Multiple models to capture the variability in biological neurons and networks

By Eve Marder, Adam L. Taylor. Nature Neuroscience, 2011

467 citations recorded by the source corpus.

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.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/809b97502ed0b25076534751d47445d366dd5d03

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Neuronal homeostasis: time for a change? (2011, background): “…failure of averaging pose problems when studying conductance regulation across a whole population of cells: it may not make sense to look at average conductance profiles, since the intrinsic properties they give rise to do not necessarily correspond to these averages (Marder & Taylor, 2011).”
The human connectome: Origins and challenges (2013, background): “…variability and functional consistency suggests a high level of “degeneracy” (Tononi et al., 1999), the capacity to achieve a given neural function, for example rhythmic firing in central pattern generators, in a large number of possible circuits (Goaillard et al., 2009; Marder and Taylor, 2011).”
Wisdom of crowds for robust gene network inference (2012, background): “In systems biology, robust models are often constructed from ensembles of instances (e.g., different parameterizations or model structures) that are derived from experimental data via a single approach 26–30 , such as Monte Carlo sampling.”
Computational Implications of Biophysical Diversity and Multiple Timescales in Neurons and Synapses for Circuit Performance (2016, background): “Recent studies highlight the importance of building populations of models that capture the variability of parameters seen in experimental measurements [21,22].”
The theory of constructed emotion: an active inference account of interoception and categorization (2017, unknown): “Degeneracy is ubiquitous in biology, from the workings inside a single cell to distributed brain networks (e.g. see Tononi et al., 1999; Edelman and Gally, 2001; Marder and Taylor, 2011).”
On the nature of seizure dynamics. (2014, background): “This concept, that there are many functional biological pathways that produce the same outcome, was developed and verified experimentally in simple neuronal networks (Marder and Taylor, 2011) and is present in human biology as well (Beall, 2007).”