Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100389
Title: Improved margin multi-class classification using dendritic neurons with morphological learning
Authors: Hussain, Shaista
Liu, Shih-Chii
Basu, Arindam
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic circuits
Issue Date: 2014
Source: Hussain, S., Liu, S. C., & Basu, A. (2014). Improved margin multi-class classification using dendritic neurons with morphological learning. 2014 IEEE International Symposium on Circuits and Systems (ISCAS), 2640-2643.
Abstract: We present an architecture of a spike based multiclass classifier using neurons with non-linear dendrites and sparse synaptic connectivity where each synapse takes a binary value. The learning in this model happens not through weight updates but through structural changes, i.e. a change of connectivity between inputs and dendrites. Hence, it is well suited for implementation in neuromorphic systems using address event representation (AER). We present a new learning rule that allows better generalization of the system to noisy testing data making it feasible to transfer learnt weights in software to a hardware device interfacing with noisy spiking sensors. The new rule improves testing accuracy by 7 - 10% compared to earlier versions. We also present preliminary results for multi-class classification on handwritten digits from the MNIST database and show that our system can attain comparable performance (≈ 3% more error) with other reported spike based classifiers while using at least 50% less synaptic resources.
URI: https://hdl.handle.net/10356/100389
http://hdl.handle.net/10220/25710
DOI: http://dx.doi.org/10.1109/ISCAS.2014.6865715
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ISCAS.2014.6865715].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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