- Neuromorphic Computing - Home
- Neuromorphic Computing - Introduction
- Neuromorphic Computing - Difference From Traditional Computing
- Neuromorphic Computing - History and Evolution
- Neuromorphic Computing - Types of Technologies
- Neuromorphic Computing - Architecture
- Neuromorphic Computing - Memristors
- Neuromorphic Computing - Synaptic Devices
- Neuromorphic Computing - Hardware Accelerators
- Neuromorphic Computing - Neuromorphic Chips
- Neuromorphic Computing - Analog Circuits
- Neuromorphic Algorithms and Programming
- Neuromorphic Computing - Spiking Neural Networks (SNNs)
- Neuromorphic Computing - Algorithms for SNNs
- Neuromorphic Computing - Programming Paradigms
- Applications of Neuromorphic Computing
- Neuromorphic Computing - Edge Computing
- Neuromorphic Computing - IoT
- Neuromorphic Computing - Robotics
- Neuromorphic Computing - Autonomous Systems
- Neuromorphic Computing - AI and ML
- Neuromorphic Computing - Cognitive Computing
- Neuromorphic Computing Resources
- Neuromorphic Computing - Useful Resources
- Neuromorphic Computing - Discussion
Neuromorphic Computing - History and Evolution
Neuromorphic computing is not a concept that originated in the 21st century, it has been a topic of discussion since the 1950s and has evolved significantly over time. A major breakthrough occurred in the 1980s when biologists gained a clearer understanding of how the human brain functions. In this section we will explore the historical advancements and future scope of neuromorphic computers.
Early Concepts and Origins
- Early 1930s: The mathematician and computer scientist Alan Turning derived a proof that computers can do mathematical calculations as humans if provided in the form of algorithms.
- 1948 - 1950: The Canadian psychologist Donald Hebbs made a breakthrough in neuroscience when he theorized a correlation between synaptic plasticity and learning. Following this Alan Turing developed a cognitive modelling machine based on human neurons.
- 1958: The U.S. Navy then created a perceptron for image recognition, but at this time there was limited knowledge about how brains work hence it failed to deliver.
- 1980s: The modern era of neuromorphic computing began in the 1980s when Carver Mead, a professor at the California Institute of Technology, introduced the term 'neuromorphic engineering.' Mead created an analog circuits that mimicked the structure and functions of biological neural systems.
Mead's efforts were groundwork for future neuromorphic research, this helped in designing systems that emulate the behavior of neurons and synapses. The early neuromorphic systems were mainly experimental, focused on understanding how the brain processes information rather than practical computing applications.
Advancements in the 1990s and 2000s
During the 1990s and early 2000s, neuromorphic computing research expanded with the development of specialized hardware called spiking neural networks (SNNs). These systems were capable of event-driven processing, similar to how biological neurons only "fire" when certain conditions are met.
This period also saw advancements in machine learning and artificial intelligence, further increasing interest in brain-inspired computing models. However, neuromorphic computing remained largely experimental, with its primary applications in academic research and areas like robotics.
Modern Era and Breakthroughs
In the early 2010s, more advanced neuromorphic chips such as IBM's TrueNorth and Intel's Loihi were developed and made significant turning point in development neuromorphic computers. These chips were designed to process information in parallel, mimic brain-like processing, and achieve remarkable energy efficiency.
Neuromorphic hardware became more practical for real-world applications, including autonomous systems, real-time sensory processing, and edge computing..
Future Outlook
The evolution of neuromorphic computing is far from complete. As research continues and the demand for more efficient and intelligent computing grows, neuromorphic systems are expected to play an increasingly important role in fields like artificial intelligence, robotics, and beyond.
Neuromorphic computers offers huge possibilities for energy-efficient, brain-like processing, but it is not yet practical for most AI and machine learning tasks, such as natural language processing (NLP), large-scale supervised learning, or reinforcement learning which require highly scalable and general-purpose hardware.