# A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions

arXiv: https://arxiv.org/abs/2306.02149 · HTML: https://mlsanity.com/paper/2306.02149/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 89 references that are in the map.
- First posted on arXiv 3 Jun 2023.

## Builds on (most cited first, 21 of 21)

- [The Forward-Forward Algorithm: Some Preliminary Investigations](https://mlsanity.com/paper/2212.13345/) (2022)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [Difference Target Propagation](https://mlsanity.com/paper/1412.7525/) (2014)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Training Neural Networks with Local Error Signals](https://mlsanity.com/paper/1901.06656/) (2019)
- [Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks](https://mlsanity.com/paper/2207.13243/) (2022)
- [Dendritic cortical microcircuits approximate the backpropagation algorithm](https://mlsanity.com/paper/1810.11393/) (2018)
- [Predictive Coding Approximates Backprop along Arbitrary Computation Graphs](https://mlsanity.com/paper/2006.04182/) (2020)
- [Putting An End to End-to-End: Gradient-Isolated Learning of Representations](https://mlsanity.com/paper/1905.11786/) (2019)
- [Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures](https://mlsanity.com/paper/2006.12878/) (2020)
- [Local plasticity rules can learn deep representations using self-supervised contrastive predictions](https://mlsanity.com/paper/2010.08262/) (2020)
- [Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic](https://mlsanity.com/paper/2008.09535/) (2020)
- [Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons](https://mlsanity.com/paper/2302.01228/) (2023)
- [Measuring multivariate redundant information with pointwise common change in surprisal](https://mlsanity.com/paper/1602.05063/) (2016)
- [Partial Information Decomposition as a Unified Approach to the Specification of Neural Goal Functions](https://mlsanity.com/paper/1510.00831/) (2015)
- [Dendritic predictive coding: A theory of cortical computation with spiking neurons](https://mlsanity.com/paper/2205.05303/) (2022)
- [A Measure of the Complexity of Neural Representations based on Partial Information Decomposition](https://mlsanity.com/paper/2209.10438/) (2022)
- [Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks](https://mlsanity.com/paper/2210.02996/) (2022)
- [Bio-Inspired, Task-Free Continual Learning through Activity Regularization](https://mlsanity.com/paper/2212.04316/) (2022)
- [Forward-Forward Contrastive Learning](https://mlsanity.com/paper/2305.02927/) (2023)
- [Bits from Biology for Computational Intelligence](https://mlsanity.com/paper/1412.0291/) (2014)

## Built on by (most cited first, 8 of 8)

- [A Brain-like Synergistic Core in LLMs Drives Behaviour and Learning](https://mlsanity.com/paper/2601.06851/) (2026)
- [What should a neuron aim for? Designing local objective functions based on information theory](https://mlsanity.com/paper/2412.02482/) (2024)
- [An Energy-Based Mechanism for Compositional Behavior](https://mlsanity.com/paper/2512.04745/) (2025)
- [Fully Geometric Multi-Hop Reasoning on Knowledge Graphs with Transitive Relations](https://mlsanity.com/paper/2505.12369/) (2025)
- [Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks](https://mlsanity.com/paper/2511.02584/) (2025)
- [Information-theoretic signatures of causality in Bayesian networks and hypergraphs](https://mlsanity.com/paper/2512.20552/) (2025)
- [Information Dynamics of Language Communication](https://mlsanity.com/paper/2606.30096/) (2026)
- [Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints](https://mlsanity.com/paper/2607.16027/) (2026)

## Abstract (arXiv)

Despite the impressive performance of biological and artificial networks, an intuitive understanding of how their local learning dynamics contribute to network-level task solutions remains a challenge to this date. Efforts to bring learning to a more local scale indeed lead to valuable insights, however, a general constructive approach to describe local learning goals that is both interpretable and adaptable across diverse tasks is still missing. We have previously formulated a local information processing goal that is highly adaptable and interpretable for a model neuron with compartmental structure. Building on recent advances in Partial Information Decomposition (PID), we here derive a corresponding parametric local learning rule, which allows us to introduce 'infomorphic' neural networks. We demonstrate the versatility of these networks to perform tasks from supervised, unsupervised and memory learning. By leveraging the interpretable nature of the PID framework, infomorphic networks represent a valuable tool to advance our understanding of the intricate structure of local learning.