Alan W Freeman
Alan W Freeman
Deep neural networks (DNNs) are practical and effective but, despite the name, they lack biological validity. The recent study by Kang et al.1 in Patterns takes a step toward rectifying this deficit by hard-wiring receptive fields into the ...
Prewired static visual receptive fields for environment-agnostic perception [0.03%]
环境无关感知的预设静态视觉接受场
Minjun Kang,Seungdae Baek,Se-Bum Paik
Minjun Kang
Biological brains can effortlessly adapt to continuously changing stimulus environments, whereas conventional deep neural networks (DNNs) remain highly susceptible to domain shifts. Here, we demonstrate that static, hard-wired receptive fie...
IoT-LLM: A framework for enhancing large language model reasoning from real-world sensor data [0.03%]
IoT-LLM：一种利用现实世界传感器数据增强大语言模型推理的框架
Tuo An,Yunjiao Zhou,Han Zou et al.
Tuo An et al.
Large language models (LLMs) excel in textual tasks but often struggle with physical-world reasoning tasks. Inspired by human cognition-where perception is fundamental to reasoning-we explore augmenting LLMs with enhanced perception abiliti...
Detecting clinically relevant topological structures in multiplexed spatial proteomics using TopKAT [0.03%]
使用TopKAT在多路复用空间蛋白质组学中检测临床相关的拓扑结构
Sarah Samorodnitsky,Katie Campbell,Amarise Little et al.
Sarah Samorodnitsky et al.
Multiplexed spatial proteomics profiling platforms expose the intricate geometric structure of cells in the tumor microenvironment (TME). The spatial arrangement of cells has been shown to have important clinical implications, correlating w...
Autonomous language-image generation loops converge to generic visual motifs [0.03%]
自主语言-图像生成循环收敛于通用视觉动机
Arend Hintze,Frida Proschinger Åström,Jory Schossau
Arend Hintze
Autonomous AI-to-AI creative systems promise new frontiers in machine creativity, yet we show that they systematically converge toward generic outputs. We built iterative feedback loops between Stable Diffusion XL (SDXL; image generation) a...
Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision [0.03%]
预测映射和坐标无关编码作为主动视觉递归神经网络模型中能量效率的后果
Thomas Nortmann,Philip Sulewski,Tim C Kietzmann
Thomas Nortmann
Despite moving our eyes from one location to another, our perception of the world is stable-an aspect thought to rely on predictive computations that use efference copies to predict the upcoming foveal input. Are these complex computations ...
Erik Hoel
Erik Hoel
Complex systems can be described at myriad different scales, and their causal workings often have a multiscale structure (e.g., a computer can be described at the microscale of its hardware circuitry, the mesoscale of its machine code, and ...
Zhicheng Lin,Aamir Sohail
Zhicheng Lin
The integration of generative AI (GenAI) into academic workflows represents a fundamental shift in scientific practice. While these tools can amplify productivity, they risk eroding the cognitive foundations of expertise by simulating the v...
The carbon and water footprints of data centers and what this could mean for artificial intelligence [0.03%]
数据中心的碳足迹和水足迹及其对人工智能意味着什么
Alex de Vries-Gao
Alex de Vries-Gao
Although there are ways to estimate the global power demand of artificial intelligence (AI) systems, it remains challenging to quantify the associated carbon and water footprints. The lack of distinction between AI and non-AI workloads in t...
Olive R Cawiding,Yun Min Song,Jae Kyoung Kim
Olive R Cawiding
Complex systems can often be analyzed at either the microscale of their individual components or the macroscale of their collective organization, yet it remains debated which level of description offers the most meaningful causal understand...