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Updated: Jun 27, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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How 'Neural' is a Neural Foundation Model?

Johannes Bertram1, Luciano Dyballa2, Anderson Keller3

  • 1University of Tubingen, Germany.

Arxiv
|February 6, 2026
PubMed
Summary
This summary is machine-generated.

Foundation models offer insights into brain function, but their inner workings remain unclear. This study analyzes a neural activity model, revealing distinct representations across processing stages and suggesting biologically inspired design improvements.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Systems Neuroscience

Background:

  • Foundation models excel at modeling biological visual systems.
  • The black-box nature of these models hinders understanding of brain function.
  • Physiological analysis of neural activity models is crucial for biological relevance.

Purpose of the Study:

  • To investigate the internal mechanisms of a state-of-the-art foundation model for neural activity.
  • To characterize individual 'neurons' based on temporal response properties to stimuli.
  • To understand how different stimuli and neurons are represented within the model's architecture.

Main Methods:

  • Characterized model 'neurons' by their temporal response properties to parametric stimuli.
  • Built decoding and neural encoding manifolds to analyze stimulus and neural representations.
  • Introduced a 'tubularity' metric to quantify stimulus-dependent neural activity development.

Main Results:

  • Different model modules (encoder, recurrent, readout) exhibit distinct representational structures.
  • The recurrent module enhances representation by differentiating temporal stimulus patterns.
  • The readout module achieves high fidelity via specialized feature maps, lacking biological plausibility.
  • The 'tubularity' metric indicates biologically plausible neural activity development.

Conclusions:

  • The study provides insights into the internal workings and biological relevance of neural foundation models.
  • Findings suggest design modifications for greater biological alignment: incorporating early recurrence and constrained readout features.
  • This work bridges artificial intelligence and neuroscience by analyzing model internals through a physiological lens.