Model testing, prediction and experimental protocols in neuroscience: A case study

In their theoretical and experimental reflections on the capacities and behaviours of living systems, neuroscientists often formulate generalizations about the behaviour of neural circuits. These generalizations are highly idealized, as they omit reference to the myriads of conditions that could perturb the behaviour of the modelled system in real-world settings. This article analyses an experimental investigation of the behaviour of place cells in the rat hippocampus, in which highly idealized generalizations were tested by comparing predictions flowing from them with real-world experimental results. The aim of the article is to identify under what conditions even single prediction failures regarding the behaviour of single cells sufficed to reject highly idealized generalizations, and under what conditions prima facie counter-examples were deemed to be irrelevant to the testing of highly idealized generalizations. The results of this analysis may contribute to understanding how idealized models are tested experimentally in neuroscience and used to make reliable predictions concerning living systems in real-world settings
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DOI 10.1016/j.shpsc.2012.04.001
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References found in this work BETA
Stuart Glennan (2005). Modeling Mechanisms. Studies in History and Philosophy of Science Part C 36 (2):443-464.
James Woodward (2000). Explanation and Invariance in the Special Sciences. British Journal for the Philosophy of Science 51 (2):197-254.

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Robert Kowalenko (2014). Ceteris Paribus Laws: A Naturalistic Account. International Studies in the Philosophy of Science 28 (2):133-155.

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