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Related Concept Videos

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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Related Experiment Video

Updated: Jan 16, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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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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Optimizing the Output of Long Short-Term Memory Cell for High-Frequency Forecasting in Financial Markets.

Adamantios Ntakaris, Moncef Gabbouj, Juho Kanniainen

    IEEE Transactions on Neural Networks and Learning Systems
    |October 1, 2025
    PubMed
    Summary

    This study introduces a novel, real-time adjusted long short-term memory (LSTM) cell for high-frequency trading (HFT) stock price forecasting. The revised LSTM cell improves prediction accuracy by dynamically selecting optimal gates and states, outperforming traditional recurrent neural networks (RNNs).

    Related Experiment Videos

    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

    Area of Science:

    • Quantitative Finance
    • Machine Learning
    • Computational Neuroscience

    Background:

    • High-frequency trading (HFT) necessitates rapid data processing to minimize information lags for accurate stock price forecasting.
    • Traditional methods often struggle with the time irregularities inherent in HFT data, treating vectors as time-independent signals.
    • Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, are established for sequential data but have limitations in optimal gate/state calculation order.

    Purpose of the Study:

    • To propose a revised, real-time adjusted LSTM cell designed to enhance stock price forecasting accuracy in HFT environments.
    • To address the limitations of standard LSTM cells by enabling dynamic selection of optimal gates and states.
    • To evaluate the performance of the proposed LSTM cell against existing RNNs for online HFT forecasting tasks.

    Main Methods:

    • Development of a novel LSTM cell architecture with real-time adjustment capabilities.
    • Implementation of a mechanism for the LSTM cell to select the best gate or state for its output.
    • Training the revised LSTM cell online with a shallow topology and minimal look-back period.
    • Testing the cell's performance on limit order book (LOB) mid-price (MP) prediction for both high-liquid U.S. and less-liquid Nordic stocks.

    Main Results:

    • The revised LSTM cell demonstrated lower forecasting error compared to other RNNs in online HFT forecasting.
    • The proposed cell's ability to select optimal gates/states contributed to improved prediction accuracy.
    • Effective performance was observed across different market liquidities, including U.S. and Nordic stock markets.

    Conclusions:

    • The real-time adjusted LSTM cell offers a significant improvement for precise stock price forecasting in HFT.
    • The dynamic gate/state selection mechanism enhances the adaptability and accuracy of the forecasting model.
    • This approach provides a more effective solution for online HFT forecasting tasks, particularly for limit order book mid-price prediction.