Spike Timing-Dependent Plasticity (STDP) is a promising substitute to backprop-agation for local training of Spiking Neural Networks (SNNs) on neuromorphic hardware. STDP allows SNNs to address classification tasks by combining unsupervised STDP for feature extraction and supervised STDP for classification. Unsupervised STDP is usually employed with Winner-Takes-All (WT A) competition to learn distinct patterns.

We present a novel OCR-free document understanding framework based on pre-trained Multimodal Large Language Models (MLLMs).

However, in certain specialized domains, such as healthcare or harmless content generation, it is nearly impossible to obtain a large volume of high-quality data that matches the downstream distribution.