Simulated in vivo electrophysiology experiments provide previously inaccessible insights into visual physiology

Maria Del Mar Quiroga, Nicholas SC Price

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


Lecture content and practical laboratory classes are ideally complementary. However, the types of experiments that have led to our detailed understanding of sensory neuroscience are often not amenable to classroom experimentation as they require expensive equipment, time-consuming surgeries, specialized experimental techniques, and the use of animals. While sometimes feasible in small group teaching, these experiments are not suitable for large cohorts of students. Previous attempts to expose students to sensory neuroscience experiments include: the use of electrophysiology preparations in invertebrates, data-driven simulations that do not replicate the experience of conducting an experiment, or simply observing an experiment in a research laboratory. We developed an online simulation of a visual neuroscience experiment in which extracellular recordings are made from a motion sensitive neuron. Students have control over stimulation parameters (direction and contrast) and can see and hear the action potential responses to stimuli as they are presented. The simulation provides an intuitive way for students to gain insight into neurophysiology, including experimental design, data collection and data analysis. Our simulation allows large cohorts of students to cost-effectively "experience" the results of animal research without ethical concerns, to be exposed to realistic data variability, and to develop their understanding of how sensory neuroscience experiments are conducted.

Original languageEnglish
Pages (from-to)A11-A17
Number of pages7
JournalJournal of Undergraduate Neuroscience Education
Issue number1
Publication statusPublished - 2016


  • Extracellular recording
  • Motion sensitivity
  • Neurophysiology
  • Online learning
  • Simulation
  • Statistical analysis
  • Tuning curves
  • Virtual experiment
  • Vision

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