Neuroscience Research Notes

Object recognition is a cornerstone of human cognition, enabling reliable perception across transformations such as scale, viewpoint, illumination, occlusion, and contextual variability.
Neurobiological evidence suggests that such robustness emerges from distributed population codes, high-dimensional neural manifolds, and hierarchical representational learning across the ventral visual stream. Recent progress in neural decoding, representational geometry, and brain–AI alignment studies has substantially advanced our understanding of invariant object representations in the human brain.

This Special Issue aims to consolidate interdisciplinary research investigating the neurobiological basis of invariant object recognition, with a particular focus on population coding, neural dynamics,
representational invariance, and emergent neural computation. Emphasis is placed on integrating insights from systems neuroscience, cognitive neurobiology, neuroimaging, computational
neuroscience, and foundation models to uncover principles governing human visual intelligence. Despite the success of modern vision models, human object recognition continues to outperform
artificial systems in out-of-distribution generalization, sample efficiency, and robustness under sensory uncertainty. Understanding how the brain achieves invariance through sparse-distributed
coding, mixed selectivity, and context-adaptive neural ensembles are crucial for advancing both neuroscience and neuro-symbolic and brain-inspired AI systems.

Potential research topics include, but are not limited to:

• Neurobiological mechanisms of invariant object recognition
• Population coding, mixed selectivity, and sparse–distributed representations
• Neural manifolds, representational geometry, and embedding spaces
• Hierarchical processing in the ventral and dorsal visual streams
• Invariant feature binding and compositional representations
• Neural tolerance to scale, rotation, occlusion, and viewpoint transformations
• Temporal coding, neural oscillations, and recurrent dynamics
• fMRI, EEG, MEG, ECoG, and intracranial recordings in object perception
• Multimodal neural fusion and cross-modal representations
• Representational Similarity Analysis (RSA), CKA, and neural alignment metrics
• Neural decoding, encoding models, and brain–computer interfaces
• Brain-inspired deep learning, predictive coding, and energy-based models
• Attention mechanisms, top-down modulation, and active perception
• Neuroplasticity, continual learning, and adaptive representations
• Cross-species and developmental perspectives on population coding