Reverse Engineering the Human Mind

With the recent advent of ChatGPT and the mind-blowing mathematical lognormal rule discovered that governs the very distrubution of neurons in our brains, it is clear that an entirely new behemoth of an industry is emerging in the field of AI and cybernetics.

A technical paper titled “A Review of Graphene-Based Memristive Neuromorphic Devices and Circuits”, published by researchers at James Cook University (Australia) and York University (Canada) descibes the next wave of analog/digital neural networks as “a detailed synthesis of the devices utilizing graphene-based memristors is provided to implement the basic building blocks of neuromorphic architectures, that is, synapses, and neurons. This is followed by reviewing studies building graphene memristive spiking neural networks (SNNs).”

Memristor-based analogue computing, like complementary metal-oxide-semiconductor (CMOS) circuits are being utilized for for things like brain-inspired sound localization, where analog components will send specific voltages to amplify or switch electronic signals.

Los Alamos National Laboratory researchers have successfully built a new interface-type memristive device, which can be used to build artificial synapses for next-generation neuromorphic computing. Memristive devices, or memristors, represent long-sought circuit technology that, unlike current resistor technology, has both programming and memory capabilities — memristors could remember which electrical state they were in when powered off, a human brain-like ability that opens up new possibilities for computing and devices.

Their website explains that “Data processing is an essential part of today’s science, with machine learning, artificial intelligence and artificial neural networks used to address pressing questions in everything from climate science to national security applications,” said Aiping Chen, Laboratory scientist with the Center for Integrated Nanotechnologies. “But conventional computing architecture demands a great deal of energy and is increasingly less able to scale up to meet bigger and bigger data challenges. Neuromorphic computing, which mimics the unmatched data storage and processing architecture and capabilities of the human brain, offers a path to continue to extend computing performance.”