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相关概念视频

Cognitive Learning01:21

Cognitive Learning

433
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
433
Synaptic Signaling01:09

Synaptic Signaling

5.6K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
5.6K
Associative Learning01:27

Associative Learning

452
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
452
System of Memory01:23

System of Memory

6.3K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
6.3K
Observational Learning01:12

Observational Learning

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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...
213
Electrical Synapses01:28

Electrical Synapses

8.4K
Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
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相关实验视频

Updated: Jul 25, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Published on: March 9, 2019

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一个基于memristor的学习引擎,用于基于synaptic的线上学习.

Deyu Wang, Jiawei Xu, Feng Li

    IEEE transactions on biomedical circuits and systems
    |June 30, 2023
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    概括
    此摘要是机器生成的。

    这项研究引入了一种用于大脑启发的尖端神经网络 (SNN) 的新型学习引擎,该引擎能够实现复杂的基于痕迹的学习规则,如尖端时间依赖可塑性 (STDP) 和贝叶斯信任传播神经网络 (BCPNN),具有显著的能源效率.

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    相关实验视频

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    Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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    科学领域:

    • 神经形态工程的神经形态工程
    • 人工智能的人工智能
    • 材料科学 材料科学 材料科学

    背景情况:

    • 记忆器对于突触在线学习在尖端神经网络 (SNN) 中至关重要.
    • 现有的memristor方法与复杂的基于痕迹的学习规则 (如STDP和BCPNN) 进行斗争.
    • 需要高效的硬件实现先进的SNN学习算法.

    研究的目的:

    • 提出和实现一种新的学习引擎,用于SNN的基于线上学习.
    • 通过使用memristors实现复杂的学习规则,包括STDP和BCPNN.
    • 为了证明拟议的学习引擎的能源效率和性能.

    主要方法:

    • 开发了一个混合学习引擎,结合基于memristor和模拟计算块.
    • 利用memristors通过它们的非线性物理性质来模拟突触痕迹动态.
    • 集成模拟计算用于诸如加法,乘法,对数和整合之类的基本操作.
    • 设计了一个可重新配置的引擎来模拟STDP和BCPNN学习规则.

    主要成果:

    • 实现了低能耗:STDP的10.61 pJ/突触更新和BCPNN的51.49 pJ/突触更新.
    • 与ASIC同行相比,显著减少了能量 (147.03×和93.61×为180nm;9.39×和5.63×为40nm).
    • 在能源效率方面,STDP和BCPNN的性能分别超过了先进的神经形态平台 (Loihi,eBrainII) 的11.31×和13.13×.

    结论:

    • 拟议的学习引擎有效地在SNN中实现基于痕迹的STDP和BCPNN学习规则.
    • 这种架构与现有的ASIC和神经形态解决方案相比,可以大幅节省能源.
    • 基于memristor的设计为更高效和更强大的大脑启发的计算系统铺平了道路.