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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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Integration of Synaptic Events01:28

Integration of Synaptic Events

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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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Propagation of Action Potentials01:23

Propagation of Action Potentials

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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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Action Potential01:14

Action Potential

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Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
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Graded Potential01:19

Graded Potential

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Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation.

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    This study introduces a novel spiking neural network model that encodes information using spike timing. This biologically inspired approach achieves high accuracy on tasks like MNIST, mimicking human decision-making trade-offs.

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    Area of Science:

    • Computational Neuroscience
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Biological brains utilize precise neuronal spike timing for rapid sensory processing.
    • Conventional artificial neural networks lack intrinsic temporal coding capabilities.

    Purpose of the Study:

    • To develop a spiking neural network (SNN) model encoding information via spike timing.
    • To enable supervised training of SNNs using biologically plausible mechanisms.
    • To explore energy-efficient, biologically inspired neural architectures.

    Main Methods:

    • Proposed an SNN model with information encoded in relative spike timing.
    • Implemented a supervised training method using backpropagation with exact derivative calculations.
    • Utilized a biologically plausible synaptic transfer function and trainable pulses.
    • Evaluated the model on time-encoded datasets, including MNIST.

    Main Results:

    • Successfully trained the SNN on multiple time-encoded datasets, including MNIST.
    • Achieved superior performance compared to other spiking models on MNIST.
    • Matched the quality of fully connected conventional networks with similar architectures.
    • Observed spontaneous discovery of accuracy-speed trade-off operating modes.

    Conclusions:

    • Demonstrated the computational power of biologically characteristic SNNs encoding information in spike timing.
    • The model offers a pathway towards energy-efficient, state-based, biologically inspired neural architectures.
    • Open-source code is provided for the developed model.