Indian Institute of Science scientist reported breakthrough in neuromorphic computing

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They developed Memristor semiconductor devices using metal-organic films instead of silicon-based technology.

This material enables the Memristor to mimic the way the biological brain processes information using networks of neurons and synapses.

What is Neuromorphic computing?

Neuromorphic computing, or neuromorphic engineering, mimics the human brain's structure and function.
It involves designing hardware and software that simulate neural networks and synapses to process information.

It mimics biological brains using hardware like spiking neural networks (SNNs).
SNNs consist of nodes (spiking neurons) connected by artificial synapses, which use analog circuitry to transfer signals.
- This approach encodes data through discrete analog signal changes rather than binary systems used in standard computers.

Adaptability: To new stimuli, excelling at solving novel problems in real-time.
Event-driven computation: Ensuring only active parts consume energy, resulting in efficient power usage.
High performance: As it integrates memory and processing in neurons, reducing latency.
Parallel processing: Of multiple tasks across different neurons for faster operation.
Challenges of neuromorphic computing: Lack of benchmarks and standards; Limited accessibility and software; Decreased accuracy, etc.

Neurons are the fundamental units of the brain and nervous system.
As messengers, these neurons relay information between different areas of the brain and to other parts of the body.
When a neuron becomes active or “spikes,” it triggers the release of chemical and electrical signals.
These signals travel via a network of connection points called synapses, allowing neurons to communicate with each other.