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Neuroplasticity01:01

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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Plasticity00:58

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Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...
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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....
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Neural Circuits01:25

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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.
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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.
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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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Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus
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Event-Driven Intrinsic Plasticity for Spiking Convolutional Neural Networks.

Anguo Zhang, Xiumin Li, Yueming Gao

    IEEE Transactions on Neural Networks and Learning Systems
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    Two novel event-driven intrinsic plasticity (IP) learning rules reduce computational load in spiking neural networks (SNNs). These rules enhance efficiency and speed up recognition tasks, paving the way for low-power brain-inspired computing.

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    Area of Science:

    • Computational Neuroscience
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Intrinsic plasticity (IP) is vital for neural information processing but incurs high energy costs.
    • Spiking neural networks (SNNs) offer energy efficiency through event-driven, sparse computations.
    • Existing IP rules require constant updates, hindering SNN efficiency.

    Purpose of the Study:

    • To introduce two novel event-driven intrinsic plasticity (IP) learning rules.
    • To reduce the computational and energy overhead of IP in SNNs.
    • To enhance the efficiency and speed of SNNs for recognition tasks.

    Main Methods:

    • Proposed input-driven and self-driven IP learning rules.
    • Developed a spiking convolutional neural network (SCNN) using the ANN2SNN conversion method.
    • Evaluated SCNN performance on MNIST, FashionMNIST, Cifar10, and SVHN datasets.

    Main Results:

    • Event-driven IP rules significantly reduced IP updating operations.
    • Achieved sparse computations and accelerated recognition processes.
    • Demonstrated the effectiveness of the proposed rules across multiple benchmark datasets.

    Conclusions:

    • Event-driven IP rules offer a promising approach for efficient SNNs.
    • These rules contribute to sparse computations and faster processing.
    • The findings support the development of low-power, brain-inspired SNNs.