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Understanding Learning Mechanisms: Habituation, Sensitization, and Conditioning in Animal Models

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This resource delves into the quantification of learning through various principles in animal models, emphasizing key terms such as habituation, sensitization, and conditioning. Habituation refers to decreased responsiveness to stimuli, while sensitization indicates heightened response. Conditioning links behavioral and physiological events through paired stimuli. Exploring neuronal identifications associated with these processes, such as long-term potentiation (LTP) and long-term depression (LTD) in the hippocampus, reveals insights into how changes in neuronal connections facilitate learning and memory.

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Understanding Learning Mechanisms: Habituation, Sensitization, and Conditioning in Animal Models

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Presentation Transcript


  1. Learning

  2. Quantifying Learning Beyond Exams….. Principles of Neural Science (Kandel): Chapter 63

  3. Terms used to quantify learning in animal models: • Habituation- animal is less reactive to an external stimulus • Sensitization- animal is more reactive to an external stimulus • Conditioning (albeit it classical or operant)- behavioral or physiological events are reinforced by pairing two external stimuli that are inherently mutually exclusive (bell and food)

  4. Neuronal Identification of Habituation vs Sensitization

  5. Neuronal Identifications of Habituation

  6. Neuronal Identifications of Habituation

  7. Neuronal Identifications of Sensitization

  8. Neuronal Identifications of Sensitization (Short-Term, Cytoplasmic)

  9. Neuronal Identifications of Sensitization (Long-Term, Nuclear)

  10. LTP vs LTD in the hippocampus LTP: long-term potentiation LTD: long-term depression

  11. LTP vs. LTD is dependent on the number of connections and successful transmissions

  12. This results in a change in the intensity of neuronal firing, pre-synaptically

  13. And in a change in receptor expression, post-synpatically

  14. The fruit fly as a powerful tool to manipulate learning

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