Scholarlink

<p>A major goal in systems neuroscience is to build computational models that capture the primate brain’s internal representations. Standard evaluations of artificial neural networks (ANNs) emphasize forward predictivity—how well model features predict neural responses—without testing whether model representations are themselves predictable from neural activity. Here we develop a diagnostic metric, reverse predictivity, that quantifies how well macaque inferior temporal cortex responses predict ANN unit activations. Using this comparative framework, we reveal a striking asymmetry: models with high forward predictivity (~50% variance explained) often contain units unpredictable from neural activity, reflecting biologically inaccessible dimensions. In contrast, monkey-to-monkey mappings are symmetric, providing an empirical reference point and indicating that the asymmetry reflects genuine representational mismatch. Reverse predictivity enables the identification of ‘common’ ANN units that are shared with the inferior temporal cortex, are behaviourally relevant and generalize across species, and ‘unique’ units lacking such alignment. Influenced by feature dimensionality, training objectives and adversarial robustness, reverse predictivity serves as a conservative diagnostic and comparative tool for guiding next-generation ANNs towards both high task performance and greater biological plausibility.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Reverse predictivity for bidirectional comparison of neural networks and biological brains

  • 期刊论文

  • 发表日期：

    2026年3月25日

查看全文
  • Sabine Muzellec,
  • Kohitij Kar

摘要

A major goal in systems neuroscience is to build computational models that capture the primate brain’s internal representations. Standard evaluations of artificial neural networks (ANNs) emphasize forward predictivity—how well model features predict neural responses—without testing whether model representations are themselves predictable from neural activity. Here we develop a diagnostic metric, reverse predictivity, that quantifies how well macaque inferior temporal cortex responses predict ANN unit activations. Using this comparative framework, we reveal a striking asymmetry: models with high forward predictivity (~50% variance explained) often contain units unpredictable from neural activity, reflecting biologically inaccessible dimensions. In contrast, monkey-to-monkey mappings are symmetric, providing an empirical reference point and indicating that the asymmetry reflects genuine representational mismatch. Reverse predictivity enables the identification of ‘common’ ANN units that are shared with the inferior temporal cortex, are behaviourally relevant and generalize across species, and ‘unique’ units lacking such alignment. Influenced by feature dimensionality, training objectives and adversarial robustness, reverse predictivity serves as a conservative diagnostic and comparative tool for guiding next-generation ANNs towards both high task performance and greater biological plausibility.