# Neuroscience-inspired online unsupervised learning algorithms

arXiv: https://arxiv.org/abs/1908.01867 · HTML: https://mlsanity.com/paper/1908.01867/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2019.

## Built on by (most cited first, 19 of 19)

- [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution](https://mlsanity.com/paper/2210.08340/) (2022)
- [Sequential Memory with Temporal Predictive Coding](https://mlsanity.com/paper/2305.11982/) (2023)
- [A Normative and Biologically Plausible Algorithm for Independent Component Analysis](https://mlsanity.com/paper/2111.08858/) (2021)
- [Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources](https://mlsanity.com/paper/2209.12894/) (2022)
- [Normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity](https://mlsanity.com/paper/2302.10051/) (2023)
- [Adaptive whitening in neural populations with gain-modulating interneurons](https://mlsanity.com/paper/2301.11955/) (2023)
- [Adaptive whitening with fast gain modulation and slow synaptic plasticity](https://mlsanity.com/paper/2308.13633/) (2023)
- [Error Bounds for a Matrix-Vector Product Approximation with Deep ReLU Neural Networks](https://mlsanity.com/paper/2111.12963/) (2021)
- [Duality Principle and Biologically Plausible Learning: Connecting the Representer Theorem and Hebbian Learning](https://mlsanity.com/paper/2309.16687/) (2023)
- [The Neuron as a Direct Data-Driven Controller](https://mlsanity.com/paper/2401.01489/) (2024)
- [Curl Descent: Non-Gradient Learning Dynamics with Sign-Diverse Plasticity](https://mlsanity.com/paper/2510.02765/) (2025)
- [Modern Neuromorphic AI: From Intra-Token to Inter-Token Processing](https://mlsanity.com/paper/2601.00245/) (2026)
- [A Neural Network with Local Learning Rules for Minor Subspace Analysis](https://mlsanity.com/paper/2102.05501/) (2021)
- [Learning in Deep Neural Networks Using a Biologically Inspired Optimizer](https://mlsanity.com/paper/2104.11604/) (2021)
- [Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training](https://mlsanity.com/paper/2308.02427/) (2023)
- [Neural Crystals](https://mlsanity.com/paper/2311.16111/) (2023)
- [Neuronal Temporal Filters as Normal Mode Extractors](https://mlsanity.com/paper/2401.03248/) (2024)
- [Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales](https://mlsanity.com/paper/2506.06134/) (2025)
- [Flexible Online Representation Learning Based on Similarity Matching](https://mlsanity.com/paper/2606.01546/) (2026)

## Abstract (arXiv)

Although the currently popular deep learning networks achieve unprecedented performance on some tasks, the human brain still has a monopoly on general intelligence. Motivated by this and biological implausibility of deep learning networks, we developed a family of biologically plausible artificial neural networks (NNs) for unsupervised learning. Our approach is based on optimizing principled objective functions containing a term that matches the pairwise similarity of outputs to the similarity of inputs, hence the name - similarity-based. Gradient-based online optimization of such similarity-based objective functions can be implemented by NNs with biologically plausible local learning rules. Similarity-based cost functions and associated NNs solve unsupervised learning tasks such as linear dimensionality reduction, sparse and/or nonnegative feature extraction, blind nonnegative source separation, clustering and manifold learning.