What makes us humans: Differences in the critical dynamics underlying the human and fruit-fly connectome
Previous simulation studies on human connectomes suggested, that critical dynamics emerge subcrititcally in the so called Griffiths Phases. %This is the consequence of the strong heterogeneity of the graphs. Now we investigate this on the largest available brain network, the $21.662$ node fruit-fly connectome, using the Kuramoto synchronization model. As this graph is less heterogeneous, lacking modular structure and exhibit high topological dimension, we expect a difference from the previous results. Indeed, the synchronization transition is mean-field like, and the width of the transition region is larger than in random graphs, but much smaller than as for the KKI-18 human connectome. This demonstrates the effect of modular structure and dimension on the dynamics, providing a basis for better understanding the complex critical dynamics of humans.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences
Explainable NLP techniques primarily explain by answering "Which tokens in the input are responsible for this prediction?''. We argue that for NLP models that make predictions by comparing two input texts, it is more use…
Machine TranslationPredictionTranslationMetacognition and Uncertainty Communication in Humans and Large Language Models
Metacognition, the capacity to monitor and evaluate one's own knowledge and performance, is foundational to human decision-making, learning, and communication. As large language models (LLMs) become increasingly embedded…
Decision MakingEvaluating Artificial Systems for Pairwise Ranking Tasks Sensitive to Individual Differences
Owing to the advancement of deep learning, artificial systems are now rival to humans in several pattern recognition tasks, such as visual recognition of object categories. However, this is only the case with the tasks f…
What Makes a Good Generated Image? Investigating Human and Multimodal LLM Image Preference Alignment
Automated evaluation of generative text-to-image models remains a challenging problem. Recent works have proposed using multimodal LLMs to judge the quality of images, but these works offer little insight into how multim…
What Can String Probability Tell Us About Grammaticality?
What have language models (LMs) learned about grammar? This question remains hotly debated, with major ramifications for linguistic theory. However, since probability and grammaticality are distinct notions in linguistic…