University of Hertfordshire

From the same journal

An inverse approach for elucidating dendritic function

Research output: Contribution to journalArticlepeer-review

Standard

An inverse approach for elucidating dendritic function. / Torben-Nielsen, Ben; Stiefel, Klaus M.

In: Frontiers in Computational Neuroscience, Vol. 4, 128, 2010.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Torben-Nielsen, Ben ; Stiefel, Klaus M. / An inverse approach for elucidating dendritic function. In: Frontiers in Computational Neuroscience. 2010 ; Vol. 4.

Bibtex

@article{b417c318b3c14e049e0eaa96603888c5,
title = "An inverse approach for elucidating dendritic function",
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",
author = "Ben Torben-Nielsen and Stiefel, {Klaus M.}",
note = "{\textcopyright} 2010 Torben-Nielsen and Stiefel. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited",
year = "2010",
doi = "10.3389/fncom.2010.00128",
language = "English",
volume = "4",
journal = "Frontiers in Computational Neuroscience",
issn = "1662-5188",
publisher = "Frontiers Media S.A.",

}

RIS

TY - JOUR

T1 - An inverse approach for elucidating dendritic function

AU - Torben-Nielsen, Ben

AU - Stiefel, Klaus M.

N1 - © 2010 Torben-Nielsen and Stiefel. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited

PY - 2010

Y1 - 2010

N2 - 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

AB - 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

U2 - 10.3389/fncom.2010.00128

DO - 10.3389/fncom.2010.00128

M3 - Article

C2 - 21258425

VL - 4

JO - Frontiers in Computational Neuroscience

JF - Frontiers in Computational Neuroscience

SN - 1662-5188

M1 - 128

ER -