# Multi-View Broad Learning System for Primate Oculomotor Decision Decoding

arXiv: https://arxiv.org/abs/1908.06180 · HTML: https://mlsanity.com/paper/1908.06180/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Multi-View Class Incremental Learning](https://mlsanity.com/paper/2306.09675/) (2023)
- [Graph-Embedded Intuitionistic Fuzzy Broad Learning System: A Multi-view Framework](https://mlsanity.com/paper/2607.16728/) (2026)

## Abstract (arXiv)

Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in decoding monkeys' oculomotor decision from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach for decoding the oculomotor decision than several classical and state-of-the-art single-view and multi-view learning approaches.