What are the top 10 breakthroughs in neuromorphic computing?

1. Memristors: Development of memristors, a fourth fundamental circuit element, has been one of the major breakthroughs in neuromorphic computing. Memristors can store and process information similar to synapses in the human brain.

2. Spiking Neural Networks (SNN): SNNs are biologically inspired neural network architectures that utilize asynchronous and event-driven processing. Implementing SNNs in neuromorphic hardware has shown promise in achieving low power consumption and high processing efficiency.

3. TrueNorth: TrueNorth is a neuromorphic chip developed by IBM that consists of 1 million neurons and 256 million synapses. It is known for its low power consumption and ability to simulate brain-like processing.

4. SpiNNaker: SpiNNaker is a massively parallel computer system designed to model large-scale brain simulations in real time. It aims to mimic the biological structure and function of the human brain, enabling breakthroughs in understanding neural processing.

5. Brain-inspired Algorithms: Developing algorithms that mimic the information processing of the human brain has been an important breakthrough in neuromorphic computing. These algorithms enable efficient and intelligent processing with neuromorphic hardware.

6. Neuromorphic Vision Sensors: Neuromorphic vision sensors are devices that replicate the functioning of the human retina. These sensors provide event-driven, low-power vision processing, making them ideal for applications such as robotics and autonomous vehicles.

7. NeuroSynaptic Cores: IBM’s NeuroSynaptic co-processors are designed to mimic the architecture and function of the human brain in hardware. These cores enable the integration of multiple neural networks and efficient processing with low power requirements.

8. Neuromorphic Learning: Advances in neuromorphic learning algorithms have revolutionized the field of artificial intelligence. These algorithms can adapt and learn from incomplete or noisy data, providing more robust and efficient learning capabilities.

9. Plasticity and Learning Rules: Incorporating plasticity and learning rules inspired by the brain has been a significant breakthrough. Adaptive and self-learning capabilities enable neuromorphic systems to continually improve their performance through experience.

10. Brain-Machine Interfaces: Developing interfaces that connect the human brain with machines has opened up new possibilities in fields like neuroprosthetics and brain-computer interfaces. These breakthroughs allow for the direct integration of neuromorphic computing systems with the human brain.