# Is Learning in Biological Neural Networks based on Stochastic Gradient Descent? An analysis using stochastic processes

arXiv: https://arxiv.org/abs/2309.05102 · HTML: https://mlsanity.com/paper/2309.05102/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Optimal rates for zero-order convex optimization: the power of two function evaluations](https://mlsanity.com/paper/1312.2139/) (2013)
- [Shadowing Properties of Optimization Algorithms](https://mlsanity.com/paper/1911.05206/) (2019)
- [Interpreting learning in biological neural networks as zero-order optimization method](https://mlsanity.com/paper/2301.11777/) (2023)

## Built on by (most cited first, 1 of 1)

- [Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling](https://mlsanity.com/paper/2411.17567/) (2024)

## Abstract (arXiv)

In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued that the updating of connections in the brain relies only on local information, and therefore a stochastic gradient-descent type optimization method cannot be used. In this paper, we study a stochastic model for supervised learning in BNNs. We show that a (continuous) gradient step occurs approximately when each learning opportunity is processed by many local updates. This result suggests that stochastic gradient descent may indeed play a role in optimizing BNNs.