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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 Potentiation01:25

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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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Synaptic plasticity enables adaptive self-tuning critical networks.

Nigel Stepp1, Dietmar Plenz2, Narayan Srinivasa1

  • 1Center for Neural and Emergent Systems, Information and System Sciences Lab, HRL Laboratories LLC, Malibu, California, United States of America.

Plos Computational Biology
|January 16, 2015
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Summary
This summary is machine-generated.

This study shows how synaptic plasticity in simulated neural networks allows for critical brain activity, reconciling irregular neuron firing with the brain

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Complex Systems

Background:

  • Mammalian cortex exhibits spontaneous neural activity during rest.
  • Single neuron spiking is typically irregular and asynchronous.
  • Population measures suggest cortical resting state criticality, characterized by neuronal avalanches.

Purpose of the Study:

  • To reconcile irregular single neuron spiking with network criticality.
  • To investigate how synaptic plasticity supports critical brain dynamics.

Main Methods:

  • Simulated a 10,000 neuron deterministic, plastic network.
  • Investigated the role of short- and long-term synaptic plasticity.
  • Analyzed network behavior under external perturbations.

Main Results:

  • Synaptic plasticity enables networks to exhibit criticality despite asynchronous spiking.
  • Long-term excitatory plasticity adapts network connectivity.
  • Long-term inhibitory plasticity facilitates self-tuning to criticality.
  • Critical state characterized by specific branching parameter, critical exponent, and self-similarity distribution.

Conclusions:

  • Synaptic plasticity is crucial for maintaining cortical criticality.
  • The model reconciles single-neuron irregularity with population-level critical dynamics.
  • This provides a framework for understanding optimal information processing in the brain.