Neuromorphic Computing

Neuromorphic Computing, also known as neuromorphic engineering is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.

In recent times the term neuromorphic has been used to describe analog, digital, and mixed-mode analog/digital VLSI and software systems that implement models of neural systems (for perception, motor control, or multisensory integration).

Computational Intelligence- Scholarpedia

Neural Networks:
Neuroscience, Computational Neuroscience, Cognitive Science, Models of Neurons, Spiking Networks, Recurrent Neural Networks, Feedforward Networks

Machine learning and pattern recognition:
Supervised learning, Unsupervised learning, Reinforcement learning, Evolutionary,, Computation, Artificial Life

Applications:
Robotics

Theory:
Graph Theory, Complexity, Artificial Intelligence, Algorithmic Information Theory, Information, Theory, Statistics, Fuzzy Systems, Signal Analysis

Whole Brain Emulation- Mind Uploading

Whole brain emulation (WBE) or mind uploading (sometimes called “mind copying” or “mind transfer”) is the hypothetical process of copying mental content (including long-term memory and “self”) from a particular brain substrate and copying it to a computational device, such as a digital, analog, quantum-based or software based artificial neural network.

The computational device could then run a simulation model of the brain information processing, such that it responds in essentially the same way as the original brain (i.e., indistinguishable from the brain for all relevant purposes) and experiences having a conscious mind.

NeuCube- next-generation computer modelled on the human brain

Excerpt

“We have developed a novel computational framework that can create flexible and configurable application systems to run on this super-computer hardware – or on any other neuromorphic chips or systems.”

Principal Investigator and project lead for the NeuCube is Professor Nikola Kasabov, director of Auckland University of Technology’s Knowledge, Engineering and Discovery Research Institute (KENRI).

For the first time the NeuCube framework will be used to combine all types of spatiotemporal brain data (STBD) related to a given problem (e.g. EEG, fMRI, DTI, structural, genetic) in order to model and understand complex spatio-temporal relationships across the data sets. This is the main method on which the EU H2020 proposal submitted in 2014 is based, with the participation of 8 EU partners and KEDRI, KEDRI being the world leader on the topic. The results will be used in other INTELLECTE/2 projects.

Launch at Auckland University Conference

Neurons hold key to future

Kiwi scientists set to unveil next generation of computing power

The NeuCube neurocomputer Professor Kasabov originated draws upon the same information-processing principles in our brains, where information is represented in temporal sequences of electrical signals, or “spikes”.

“If we can use this neurocomputer to capture the patterns ...

Artificial Intelligence Overview

Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is an academic field of study which studies the goal of creating intelligence, whether in emulating human-like intelligence or not. Major AI researchers and textbooks define this field as “the study and design of intelligent agents”, where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.

John McCarthy, who coined the term in 1955, defines it as “the science and engineering of making intelligent machines”

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