Computational Neuroscience

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Homeostatic plasticity

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Computational Neuroscience

Definition

Homeostatic plasticity is the mechanism by which neurons and synapses adjust their activity levels to maintain stability in response to changes in their environment. This process is essential for the brain's ability to adapt and ensure consistent functioning despite fluctuations in synaptic strength or overall network activity. It allows neurons to scale their excitability and synaptic efficacy, contributing to a balanced neural network.

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5 Must Know Facts For Your Next Test

  1. Homeostatic plasticity helps counteract the effects of Hebbian plasticity by ensuring that neurons do not become too excitable or depressed over time.
  2. This mechanism operates on multiple time scales, enabling short-term adjustments in response to immediate changes in activity and longer-term adaptations to chronic changes in input.
  3. Homeostatic plasticity can involve changes in receptor density, ion channel expression, or the overall structure of synapses.
  4. It is believed that homeostatic plasticity plays a key role in learning and memory by stabilizing neural circuits while still allowing for functional changes.
  5. Disruptions in homeostatic plasticity are linked to various neurological disorders, highlighting its importance for maintaining healthy brain function.

Review Questions

  • How does homeostatic plasticity interact with Hebbian plasticity to maintain neuronal stability?
    • Homeostatic plasticity serves as a counterbalance to Hebbian plasticity. While Hebbian plasticity strengthens synapses based on activity patterns, homeostatic plasticity adjusts overall synaptic strengths and neuronal excitability to prevent runaway excitation or depression. This interaction ensures that while learning occurs through Hebbian mechanisms, the network remains stable and functional over time.
  • Discuss the physiological mechanisms that underlie homeostatic plasticity in neurons.
    • Homeostatic plasticity involves several physiological mechanisms such as changes in the number and sensitivity of neurotransmitter receptors at the synapse, adjustments in ion channel expression, and alterations in synaptic structure. These changes help neurons respond to prolonged increases or decreases in activity by scaling their input-output relationship. For example, if a neuron experiences chronic inactivity, it may increase the number of excitatory receptors to enhance responsiveness.
  • Evaluate the implications of disrupted homeostatic plasticity for neurological disorders.
    • Disruptions in homeostatic plasticity can have significant implications for neurological disorders. For instance, conditions such as epilepsy may arise from an inability to regulate excitability properly, leading to excessive neuronal firing. Similarly, disorders like autism and schizophrenia have been linked to altered synaptic scaling mechanisms. Understanding these disruptions provides valuable insights into potential therapeutic targets for restoring balance within neural networks affected by these conditions.
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