NEUROMORPHIC COMPUTING
Source: Hindu
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Context
Scientists at the IISc, Bengaluru, are reporting a momentous breakthrough in neuromorphic, or brain-inspired, computing technology that could potentially allow India to play in the global AI race.
About Neuromorphic computing
Aspect | Details |
Definition |
●Neuromorphic computing is also known as neuromorphic engineering. ●It is a branch of computing that designs hardware and software to mimic the neural structures and functions of the human brain. |
Origins |
●Emerged in the 1980s when Misha Mahowald and Carver Mead pioneered the development of silicon neurons, synapses, and retinas. |
Key Components |
●Neurons: Store and process data (similar to biological neurons). ●Synapses: Form connections between neurons, adjust their weights over time. ●Spiking Neural Networks (SNNs): Event-driven neural networks based on neuron spikes. |
Algorithms |
●Deep Learning: Converts deep neural networks to spiking neural networks. ●Evolutionary Algorithms: Bio-inspired methods for optimizing neural networks. ●Plasticity: Mimicking neural plasticity by adjusting synaptic weights. |
Benefits |
●Adaptability: Real-time learning and problem-solving. ●Energy Efficiency: Only active neurons consume energy, making it highly efficient. ●High Performance: Parallel processing and low latency in computations. |
Challenges |
●Decreased Accuracy: Loss of precision when converting from deep neural networks. ●Lack of Standards: Limited benchmarks for evaluating performance. ●Complexity: Requires interdisciplinary knowledge and a steep learning curve. |
Uses |
●Autonomous Vehicles: Faster processing for navigation and collision avoidance. ●Cybersecurity: Detects patterns indicating cyberattacks. ●Edge AI: Supports low-power devices like IoT sensors and smartphones. ●Robotics: Improves real-time decision-making in robots. |
Terminology
Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
READ ABOUT IoT: https://www.iasgyan.in/daily-current-affairs/internet-of-things-iot#:~:text=The%20internet%20of%20things%2C%20or,human%2Dto%2Dcomputer%20interaction.
- Neural plasticity is defined as the ability of the nervous system to change its activity in response to intrinsic or extrinsic stimuli by reorganizing its structure, functions, or connections after injuries.
- Latency is a time delay between the cause and the effect of some physical change in the system being observed.
Neurons
Category |
Description |
Definition |
●Neurons are specialized cells of the nervous system responsible for transmitting electrical and chemical signals. |
Basic Structure |
●Cell body (Soma): Contains the nucleus. ●Dendrites: Receive signals. ●Axon: Sends signals away. |
Types of Neurons |
●Sensory Neurons: Carry signals from sensory organs to Central Nervous System (CNS). ●Motor Neurons: Send signals from CNS to muscles. ●Interneurons: Connect neurons within CNS. |
Synapse |
●Junction between two neurons where neurotransmitters are released to transmit signals. |
Function |
●Process and transmit information through electrical impulses (action potentials). |
Read about Artificial Intelligence:
Sources:
PRACTICE QUESTION Q:Consider the following statements about Neurons: 1. Node of Ranvier is a Fatty layer that insulates axons and speeds up signal transmission. 2. Myelin Sheath is a gap where action potentials are regenerated for faster signal transmission. Which of the above statements is/are correct? a) 1 only b) 2 only c) Both 1 and 2 d) Neither 1 nor 2 Answer: d Explanation: 1st statement is incorrect: Myelin sheath is a Fatty layer that insulates axons and speeds up signal transmission (produced by Schwann cells or oligodendrocytes). 2nd statement is incorrect: Node of Ranvier is a Gap in the myelin sheath where action potentials are regenerated for faster signal transmission (saltatory conduction). |