Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Intelligence in Biological Systems
- 2 Principles of Artificial Neural Networks
- 3 Artificial Synapses
- 4 Artificial Neurons
- 5 Examples of Applications in Artificial Neural Networks
- 6 System Design
- 7 Neuromorphic Algorithms
- 8 Lifelong Learning with AI Algorithms and Hardware
- 9 Practice Problems
- Index
- References
8 - Lifelong Learning with AI Algorithms and Hardware
Published online by Cambridge University Press: 01 January 2026
Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Intelligence in Biological Systems
- 2 Principles of Artificial Neural Networks
- 3 Artificial Synapses
- 4 Artificial Neurons
- 5 Examples of Applications in Artificial Neural Networks
- 6 System Design
- 7 Neuromorphic Algorithms
- 8 Lifelong Learning with AI Algorithms and Hardware
- 9 Practice Problems
- Index
- References
Summary
The chapter discusses concepts in plasticity that go beyond memory. Several examples are discussed starting with the complexity of dendritic structure in biological neurons, nonlinear summation of signals from synapses by neurons and the vast range of plasticity that has been discovered in biological brain circuits. Learning and memory are commonly assigned to synapses; however, non-synaptic changes are important to consider for neuromorphic hardware and algorithms. The distinction between bioinspired and bio-realistic designs of hardware for AI is discussed. While synaptic connections can undergo both functional and structural plasticity, emulating such concepts in neuromorphic computing will require adaptive algorithms and semiconductors that can be dynamically reprogrammed. The necessity for close collaboration between neuroscience and neuromorphic engineering community is highlighted. Methods to implement lifelong learning in algorithms and hardware are discussed. Gaps in the field and directions for future research and development are discussed. The prospects for energy-efficient neuromorphic computing with disruptive brain-inspired algorithms and emerging semiconductors are discussed.
Keywords
Information
- Type
- Chapter
- Information
- Introduction to Neuromorphic Computing , pp. 169 - 181Publisher: Cambridge University PressPrint publication year: 2026
References
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