# Distinguishing Learning Rules with Brain Machine Interfaces

arXiv: https://arxiv.org/abs/2206.13448 · HTML: https://mlsanity.com/paper/2206.13448/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 27 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Learning without Weight Transport](https://mlsanity.com/paper/1904.05391/) (2019)
- [Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules](https://mlsanity.com/paper/1801.05219/) (2018)
- [A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks](https://mlsanity.com/paper/1907.02649/) (2019)
- [Learning to solve the credit assignment problem](https://mlsanity.com/paper/1906.00889/) (2019)
- [Deep learning with asymmetric connections and Hebbian updates](https://mlsanity.com/paper/1812.07965/) (2018)
- [Identifying Learning Rules From Neural Network Observables](https://mlsanity.com/paper/2010.11765/) (2020)

## Built on by (most cited first, 5 of 5)

- [BRACTIVE: A Brain Activation Approach to Human Visual Brain Learning](https://mlsanity.com/paper/2405.18808/) (2024)
- [Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI](https://mlsanity.com/paper/2312.00236/) (2023)
- [Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example](https://mlsanity.com/paper/2506.06904/) (2025)
- [Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron](https://mlsanity.com/paper/2409.03749/) (2024)
- [Flexible inference for animal learning rules using neural networks](https://mlsanity.com/paper/2509.04661/) (2025)

## Abstract (arXiv)

Despite extensive theoretical work on biologically plausible learning rules, clear evidence about whether and how such rules are implemented in the brain has been difficult to obtain. We consider biologically plausible supervised- and reinforcement-learning rules and ask whether changes in network activity during learning can be used to determine which learning rule is being used. Supervised learning requires a credit-assignment model estimating the mapping from neural activity to behavior, and, in a biological organism, this model will inevitably be an imperfect approximation of the ideal mapping, leading to a bias in the direction of the weight updates relative to the true gradient. Reinforcement learning, on the other hand, requires no credit-assignment model and tends to make weight updates following the true gradient direction. We derive a metric to distinguish between learning rules by observing changes in the network activity during learning, given that the mapping from brain to behavior is known by the experimenter. Because brain-machine interface (BMI) experiments allow for precise knowledge of this mapping, we model a cursor-control BMI task using recurrent neural networks, showing that learning rules can be distinguished in simulated experiments using only observations that a neuroscience experimenter would plausibly have access to.