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- W2110323906 endingPage "798" @default.
- W2110323906 startingPage "782" @default.
- W2110323906 abstract "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." @default.
- W2110323906 created "2016-06-24" @default.
- W2110323906 creator A5014720622 @default.
- W2110323906 creator A5028361219 @default.
- W2110323906 creator A5059117047 @default.
- W2110323906 creator A5077653455 @default.
- W2110323906 creator A5089495945 @default.
- W2110323906 date "2014-05-01" @default.
- W2110323906 modified "2023-09-29" @default.
- W2110323906 title "An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites" @default.
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