Technology

Neuromorphic engineering (also known as neuromorphic computing ) is a field of technology that designs and builds computing systems inspired by the structure, function, and plasticity of the human brain. It blends elements from neuroscience, computer engineering, and materials science to develop hardware and software that mimic neural systems. 🔍 Core Concepts of Neuromorphic Engineering Brain-Inspired Architecture : Uses spiking neural networks (SNNs) instead of traditional artificial neural networks (ANNs). SNNs process information similarly to biological neurons using discrete spikes of electrical activity. Event-Driven Processing : Unlike standard CPUs or GPUs that operate on a clock cycle, neuromorphic systems are asynchronous and event-driven—meaning they compute only when needed. This enables ultra-low power consumption , ideal for edge computing and mobile devices. Hardware Components : Neuromorphic chips (e.g., IBM’s TrueNorth , Intel’s Loihi ...

🧠 What Is Neuromorphic Engineering? At its core, neuromorphic engineering involves building hardware and software systems that replicate: Neurons (processing units) Synapses (connections that adapt with experience) Neuroplasticity (learning and memory through weight adjustment) Spiking neural activity (communication via discrete electrical pulses) 🔧 Key Components 1. Spiking Neural Networks (SNNs) Unlike traditional artificial neural networks, SNNs use spikes (binary events over time) to transmit information. More biologically plausible and energy-efficient. 2. Neuromorphic Chips Custom hardware that emulates brain-like computing: Chip Developed By Features Loihi Intel On-chip learning, event-driven, energy-efficient TrueNorth IBM 1 million neurons, 256 million synapses SpiNNaker University of Manchester Real-time brain modeling BrainScaleS Heidelberg University Analog/digital hybrid brain simulation 3. Event-Driven Processing Unlike traditional...