What does it mean to understand a neural network?
Not on the map. Why?
Abstract (arXiv)
We can define a neural network that can learn to recognize objects in less than 100 lines of code. However, after training, it is characterized by millions of weights that contain the knowledge about many object types across visual scenes. Such networks are thus dramatically easier to understand in terms of the code that makes them than the resulting properties, such as tuning or connections. In analogy, we conjecture that rules for development and learning in brains may be far easier to understand than their resulting properties. The analogy suggests that neuroscience would benefit from a focus on learning and development.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jul 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2021Causal Abstractions of Neural Networkscited by 401
- 2025The Dead Salmons of AI Interpretabilitycited by 10
- 2024Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neurosciencecited by 5
- 2026Optimal Learning Rate Schedule for Balancing Effort and Performancecited by 1
- 2026Optimal Learning Rate Schedule for Balancing Effort and Performance12 Jan 2026
- 2025The Dead Salmons of AI Interpretability21 Dec 2025
- 2024Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience22 Aug 2024
- 2021Causal Abstractions of Neural Networks6 Jun 2021