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Experimentalists and modelers: can we all just get along?
1Computation and Neural Systems Program, California Institute of Technology, Pasadena 91125.
Trends in Neurosciences
|November 1, 1992
Summary
Improve collaboration between theoretical and experimental neuroscientists by presenting data more effectively. Researchers should highlight neuronal response diversity and include raw, individual trial data to show variability, enhancing the understanding of neural stochasticity.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Effective collaboration between theoretical and experimental neuroscientists is crucial for advancing the field.
- Current data presentation practices in experimental neuroscience may hinder the integration of theoretical models.
- Understanding neuronal variability is key to accurately modeling brain function.
Purpose of the Study:
- To propose improvements in the presentation of experimental neuroscience data.
- To facilitate better communication and integration between theoretical and experimental neuroscience research.
- To enhance the accurate representation of neuronal behavior in computational models.
Main Methods:
- Analysis of current data presentation standards in experimental neuroscience publications.
- Discussion of the impact of data representation on theoretical modeling.
- Formulation of recommendations for improved data sharing and visualization.
Main Results:
- Experimental data often oversimplifies neuronal responses by focusing on 'representative' examples.
- The inherent stochasticity and trial-to-trial variability of neuronal activity are frequently obscured by averaging.
- Current practices do not adequately capture the diversity of response properties across neuronal populations.
Conclusions:
- Presenting a wider range of response properties, not just the average, is essential.
- Including raw, unprocessed single-trial data is critical for revealing neuronal variability.
- Adopting these changes will improve the utility of experimental data for theoretical neuroscience and advance our understanding of neural computation.