mlsanityData to 14 Oct 2026

System identification of neural systems: If we got it right, would we know?

arXiv 2302.06677 ↗First posted 13 Feb 2023q-bio.NC cs.AI cs.LGcited by 9

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Abstract (arXiv)

Artificial neural networks are being proposed as models of parts of the brain. The networks are compared to recordings of biological neurons, and good performance in reproducing neural responses is considered to support the model's validity. A key question is how much this system identification approach tells us about brain computation. Does it validate one model architecture over another? We evaluate the most commonly used comparison techniques, such as a linear encoding model and centered kernel alignment, to correctly identify a model by replacing brain recordings with known ground truth models. System identification performance is quite variable; it also depends significantly on factors independent of the ground truth architecture, such as stimuli images. In addition, we show the limitations of using functional similarity scores in identifying higher-level architectural motifs.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 17 of its 58 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

17 of 58 references.

References · 17

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5 more
  1. 2021Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNetcited by 61
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 4.

9 papers.

  1. 2024Evaluating Representational Similarity Measures from the Lens of Functional Correspondencecited by 12
  2. 2024AdaNCA: Neural Cellular Automata As Adaptors For More Robust Vision Transformercited by 4
  3. 2024Training the Untrainable: Introducing Inductive Bias via Representational Alignmentcited by 4
  4. 2025Estimating Neural Representation Alignment from Sparsely Sampled Inputs and Featurescited by 3
  5. 2026Stimulus symmetries can confound representational similarity analysescited by 1
  6. 2026Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networkscited by 1
  7. 2025Network of Theseus (like the ship)cited by 0
  8. 2026Discovering Mechanistic Models of Neural Activity: System Identification in an in Silico Zebrafishcited by 0
  9. 2026The Umwelt Representation Hypothesis: Rethinking Universalitycited by 0