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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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Long-term Potentiation01:35

Long-term Potentiation

58.0K
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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Design Example: Frog Muscle Response01:14

Design Example: Frog Muscle Response

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A student is tasked to work on an intriguing experiment involving an RL (Resistor-Inductor) circuit to study the muscle response of a frog's leg to electrical stimulation. The RL circuit plays a crucial role in this experiment, providing the means to control and measure the electrical impulses that trigger muscle contraction.
When the switch connecting the RL circuit is closed, a brief muscle contraction is observed. This is because, at a steady state, the inductor acts like a short...
523
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

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The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
4.5K
RC Circuit with Source01:15

RC Circuit with Source

2.5K
When a DC source is abruptly applied to an RC (Resistor-Capacitor) circuit, the voltage can be represented as a unit step function. The voltage across the capacitor, known as the step response, characterizes how the circuit reacts to this sudden change in input.
Due to the inherent properties of a capacitor, its voltage cannot change instantaneously. This means that immediately after the switch is closed, the capacitor's voltage remains the same as it was just before the switch was closed.
2.5K
Neural Circuits01:25

Neural Circuits

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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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Related Experiment Video

Updated: Jan 1, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

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A Model for R(t) Elements and R(t) -Based Spike-Timing-Dependent Plasticity With Basic Circuit Examples.

Robert C Ivans, Sumedha Gandharava Dahl, Kurtis D Cantley

    IEEE Transactions on Neural Networks and Learning Systems
    |December 24, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel method for electronic spiking neural networks to emulate biological spike-timing-dependent plasticity (STDP) using dynamic resistance elements. This approach enables local synaptic learning and spatiotemporal pattern recognition in memristive networks.

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    3D Modeling of Dendritic Spines with Synaptic Plasticity

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    Area of Science:

    • Neuroscience
    • Computer Science
    • Materials Science

    Background:

    • Spike-timing-dependent plasticity (STDP) is a crucial biological learning mechanism with implications for behavior and cognition.
    • Emulating STDP in electronic spiking neural networks (SNNs) using memristive synapses is a key goal for advanced AI.
    • Existing methods often rely on complex pulse-shaping techniques for STDP implementation.

    Purpose of the Study:

    • To propose and demonstrate an alternative STDP implementation in memristive SNNs.
    • To utilize time-varying dynamic resistance [R(t)] elements for local synaptic learning.
    • To showcase the efficacy of this method for spatiotemporal pattern recognition (STPR).

    Main Methods:

    • Implemented STDP using dynamic resistance [R(t)] elements connected to neuron circuits.
    • Leveraged voltage division for altering synaptic weights (memristor voltage).
    • Simulated R(t) element behaviors, a three-input-two-output network, and STPR.

    Main Results:

    • Demonstrated local synaptic learning capabilities including spike-pair STDP, spike triplet STDP, and firing rate dependency.
    • Successfully simulated a network with single-memristor synaptic connections and R(t) elements.
    • Validated the R(t) element approach for spatiotemporal pattern recognition (STPR).

    Conclusions:

    • The proposed dynamic resistance [R(t)] element method offers a viable alternative for STDP emulation in memristive SNNs.
    • This approach maintains synaptic density and enables versatile local learning rules.
    • The method shows promise for applications in spatiotemporal pattern recognition.