Cross-Species RSA Reveals Conserved Early Visual Alignment but Divergent Higher-Area Rankings Across Human fMRI and Macaque Electrophysiology
arXiv:2605.22401v1 Announce Type: new Abstract: Does the relationship between learning rules and brain alignment generalize across species? We extend our prior finding that untrained CNNs match backpropagation at human V1 by testing the same five learning rules against macaque electrophysiology. The rules are backpropagation (BP), feedback alignment (FA), predictive coding (PC), spike-timing-dependent plasticity (STDP), and an untrained random-weights baseline. The macaque data come from two datasets: MajajHong2015 (V4/IT, 3,200 stimulus presentations, 88/168 neurons) and FreemanZiemba2013 (V1
The proliferation of advanced AI models and growing interest in brain-inspired AI architectures drives research into understanding the fundamental alignment between biological and artificial learning mechanisms.
Understanding the fundamental principles of intelligence across species can inform the development of more robust, efficient, and biologically plausible AI, potentially leading to breakthroughs in AI design and neuroscience.
This research provides deeper insight into the biological underpinnings of learning algorithms, suggesting that some foundational visual processing mechanisms are highly conserved while higher-level cognitive functions diverge.
- · AI researchers
- · Neuroscience community
- · Developers of bio-inspired AI
- · Cognitive science
- · Purely empirical AI development approaches
Refined understanding of how different AI learning rules map to biological brain functions, particularly in early visual processing.
Development of new AI architectures or training methodologies inspired by the conserved and divergent brain mechanisms identified.
Potential for a unifying theory of intelligence that bridges biological and artificial systems, leading to more general and adaptable AI.
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Read at arXiv cs.LG