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Deep learning is on the rise in the machine learning community, because the traditional shallow learning architectures have proved unfit for the more challenging tasks of machine learning and strong artificial intelligence (AI). The surge in and wide availability of increased computing power, coupled with the creation of efficient training algorithms and advances in neuroscience, have enabled the implementation, hitherto impossible, of deep learning principles. These developments have led to the formation of deep architecture algorithms that look to cognitive neuroscience to suggest biologically inspired learning solutions. This chapter presents the concepts of spiking neural networks (SNNs) and hierarchical temporal memory (HTM), whose associated techniques are the least mature of the techniques covered in this book.
- Deep Learning
- Chapter 9
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