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Neuromorphic engineering

1. Neuromorphic Engineering Overview: – Inspired by the brain’s structure and operations. – Aims to replicate biological computation using small computing elements. – Examples include […]

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1. Neuromorphic Engineering Overview:
– Inspired by the brain’s structure and operations.
– Aims to replicate biological computation using small computing elements.
– Examples include field programmable neural arrays and brain-mimicking computer chips.
– Research projects like the Human Brain Project and the BRAIN Initiative drive advancements.
– Advancements include nanocrystals, memristive devices, and brain-inspired artificial synapses.

2. Neuromorphic Systems and Technologies:
– Focus on memristors for neuroplasticity and high-level pattern recognition.
– Development of neuromorphic sensors like event cameras and spiking neurons.
– Advancements in neuromorphic hardware for quantum architectures and self-learning capabilities.
– Emerging technologies include photonic quantum memristors and Loihi manycore processor.

3. Military Applications of Neuromorphic Technology:
– U.S. military’s Joint AI Center focuses on AI and neuromorphic hardware.
– Applications in smart headsets, goggles, and combat robots.
– Connects sensors and shooters for enhanced military operations.
– Aims to deploy neuromorphic-enabled units for various military applications.

4. Ethical, Legal, and Social Considerations:
– Raises ethical questions due to brain mimicry and public perception concerns.
– Social attitudes towards robots and concerns about replicating human functions.
– Legal debates on property rights and artificial intelligence ownership.
– Advocacy for granting personhood rights to advanced neuromorphic systems.

5. Advancements in Neuromorphic Computing:
– Development of large-scale neuromorphic computing systems.
– Advancements in spiking neural networks, neural modulation, and memristive circuits.
– Utilization of spin devices, neuristors, and phase transitions for advanced computing.
– Exploration of emerging technologies like quantum computing, memristive circuits, and artificial neurons.

Neuromorphic engineering (Wikipedia)

Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, transistors, among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.

A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.

Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems. One of the first applications for neuromorphic engineering was proposed by Carver Mead in the late 1980s.

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