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Related Concept Videos

Integration of Synaptic Events01:28

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Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
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Patterned Photostimulation with Digital Micromirror Devices to Investigate Dendritic Integration Across Branch Points
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Spatially distributed dendritic resonance selectively filters synaptic input.

Jonathan Laudanski1, Benjamin Torben-Nielsen2, Idan Segev3

  • 1Scientific and Clinical Research Department, Neurelec, Vallauris, France; Equipe Audition, Département d'études cognitives, Ecole Normale Supérieure, Paris, France.

Plos Computational Biology
|August 22, 2014
PubMed
Summary
This summary is machine-generated.

Neurons select important signals using dendritic resonance, a property that varies spatially along dendrites. This allows neurons to filter inputs based on location and timing, enhancing neural computation.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Neurons must select relevant synaptic inputs from thousands impinging on their dendritic trees.
  • Synaptic plasticity strengthens relevant synapses, but alternative input selection mechanisms exist.
  • Dendritic resonance is proposed to filter synaptic inputs based on their temporal rates.

Purpose of the Study:

  • To investigate the spatial properties of dendritic resonance.
  • To determine if dendritic resonance is a single frequency or a distributed property.
  • To elucidate the role of dendritic resonance in spatiotemporal input selection.

Main Methods:

  • Mathematical analysis of neuronal models.
  • Numerical simulations of synaptic input integration.
  • Analysis of resonance frequency variations along dendritic branches.

Main Results:

  • Dendritic resonance is an inherently spatially distributed property.
  • Resonance frequency varies systematically along the dendrites.
  • This spatial variation creates a spatiotemporal selection mechanism.

Conclusions:

  • Neurons possess a sophisticated input selection mechanism based on dendritic resonance.
  • This mechanism is sensitive to both the location and temporal structure of synaptic inputs.
  • Dendritic resonance provides a powerful filter for processing information in neural circuits.