# Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment

arXiv: https://arxiv.org/abs/2312.09257 · HTML: https://mlsanity.com/paper/2312.09257/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 82 of its 480 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 30 of 82)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Exact solutions to the nonlinear dynamics of learning in deep linear neural networks](https://mlsanity.com/paper/1312.6120/) (2013)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [The Forward-Forward Algorithm: Some Preliminary Investigations](https://mlsanity.com/paper/2212.13345/) (2022)
- [Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation](https://mlsanity.com/paper/1602.05179/) (2016)
- [Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning](https://mlsanity.com/paper/1605.08104/) (2016)
- [Decoupled Neural Interfaces using Synthetic Gradients](https://mlsanity.com/paper/1608.05343/) (2016)
- [Predictive Coding Approximates Backprop along Arbitrary Computation Graphs](https://mlsanity.com/paper/2006.04182/) (2020)
- [Deep Learning without Weight Transport](https://mlsanity.com/paper/1904.05391/) (2019)
- [How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation](https://mlsanity.com/paper/1407.7906/) (2014)
- [Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks](https://mlsanity.com/paper/2001.05992/) (2020)
- [Revisiting Locally Supervised Learning: an Alternative to End-to-end Training](https://mlsanity.com/paper/2101.10832/) (2021)
- [Reviving and Improving Recurrent Back-Propagation](https://mlsanity.com/paper/1803.06396/) (2018)
- [On weight initialization in deep neural networks](https://mlsanity.com/paper/1704.08863/) (2017)
- [Reweighted Wake-Sleep](https://mlsanity.com/paper/1406.2751/) (2014)
- [Towards Biologically Plausible Deep Learning](https://mlsanity.com/paper/1502.04156/) (2015)
- [Scaling Forward Gradient With Local Losses](https://mlsanity.com/paper/2210.03310/) (2022)
- [Training End-to-End Analog Neural Networks with Equilibrium Propagation](https://mlsanity.com/paper/2006.01981/) (2020)
- [Hebbian Deep Learning Without Feedback](https://mlsanity.com/paper/2209.11883/) (2022)
- [Unbiased Online Recurrent Optimization](https://mlsanity.com/paper/1702.05043/) (2017)
- [Unsupervised Learning by Competing Hidden Units](https://mlsanity.com/paper/1806.10181/) (2018)
- [Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex](https://mlsanity.com/paper/1604.03640/) (2016)
- [Biologically-plausible learning algorithms can scale to large datasets](https://mlsanity.com/paper/1811.03567/) (2018)
- [The Predictive Forward-Forward Algorithm](https://mlsanity.com/paper/2301.01452/) (2023)
- [Equivalence of Equilibrium Propagation and Recurrent Backpropagation](https://mlsanity.com/paper/1711.08416/) (2017)
- [Feedback alignment in deep convolutional networks](https://mlsanity.com/paper/1812.06488/) (2018)

## Built on by (most cited first, 15 of 15)

- [Stochastic Forward-Forward Learning through Representational Dimensionality Compression](https://mlsanity.com/paper/2505.16649/) (2025)
- [Temporal-Difference Learning Using Distributed Error Signals](https://mlsanity.com/paper/2411.03604/) (2024)
- [A Contrastive Symmetric Forward-Forward Algorithm (SFFA) for Continual Learning Tasks](https://mlsanity.com/paper/2409.07387/) (2024)
- [Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections](https://mlsanity.com/paper/2508.11659/) (2025)
- [Accelerated Predictive Coding Networks via Direct Kolen-Pollack Feedback Alignment](https://mlsanity.com/paper/2602.15571/) (2026)
- [HCL-FF: Hierarchical and Contrastive Learning for Forward-Forward Algorithm](https://mlsanity.com/paper/2605.24797/) (2026)
- [Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps](https://mlsanity.com/paper/2402.12465/) (2024)
- [Forward Learning of Graph Neural Networks](https://mlsanity.com/paper/2403.11004/) (2024)
- [Emerging NeoHebbian Dynamics in Forward-Forward Learning: Implications for Neuromorphic Computing](https://mlsanity.com/paper/2406.16479/) (2024)
- [On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization](https://mlsanity.com/paper/2408.09210/) (2024)
- [Meta-Representational Predictive Coding: Neuroscience-Informed Self-Supervised Learning](https://mlsanity.com/paper/2503.21796/) (2025)
- [Hebbian Learning with Global Direction](https://mlsanity.com/paper/2601.21367/) (2026)
- [Error Highways: Scaling Predictive Coding to Very Deep Networks](https://mlsanity.com/paper/2606.22744/) (2026)
- [Lock-in EP: An In-Situ Training Algorithm for Oscillatory Hardware](https://mlsanity.com/paper/2610.07283/) (2026)
- [DeltaNet-PC: Test-Time Memory Without Backpropagation Through Time](https://mlsanity.com/paper/2610.15185/) (2026)

## Abstract (arXiv)

In this survey, we examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. These processes are unified under one possible taxonomy, which is constructed based on how a learning algorithm answers a central question underpinning the mechanisms of synaptic plasticity in complex adaptive neuronal systems: where do the signals that drive the learning in individual elements of a network come from and how are they produced? In this unified treatment, we organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors and its known criticisms. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes, wherein lies an important opportunity to build a strong bridge between machine learning, computational neuroscience, and cognitive science.