ArticleFrontiers in computational neuroscience2010
An inverse approach for elucidating dendritic function.
Article in Frontiers in computational neuroscience, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Targeted pruning of a neuron's dendritic tree via femtosecond laser dendrotomy.Scientific reports · 2016Article
- An efficient and extendable python library to analyze neuronal morphologies.Neuroinformatics · 2014Article
- Combined role of seizure-induced dendritic morphology alterations and spine loss in newborn granule cells with mossy fiber sprouting on the hyperexcitability of a computer model of the dentate gyrus.PLoS computational biology · 2014Article
- Dendritic atrophy constricts functional maps in resonance and impedance properties of hippocampal model neurons.Frontiers in cellular neuroscience · 2014Article
- Context-aware modeling of neuronal morphologies.Frontiers in neuroanatomy · 2014Article
- Self-referential forces are sufficient to explain different dendritic morphologies.Frontiers in neuroinformatics · 2013Article
- Synthesis of neural networks for spatio-temporal spike pattern recognition and processing.Frontiers in neuroscience · 2013Article
- Influence of highly distinctive structural properties on the excitability of pyramidal neurons in monkey visual and prefrontal cortices.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2012Article
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2 authors.
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Abstract
We outline an inverse approach for investigating dendritic function-structure relationships by optimizing dendritic trees for a priori chosen computational functions. The inverse approach can be applied in two different ways. First, we can use it as a "hypothesis generator" in which we optimize dendrites for a function of general interest. The optimization yields an artificial dendrite that is subsequently compared to real neurons. This comparison potentially allows us to propose hypotheses about the function of real neurons. In this way, we investigated dendrites that optimally perform input-order detection. Second, we can use it as a "function confirmation" by optimizing dendrites for functions hypothesized to be performed by classes of neurons. If the optimized, artificial, dendrites resemble the dendrites of real neurons the artificial dendrites corroborate the hypothesized function of the real neuron. Moreover, properties of the artificial dendrites can lead to predictions about yet unmeasured properties. In this way, we investigated wide-field motion integration performed by the VS cells of the fly visual system. In outlining the inverse approach and two applications, we also elaborate on the nature of dendritic function. We furthermore discuss the role of optimality in assigning functions to dendrites and point out interesting future directions.
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