (SNN) configurations to determine the optimal combination of neuron model and spike encoding scheme for network intrusion detection, findin…

A modular, event-driven neuromorphic architecture for spiking neural network inference is presented, allowing for flexible configuration of neuron model, precision, and partitioning.

The paper presents an open-source framework for assessing biological plausibility of spiking neuron models by optimizing parameters to replicate canonical neuronal firing patterns.

The paper introduces FHE-DiCSNN, a novel framework that uses the TFHE scheme to enable secure and efficient computation on Spiking Neural Networks (SNNs), achieving high accuracy and fast inference ti…

This paper proposes and validates a novel hardware architecture, ITP-STDP, to significantly reduce the energy consumption and hardware overhead associated with training Spiking Neural Networks (SNNs).

This paper proposes SpikingMOT, a spike-driven multi-object tracking system that uses spiking neural networks and adaptively models sparse trajectory dynamics.

This paper introduces a mechanistic neuronal network model for multilayer learning, offering biological insights and an alternative to backpropagation.

A new error backpropagation method called supervised counterstream learning is proposed for deep associative networks, which only requires recognition of errors during training and backpropagates corr…

This paper presents a neuromorphic primitive using memristive edges with inhibitory couplings for autonomous learning in oscillatory neural networks, enabling the persistence of anti-phase attractors.

This paper compares the cost-performance trade-off of Hebbian learning, Dense Difference Target Propagation (DDTP), and backpropagation (BP) using mutual-information-based measures.