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.