The study investigates whether a biological wiring diagram can serve as a computational substrate for tasks beyond its evolutionary purpose. Using the MaleCNS v1.0 Drosophila connectome as a fixed recurrent network, the authors train models to perform bounded addition and a grounded relational language task, achieving high accuracy (92.77% for addition and 61.59% for language) that surpasses degree-preserving rewired controls. The results demonstrate that the higher-order structure of the MaleCNS provides a reusable inductive bias for these cognitive tasks.
By Joonghui Cho, Minchan Kang, Daeshik Kim
arXiv:2602. 17997v3 Announce Type: replace Abstract: Animals perform coordinated whole-body movements under the control of neural systems shaped by brain-wide connectivity.
By Zehao Jin, Yaoye Zhu, Chen Zhang, Yanan Sui
arXiv:2610.10023v1 Announce Type: cross
Abstract: Understanding the extent to which measured synaptic wiring determines computation remains a central challenge. Here, we couple the proofread adult Dr...
By Eudald Correig-Fraga, Roger Guimer\`a, Marta Sales-Pardo
arXiv:2609.13899v1 Announce Type: cross
Abstract: The BabyLM challenge measures how much language a model can learn from developmentally-plausible, child-scale data rather than internet-scale corpora...
By Po-Han Chiang
arXiv:2609.06093v1 Announce Type: new
Abstract: Connectomes, graph-level maps of neurons and their synaptic connections, provide a structural basis for understanding how brain circuits support functi...
By Zhuolin Yu, Xingyu Liu, Yuanhao Jia, Yunhang Xiao, Hairuo Xue, Feihan Sun, Guozhang Chen
arXiv:2606. 14975v1 Announce Type: cross Abstract: How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning.
By Mo Shakiba, Rana Rokni, Mohammad Mohammadi, Nima Dehghani
arXiv:2609.25146v1 Announce Type: new
Abstract: Continual learning, the ability to learn from sequential experience while retaining and adapting prior knowledge, is central to intelligent systems ope...
By Hongwei Yan, Kanglei Zhou, Qi Cheng, Weiyi Dong, Chunyan Lan, Guanglong Sun, Jun Zhou, Qian Li, Yi Zhong, Liyuan Wang
Continual learning, the ability to learn from sequential experience while retaining and adapting prior knowledge, is central to intelligent systems operating in changing environments. However, convent...
arXiv:2607. 00025v1 Announce Type: cross Abstract: While deep learning models achieve state-of-the-art performance in complex tasks, they remain brittle when faced with new environments or sensory deprivation.
By Benquan Wang, Jingdao Chen
arXiv:2609.14384v1 Announce Type: new
Abstract: What must a neural system be capable of to implement language? Current research annotates stimuli with linguistic variables and tests which electrodes,...
By Elliot Murphy
Where generalization capacity--the ability to extract context-invariant relational structures--first emerges remains a central question in AI and neuroscience. The foundation for this capacity lies up...
arXiv:2607. 00397v1 Announce Type: cross Abstract: Understanding how complex cognitive functions are organized within artificial systems is central to interpreting large language models (LLMs) and relating them to biological cognition.
By Zhongxiang Sun, Haolang Lu, Qiang Ma, Qi Li, Qipeng Wang, Liang Pang, Chenyu Liu, Qiankun Li, Hao Sun, Kun Wang, Yi Zeng, Jun Xu, Guoqi Li, Ji-Rong Wen