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Multilayer processing of spatiotemporal spike patterns in a neuron with active dendrites.

Yingxue Wang¹, Shih-Chii Liu

¹Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zürich, Switzerland. yingxue@ini.phys.ethz.ch

Neural Computation

|March 27, 2010

Summary

Active dendrites process spatiotemporal input patterns. A new model shows dendritic compartment responses depend on input synchrony and spatial clustering, suggesting a multilayer network model for neurons with active dendrites.

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Area of Science:

Neuroscience
Computational Neuroscience
Artificial Intelligence

Background:

Recent evidence highlights the active role of dendrites in neural signal processing.
Existing models adequately describe spatial input integration but lack abstractions for temporal processing on dendrites.

Purpose of the Study:

To develop a computational abstraction for how neurons with active dendrites process spatiotemporal input patterns.
To investigate the role of dendritic regenerative mechanisms in neural computation.

Main Methods:

Developed a real-time analog very-large-scale-integrated (aVLSI) dendritic compartmental model.
Incorporated voltage-gated ion channels and transmitter-gated NMDA channels as regenerative event mechanisms.
Analyzed the response of dendritic compartments to varying input synchrony and spatial clustering.

Main Results:

Dendritic compartment response is a nonlinear sigmoidal function of input temporal synchrony and spatial clustering.
Identified key mechanisms underlying dendritic computation.

Conclusions:

A neuron with active dendrites can be modeled as a multilayer network.
This network model selectively amplifies responses to relevant spatiotemporal input spike patterns.
Provides a novel framework for understanding neural computation in active dendrites.