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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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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 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 depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) 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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Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus

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Network-timing-dependent plasticity.

Vincent Delattre1, Daniel Keller2, Matthew Perich3

  • 1Laboratory of Neural Microcircuitry, Brain and Mind Institute, École Polytechnique Fédérale de Lausanne Lausanne, Switzerland.

Frontiers in Cellular Neuroscience
|June 25, 2015
PubMed
Summary
This summary is machine-generated.

Network bursts profoundly impact Spike-Timing-Dependent Plasticity (STDP). Depending on timing, network bursts can block, reverse, or saturate STDP, leading to a new resource-dependent learning rule for neural networks.

Keywords:
STDPacute brain slicesneural networks simulationspatch-clampself-organized criticalitysomatosensory cortexsynaptic plasticity

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Synaptic Plasticity

Background:

  • Neuronal network bursts are crucial for information processing and synaptic plasticity.
  • Spike-Timing-Dependent Plasticity (STDP) modifies synaptic strength based on spike timing.

Purpose of the Study:

  • To investigate the effect of network bursts on STDP.
  • To develop and test a resource-dependent STDP learning rule.

Main Methods:

  • Experiments on acute slices of juvenile rat somatosensory cortex.
  • Paired stimulation of network bursts with STDP protocols (LTP and LTD).
  • Development and simulation of a resource-dependent STDP learning rule in a model neural network.

Main Results:

  • Network bursts significantly altered STDP, flipping Long-Term Potentiation (LTP) to Long-Term Depression (LTD) and vice versa, depending on timing.
  • A resource-dependent STDP learning rule was proposed to explain these observations.
  • Simulations using the new rule showed homeostatic regulation of synaptic coupling and power-law distributed burst amplitudes, indicative of self-organized criticality.

Conclusions:

  • Network bursts dynamically modulate STDP, challenging previous assumptions.
  • The resource-dependent STDP rule provides a framework for understanding how network activity influences synaptic learning.
  • Self-organized criticality in neural networks, driven by this rule, may optimize information coding.