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DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention
We introduce DISTA, a Denoising Spiking Transformer with Intrinsic Plasticity and SpatioTemporal Attention, designed to maximize the spatiotemporal computational prowess of spiking neurons, particularly for vision applications.
UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities
Large Language Models (LLMs) have demonstrated impressive inferential capabilities, with numerous research endeavors devoted to enhancing this capacity through prompting.
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
Spiking neural networks (SNNs) offer promise for efficient and powerful neurally inspired computation.
Composing Recurrent Spiking Neural Networks using Locally-Recurrent Motifs and Risk-Mitigating Architectural Optimization
The small size of the motifs and sparse inter-motif connectivity leads to an RSNN architecture scalable to large network sizes.
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