decoding-encoding-alignment-critique

name

decoding-encoding-alignment-critique

description

Critical analysis framework for brain-model alignment methodology. Demonstrates that representational similarity analysis (RSA) and decoding-based alignment metrics are fundamentally insensitive to encoding manifold topology. Similar decoding behavior and high representational alignment can arise from small, non-representative neuron subpopulations. Use when: evaluating brain-DNN alignment, RSA/DSA methodology, encoding vs decoding analysis, neural representation comparison, brain-model similarity metrics, neuroscience interpretability. Activation: decoding alignment, encoding alignment, RSA critique, representational similarity analysis, brain model alignment critique, encoding manifold, decoding manifold, neural representation comparison, alignment methodology.

Decoding-Encoding Alignment Critique

Fundamental critique of similarity analysis methods in neuroscience. Shows that decoding-based alignment metrics (RSA, DSA) are misleading because they can be driven by small, non-representative neuron subpopulations.

Core Argument

Popular methods (RSA, DSA, perceptual manifolds) assume that similarity in decoding representations implies similar computation. This is not necessarily true:

1. Subpopulation dominance: High alignment can be driven by a tiny subset of neurons
2. Encoding insensitivity: Alignment metrics are blind to encoding manifold topology
3. Causal evidence: Decoding metrics unchanged when encoding topology is manipulated

Key Findings

1. Subpopulation Effect

• Similar decoding behavior and high representational alignment can arise from small, non-representative subpopulations of neurons
• The representational geometry of a population may be shaped by very few neurons
• Alignment to a few neurons ≠ alignment to the whole population

2. Encoding vs Decoding Manifolds

• Decoding manifold: How well stimuli can be read out (what most metrics measure)