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相关概念视频

Neural Circuits01:25

Neural Circuits

1.2K
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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Electrical Synapses01:28

Electrical Synapses

8.3K
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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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...
949
Biasing of FET01:22

Biasing of FET

283
Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
In an N-channel JFET, the structure consists of N-type material forming the channel on a P-type substrate, with the...
283
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.2K
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....
3.2K
Integration of Synaptic Events01:28

Integration of Synaptic Events

1.5K
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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相关实验视频

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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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为二元神经网络提供单体集成补充铁电FET XNOR突触.

Junghyeon Hwang1, Hongrae Joh1, Chaeheon Kim1

  • 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea.

ACS applied materials & interfaces
|January 4, 2024
PubMed
概括

研究人员使用互补的铁电晶体管开发了高密度,准确的非挥发性XNOR突触. 这一突破增强了神经形态计算和人工智能硬件,提高了图像识别和能源效率.

关键词:
二元神经网络是二元神经网络.补充的铁电场效应晶体管.在内存中进行计算.聚焦的微波火火.一个单一的三维整合.

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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:

  • 神经形态计算和人工智能硬件.
  • 先进的半导体设备物理和制造.

背景情况:

  • 神经形态计算模仿大脑以获得高效的AI硬件.
  • 基于XNOR突触的二元神经网络 (BNNs) 提供了紧的尺寸和低成本.
  • 现有的XNOR突触面临细胞密度和精度之间的权衡.

研究的目的:

  • 为了开发高密度和精度的非挥发性XNOR突触.
  • 为了克服以前的XNOR突触设计的局限性.
  • 为人工智能和神经形态系统推进硬件实现.

主要方法:

  • 使用单立体堆叠的互补铁电场效应晶体管 (C-FeFET).
  • 采用双门配置和独特的操作方案,用于n型铁电TFT.
  • 执行了数组级模拟 (512x512子数组) 和系统级分析.

主要成果:

  • 通过使用2C-FeFETs,高精度地达到每细胞密度60F2.
  • 与其他突触相比,显示出更好的图像识别精度 (MNIST +3.17%,CIFAR-10 +14.07%)
  • 展示了高吞吐量 (717.37 GOPS) 和能源效率 (196.7 TOPS/W).

结论:

  • 开发的C-FeFET非挥发性XNOR突触提供了更高的密度和精度.
  • 这种方法显著提高了AI硬件和神经形态系统的性能.
  • 这项技术对高密度内存,内存逻辑和神经网络硬件具有前景.