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…

This paper introduces energy-aware learning, an approach that reduces actuator energy in closed-loop deep brain stimulation systems by incorporating actuator energy into the reinforcement learning rew…

Turning off automatic NUMA balancing in simulation of large-scale spiking networks reduces energy consumption by 30%.

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 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…

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

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

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

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…

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…

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 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 compares and optimizes CUDA strategies for a shallow neural network, achieving a 1.41x speedup on a large dataset.

This paper argues for the importance of modularity and heterogeneity in AI architectures, contrasting the Transformer model with the structure of the cortex.