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相关概念视频

Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

3.9K
Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
3.9K
Language and Cognition01:27

Language and Cognition

829
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
829
Language Development01:22

Language Development

963
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
963
Language01:16

Language

924
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
924
Components of Language01:24

Components of Language

837
Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
837
Neural Circuits01:25

Neural Circuits

2.9K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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相关实验视频

Updated: Feb 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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基于神经形态的大型语言模型.

Han Xu1,2,3, Xuerui Qiu1,4,5, Yunhui Xu6

  • 1Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

National science review
|February 16, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个统一的神经形态基大语言模型 (NSLLM) 框架,显著降低了能源消耗,提高了可解释性. 该NSLLM框架将大型语言模型 (LLM) 转换为高效,可解释的神经动态,为更绿色的人工智能铺平道路.

关键词:
这是一个跨学科的神经科学.神经形态计算是一种神经形态计算.基于尖的LLM.尖的线性注意力.

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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Decoding Natural Behavior from Neuroethological Embedding
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Decoding Natural Behavior from Neuroethological Embedding

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相关实验视频

Last Updated: Feb 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Decoding Natural Behavior from Neuroethological Embedding
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Decoding Natural Behavior from Neuroethological Embedding

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科学领域:

  • 神经形态工程的神经形态工程
  • 计算神经科学是一种神经科学.
  • 人工智能的人工智能

背景情况:

  • 大型语言模型 (LLM) 面临着高能耗和有限可解释性的挑战.
  • 目前的LLM架构是计算密集的,缺乏生物可信性.

研究的目的:

  • 提出一个统一的神经形态基大语言模型 (NSLLM) 框架.
  • 为了同时提高能源效率和LLMs的解释性.
  • 为LLMs提供一种新的神经科学视角.

主要方法:

  • 使用数学建模,量子化和散散化,将LLM转化为NSLLM.
  • 将LLM行为转化为神经动力学,就像尖峰列车一样.
  • 在FPGA上利用硬件算法联合设计范式与定制MatMul无硬件核心.
  • 利用计算神经科学工具来分析信息编码.

主要成果:

  • 实现了13.849W的动态功耗和1.5亿参数NSLLM的161.8令牌/秒的推断吞吐量.
  • 与A800 GPU相比,在能源效率 (19.8%),内存使用率 (21.3%) 和推断吞吐量 (2.2倍) 中显著改善.
  • 在NSLLM硬件核心中成功消除了矩阵乘法 (MatMul).

结论:

  • 该NSLLM框架提供了一种统一的方法来提高LLM的能源效率和可解释性.
  • 将LLM概念化为神经群体可以通过计算神经科学来增强理解.
  • 这些发现为设计大型模型的未来神经形态芯片提供了宝贵的见解.