A biologically inspired computational model of human ventral temporal cortex

Zhang, Yiyuan; Zhou, Ke; Bao, Pinglei; Liu, Jia

doi:10.1016/j.neunet.2024.106437

Cited by 2 publications

(2 citation statements)

References 95 publications

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“…Specifically, the organization and specialization of the VTC are heavily influenced by connectivity among cortical areas within (Y. Zhang et al, 2024) and outside this region (Q. Chen et al, 2017;Stevens et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

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The Origin of Cognitive Modules for Face Processing: A Computational Evolutionary Perspective

Liu,

Wang,

Liu

2024

Preprint

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Despite extensive research, understanding how cognitive modules emerge remains elusive due to the complex interplay of genetic, developmental, and environmental factors. Computational modeling, however, provides a means of exploring their origins by directly manipulating these factors. Here we aimed to investigate the emergence of cognitive modules by developing the Dual-Task Meta-Learning Partitioned (DAMP) model, whose plastic architecture facilitates automatic structure optimization through a genetic algorithm that emulates natural selection by iteratively selecting for efficient learning fitness. We found that a specialized module for face identification robustly emerged in the DAMP model. Critically, the emergence of the face module was not influenced by the demands of cognitive tasks (identification versus categorization) or the type of stimuli (faces versus non-face objects). Instead, it was determined by the structural constraint of sparse connectivity within the network, suggesting that the face module may arise as an adaptation strategy to challenges posed by sparse connections in neural networks, rather than being an information processor required by certain stimuli or tasks. These findings provide a new evolutionary perspective on the formation of cognitive modules in the human brain, highlighting the pivotal role of the structural properties of neural networks in shaping their cognitive functionality.

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Section: Discussionmentioning

confidence: 99%

“…Therefore, with sparse connectivity, the DAMP model illustrates the emergence of the face module to balance the need to optimize learning efficiency and minimize connection costs (Y. Zhang et al, 2024), underscoring the critical role of network structure in driving functional specialization.…”

Section: Discussionmentioning

confidence: 99%

The Origin of Cognitive Modules for Face Processing: A Computational Evolutionary Perspective

Liu,

Wang,

Liu

2024

Preprint

Self Cite

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Balancing Selectivity and Generality in Object Recognition through Structured Interconnectivity

Zhang,

Liu,

Liu

2024

Preprint

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Balancing selectivity and generality in object recognition is a significant challenge, as it requires the ability to discern fine details that set objects apart while simultaneously embracing the common threads that classify them into one single category. Here we investigated how the brain addresses this challenge by examining the relationship between the interconnectivity of neural networks, the dimensionality of neural space, and the balance of selectivity and generality using neurophysiological data and computational modeling. We found that higher interconnectivity in the TEa of macaques IT cortex was associated with lower dimensionality and increased generality, while lower interconnectivity in the TEO correlated with higher dimensionality and enhanced selectivity. To establish the causal link, we developed a brain-inspired computational model formed through Hebbian and anti-Hebbian rules, with wiring length constraints derived from biological brains. The resulting structured interconnectivity created an optimal dimensionality of the neural space, allowing for efficient energy distribution across the representational manifold embedded in the neural space to balance selectivity and generality. Interestingly, this structured interconnectivity placed the network in a critical state that balances adaptability and stability, and fostered a cognitive module with cognitive impenetrability. In summary, our study underscores the importance of structured interconnectivity in achieving a balance between selectivity and generality, providing a unifying view of balancing two extreme demands in object recognition.

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A biologically inspired computational model of human ventral temporal cortex

Cited by 2 publications

References 95 publications

The Origin of Cognitive Modules for Face Processing: A Computational Evolutionary Perspective

The Origin of Cognitive Modules for Face Processing: A Computational Evolutionary Perspective

Balancing Selectivity and Generality in Object Recognition through Structured Interconnectivity

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