The Clusteron: Toward a Simple Abstraction for a Complex Neuron
- Creators
- Mel, Bartlett W.
Abstract
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.
Additional Information
© 1992 Morgan Kaufmann. This work was supported by the Office of Naval Research, the James McDonnell Foundation, and National Institute of Mental Health. Thanks to Christof Koch for providing an excellent working environment, Ken Miller for helpful discussions, and to Rodney Douglas for discussions and use of his neurons.Attached Files
Published - 450-the-clusteron-toward-a-simple-abstraction-for-a-complex-neuron.pdf
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Additional details
- Eprint ID
- 63785
- Resolver ID
- CaltechAUTHORS:20160119-164727090
- Office of Naval Research (ONR)
- James McDonnell Foundation
- National Institute of Mental Health (NIMH)
- Created
-
2016-01-20Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Series Name
- Advances in Neural Information Processing Systems
- Series Volume or Issue Number
- 4