Influence fields: A quantitative framework for representation and analysis of active dendrites

Rahul Kumar Rathour, Rishikesh Narayanan

Research output: Contribution to journalArticlepeer-review

Abstract

Neuronal dendrites express numerous voltage-gated ion channels (VGICs), typically with spatial gradients in their densities and properties. Dendritic VGICs, their gradients, and their plasticity endow neurons with information processing capabilities that are higher than those of neurons with passive dendrites. Despite this, frameworks that incorporate dendritic VGICs and their plasticity into neurophysiological and learning theory models have been far and few. Here, we develop a generalized quantitative framework to analyze the extent of influence of a spatially localized VGIC conductance on different physiological properties along the entire stretch of a neuron. Employing this framework, we show that the extent of influence of a VGIC conductance is largely independent of the conductance magnitude but is heavily dependent on the specific physiological property and background conductances. Morphologically, our analyses demonstrate that the influences of different VGIC conductances located on an oblique dendrite are confined within that oblique dendrite, thus providing further credence to the postulate that dendritic branches act as independent computational units. Furthermore, distinguishing between active and passive propagation of signals within a neuron, we demonstrate that the influence of a VGIC conductance is spatially confined only when propagation is active. Finally, we reconstruct functional gradients from VGIC conductance gradients using influence fields and demonstrate that the cumulative contribution of VGIC conductances in adjacent compartments plays a critical role in determining physiological properties at a given location. We suggest that our framework provides a quantitative basis for unraveling the roles of dendritic VGICs and their plasticity in neural coding, learning, and homeostasis.

Original languageEnglish
Pages (from-to)2313-2334
Number of pages22
JournalJournal of Neurophysiology
Volume107
Issue number9
DOIs
StatePublished - 1 May 2012
Externally publishedYes

Keywords

  • Computational model
  • Intrinsic plasticity
  • Learning theory
  • Voltage-gated ion channels

ASJC Scopus subject areas

  • General Neuroscience
  • Physiology

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