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相关概念视频

Neural Circuits01:25

Neural Circuits

2.6K
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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Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

1.9K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Understanding Memory01:19

Understanding Memory

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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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Neuronal Communication01:28

Neuronal Communication

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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
2.9K
Neuroplasticity01:01

Neuroplasticity

1.5K
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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相关实验视频

Updated: Jan 8, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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神经群体对记忆的活性:属性,计算和代码

David Dupret1, Stefano Fusi2, Stefano Panzeri3

  • 1Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Neuron
|December 23, 2025
PubMed
概括
此摘要是机器生成的。

了解大脑的记忆包括分析神经群体升活动. 这项研究探讨了有效记忆功能的神经代码的权衡.

关键词:
代码 代码 代码 代码计算 计算 计算 计算记忆 记忆 记忆 记忆 记忆神经人口活动神经人口活动.

更多相关视频

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
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Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo

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相关实验视频

Last Updated: Jan 8, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
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Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo

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科学领域:

  • 神经科学是一个神经科学.
  • 计算神经科学是一种神经科学.
  • 系统神经科学 系统神经科学

背景情况:

  • 记忆依赖于神经群体在获取,保留和检索阶段的活动模式.
  • 人口活动特征与内存属性,计算和代码之间的准确联系仍然不清楚.
  • 现有的研究经常单独研究记忆阶段,限制了对神经编码的整体理解.

研究的目的:

  • 从大脑网络生理学的角度综合记忆研究的最新进展.
  • 将内存属性和计算映射到人口活动代码特征.
  • 提出大脑记忆电路平衡了对人口代码的相互矛盾需求.

主要方法:

  • 这种观点综合了最近关于记忆和神经人口活动的研究结果.
  • 它采用大脑网络生理学的观点来分析记忆电路.
  • 该方法侧重于在人口代码中确定权衡.

主要成果:

  • 最近的进展为神经人口活动和记忆之间的关系提供了洞察力.
  • 大脑的记忆电路可能会实施权衡,以管理对人口代码的相互矛盾要求.
  • 建议在人口活动空间内建立一个"安全区",以确保神经元电路的高效运行.

结论:

  • 将记忆计算映射到人口活动代码需要理解神经电路的权衡.
  • 调查这些权衡对基本和翻译记忆研究至关重要.
  • 识别操作"安全区"可以指导人们了解记忆中的高效神经元电路功能.