An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites

In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based "technology" that underlies the brain's remarkable ability to process and store information. The basic building bloc...

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Bibliographic Details
Published in:Proceedings of the IEEE 2014-05, Vol.102 (5), p.782-798
Main Authors: Jadi, Monika P., Behabadi, Bardia F., Poleg-Polsky, Alon, Schiller, Jackie, Mel, Bartlett W.
Format: Article
Language:English
Subjects:
Biological system modeling
Brain
Brain modeling
Computational modeling
Context awareness
Contextual modulation
dendrites
dendritic spike
Dendritic structure
Mathematical analysis
Mathematical model
Mathematical models
multilayer network
multiplicative interaction
Neural networks
Neurons
Neuroscience
Nonlinearity
Predictive models
single-neuron model
Stores
synaptic integration
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Summary:In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based "technology" that underlies the brain's remarkable ability to process and store information. The basic building block of the nervous system is the nerve cell, or "neuron," yet after more than 100 years of neurophysiological study and 60 years of modeling, the information processing functions of individual neurons, and the parameters that allow them to engage in so many different types of computation (sensory, motor, mnemonic, executive, etc.) remain poorly understood. In this paper, we review both historical and recent findings that have led to our current understanding of the analog spatial processing capabilities of dendrites, the major input structures of neurons, with a focus on the principal cell type of the neocortex and hippocampus, the pyramidal neuron (PN). We encapsulate our current understanding of PN dendritic integration in an abstract layered model whose spatially sensitive branch-subunits compute multidimensional sigmoidal functions. Unlike the 1-D sigmoids found in conventional neural network models, multidimensional sigmoids allow the cell to implement a rich spectrum of nonlinear modulation effects directly within their dendritic trees.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2014.2312671