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Multiple models to capture the variability in biological neurons and networks


How tightly tuned are the synaptic and intrinsic properties that give rise to neuron and circuit function? Experimental work shows that these properties vary considerably across identified neurons in different animals. Given this variability in experimental data, this review describes some of the complications of building computational models to aid in understanding how system dynamics arise from the interaction of system components. We argue that instead of trying to build a single model that captures the generic behavior of a neuron or circuit, it is beneficial to construct a population of models that captures the behavior of the population that provided the experimental data. Studying a population of models with different underlying structure and similar behaviors provides opportunities to discover unsuspected compensatory mechanisms that contribute to neuron and network function.

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Figure 1: The pyloric rhythm has a variable period but phase relationships are held invariant.
Figure 2: Example distributions of neuron parameters for neurons that all share a common behavior or set of behaviors.
Figure 3: Model LP neurons with similar behavior but substantially different parameters.
Figure 4: Tolerance and degeneracy.
Figure 5: Quantification of the effect of each model parameter on each model property for a population of LP models.


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This work was supported by US National Institutes of Health grants NS17813 and MH46742, and by the James D. McDonnell Foundation.

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E.M. and A.L.T. wrote and edited the paper. A.L.T. made the figures, some of which are adapted versions of figures originally published elsewhere.

Corresponding author

Correspondence to Eve Marder.

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The authors declare no competing financial interests.

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Marder, E., Taylor, A. Multiple models to capture the variability in biological neurons and networks. Nat Neurosci 14, 133–138 (2011).

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