Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120138
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Type: Conference paper
Title: Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning
Author: Padilla, D.
McDonnell, M.
Citation: 2016 Intemational Joint Conference on Neural Networks (IJCNN), 2016 / vol.2016-October, pp.1187-1194
Publisher: IEEE
Issue Date: 2016
Series/Report no.: IEEE International Joint Conference on Neural Networks (IJCNN)
ISBN: 9781509006199
ISSN: 2161-4393
2161-4407
Conference Name: International Joint Conference on Neural Networks (IJCNN) (24 Jul 2016 - 29 Jul 2016 : Vancouver, Canada)
Statement of
Responsibility: 
Daniel E. Padilla and Mark D. McDonnell
Abstract: Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despite major advances in neuroscience over the last few decades, an understanding of the brain's underlying mechanisms for pattern recognition remains unattained. Efforts to replicate such high-level brain functions on the basis of the limited, low-level known details of the brain have naturally led to critical assumptions that make brain-inspired machine learning possible. Convolutional neural networks are an example of such architectures, shown to produce state-of-the-art classification performance on practical applications. The Hierarchical Temporal Memory (HTM) model, on the other hand, performs pattern and sequence recognition on the basis of highly biologically plausible structure and operation. In this work we build on the strengths of convolutional neural networks by integrating them into the HTM framework. An analysis of the common and complementary features between the two models results in the proposal of an innovative, hybrid machine learning architecture. Practical tests on a handwritten digit recognition task reveal a 2% fall in pattern recognition performance, compared to that of the original convolutional neural network design. Nevertheless, key HTM features embedded in the novel architecture enable its potential future enhancement with sequence learning and prediction, an inexistent capability in traditional convolutional neural networks.
Rights: © 2016 IEEE
RMID: 0030062520
DOI: 10.1109/IJCNN.2016.7727332
Grant ID: http://purl.org/au-research/grants/arc/DP1093425
Appears in Collections:Electrical and Electronic Engineering publications

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