Recurrently Connected Silicon Neurons with Active Dendrites for One-Shot Learning

Loading...
Thumbnail Image
Penn collection
Departmental Papers (BE)
Degree type
Discipline
Subject
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Arthur, John V.
Contributor
Abstract

We describe a neuromorphic chip designed to model active dendrites, recurrent connectivity, and plastic synapses to support one-shot learning. Specifically, it is designed to capture neural firing patterns (short-term memory), memorize individual patterns (long-term memory), and retrive them when primed (associative recall). It consists of a recurrently connected population of excitatory pyramidal cells and a recurrently connected population of inhibitory basket cells. In addition to their recurrent connections, the excitatory and inhibitory populations are reciprocally connected. The model is novel in that it utilizes recurrent connections and active dendrites to maintain short-term memories as well as to store long-term memories.

Advisor
Date of presentation
2004-07-25
Conference name
Departmental Papers (BE)
Conference dates
2023-05-16T22:57:31.000
Conference location
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Copyright 2004 IEEE. Reprinted from Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Volume 3, pages 1699-1704. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Recommended citation
Collection