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関連する概念動画

Purposive Learning01:22

Purposive Learning

411
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
411
Language Development01:22

Language Development

801
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...
801
Language01:16

Language

855
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...
855
Components of Language01:24

Components of Language

715
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.
715
Language and Cognition01:27

Language and Cognition

681
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.
681
Observational Learning01:12

Observational Learning

782
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
782

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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BERTを用いた知識蒸留により学習された言語スパイクフォーマー、SpikeBERT

Changze Lv1, Tianlong Li1, Weiming Qiao1

  • 1School of Computer Science, Fudan University, Shanghai, China; Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China.

Neural networks : the official journal of the International Neural Network Society
|December 25, 2025
PubMed
まとめ

SpikeBERTは、新しいスパイクニューラルネットワーク（SNN）アプローチであり、大幅にエネルギー消費を削減しながら、言語タスクにおいてBERTレベルのパフォーマンスを達成します。この手法は、自然言語処理のためのディープSNNを強化します。

キーワード:
BERT知識蒸留言語理解スパイクニューラルネットワーク

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科学分野:

  • 人工知能
  • 計算神経科学
  • 自然言語処理

背景:

  • スパイクニューラルネットワーク（SNN）はエネルギー効率の高いディープラーニングを提供しますが、浅いアーキテクチャのために言語タスクのパフォーマンスが遅れています。
  • 言語タスクのための既存のSNNは、BERTのようなTransformerモデルと比較してパフォーマンスのギャップを示しています。

研究 の 目的:

  • 高度な言語タスク処理のために、スパイクTransformerであるSpikformerを強化すること。
  • ディープSNNをトレーニングするための2段階知識蒸留法を開発すること。

主な方法:

  • 言語タスクのためのSpikformerアーキテクチャの改善。
  • 2段階知識蒸留：BERTの事前トレーニングとファインチューニング。
  • 大規模なラベルなしデータセットとタスク固有のデータセットを使用してSpikeBERTをトレーニングしました。

主要な成果:

  • SpikeBERTはSNNの中で最先端のパフォーマンスを達成しています。
  • 英語と中国語のテキスト分類においてBERTと同等の結果を達成しました。
  • BERTよりも大幅に低いエネルギー消費を示しました。

結論:

  • 提案された手法は、言語タスクのためのディープSNNを効果的にトレーニングします。
  • SpikeBERTは、NLPのためのTransformerモデルに代わる、実行可能でエネルギー効率の高い選択肢を提供します。
  • この研究は、言語処理におけるSNNとTransformerのパフォーマンスギャップを埋めます。