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Long-term Potentiation01:35

Long-term Potentiation

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Long-term Potentiation01:25

Long-term Potentiation

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
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相关实验视频

Updated: Jan 16, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

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

Published on: March 25, 2014

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优化长期短期记忆细胞的输出,用于金融市场的高频预测.

Adamantios Ntakaris, Moncef Gabbouj, Juho Kanniainen

    IEEE transactions on neural networks and learning systems
    |October 1, 2025
    PubMed
    概括

    本研究引入了一种新的,实时调整的长期短期记忆 (LSTM) 单元,用于高频交易 (HFT) 股票价格预测. 修订后的LSTM细胞通过动态选择最佳门和状态来提高预测准确性,优于传统的循环神经网络 (RNN).

    科学领域:

    • 量化金融 量化金融
    • 机器学习 机器学习
    • 计算神经科学是一种神经科学.

    背景情况:

    • 高频交易 (HFT) 需要快速处理数据,以尽量减少信息滞后,以便准确预测股价.
    • 传统的方法经常与HFT数据固有的时间不规则作斗争,将矢量视为时间独立的信号.
    • 循环神经网络 (RNN),特别是长期短期记忆 (LSTM) 网络,用于序列数据,但在最佳门/状态计算顺序方面存在局限性.

    研究的目的:

    • 提出一个修订后的实时调整的LSTM单元,旨在提高HFT环境中的股价预测准确度.
    • 通过使最佳门和状态的动态选择,解决标准LSTM细胞的局限性.
    • 评估拟议的LSTM单元与现有的RNN对线上HFT预测任务的性能.

    主要方法:

    • 开发一种具有实时调整能力的新型LSTM细胞架构.
    • 实现一个机制,使LSTM单元为其输出选择最佳的门或状态.
    • 在线训练修订后的LSTM单元,采用浅层拓和最小的回顾时间.
    • 测试单元在限量订单簿 (LOB) 中期价格 (MP) 预测中对高流动性美国股票和较少流动性的北欧股票的表现.

    主要成果:

    • 修订后的LSTM单元显示在线HFT预测中,与其他RNN相比,预测误差较低.

    相关实验视频

    Last Updated: Jan 16, 2026

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
    07:34

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

    Published on: March 25, 2014

    10.3K
  • 拟议的细胞选择最佳门/状态的能力有助于提高预测准确度.
  • 在不同的市场流动性,包括美国和北欧股票市场,观察到有效的表现.

    • 结论:

    • 实时调整的LSTM单元为HFT中精确的股价预测提供了显著的改进.
    • 动态门/状态选择机制提高了预测模型的适应性和准确性.
    • 这种方法为在线HFT预测任务提供了更有效的解决方案,特别是对于限量订单簿中期价格预测.