Are single neocortical neurons as powerful as multi-layered networks? A recent compartmental modeling study has shown that voltage-dependent membrane nonlinearities present in a complex dendritic tree can provide a virtual layer of local nonlinear processing elements between synaptic inputs and the final output at the cell body, analogous to a hidden layer in a multi-layer network. In this paper, an abstract model neuron is introduced, called a clusteron, which incorporates aspects of the dendritic "cluster-sensitivity" phenomenon seen in these detailed biophysical modeling studies. It is shown, using a clusteron, that a Hebb-type learning rule can be used to extract higher-order statistics from a set of training patterns, by manipulating the spatial ordering of synaptic connections onto the dendritic tree. The potential neurobiological relevance of these higher-order statistics for nonlinear pattern discrimination is then studied within a full compartmental model of a neocortical pyramidal cell, using a training set of 1000 high-dimensional sparse random patterns.
[1]
Bartlett W. Mel.
NMDA-Based Pattern Discrimination in a Modeled Cortical Neuron
,
1992,
Neural Computation.
[2]
Bartlett W. Mel,et al.
Sigma-Pi Learning: On Radial Basis Functions and Cortical Associative Learning
,
1989,
NIPS.
[3]
Jerome A. Feldman,et al.
Connectionist Models and Their Properties
,
1982,
Cogn. Sci..
[4]
Anthony M. Zador,et al.
Self-organization of Hebbian Synapses in Hippocampal Neurons
,
1990,
NIPS.
[5]
D. Whitteridge,et al.
An intracellular analysis of the visual responses of neurones in cat visual cortex.
,
1991,
The Journal of physiology.
[6]
M Hines,et al.
A program for simulation of nerve equations with branching geometries.
,
1989,
International journal of bio-medical computing.
[7]
David E. Rumelhart,et al.
Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation Networks
,
1989,
Neural Computation.