English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The "fire together, wire together" Hebbian model is a central principle forlearning in neuroscience, but surprisingly, it has found limited applicabilityin modern machine learning. In this paper, we take a first step towardsbridging this gap, by developing flavors of competitive Hebbian learning whichproduce sparse, distributed neural codes using online adaptation with minimaltuning. We propose an unsupervised algorithm, termed Adaptive Hebbian Learning(AHL). We illustrate the distributed nature of the learned representations viaoutput entropy computations for synthetic data, and demonstrate superiorperformance, compared to standard alternatives such as autoencoders, intraining a deep convolutional net on standard image datasets.

Learning Sparse, Distributed Representations using the Hebbian Principle