NeuraBASE™ is a patented technology that enables machines to learn a task using defined sensor and motor neurons.

Developed based on a novel concept for the construction of adaptive neural networks, its unique characteristics enable the creation of neuronal networks that are larger and faster than conventional neural networks.

The massive global efforts in advancing neuroscience are underlined by the belief that the human brain stores and recalls meaningful associations of events (memories). When the structure and associations of these events are understood, they can be replicated to enable the development of intelligent systems and devices.

NeuraBASE™ implements a model based on an alternative theory of how the brain works. To exhibit true artificial general intelligence (AGI), a system must be able to adapt to continuous learning from new inputs, and to recall what it has learnt previously.

NeuraBASE™ can be used to rapidly build neuronal network systems from 4 billion to 280 trillion neurons, which can subsequently be analysed at high speeds. This makes the mining or processing of very large volumes of data extremely fast and accurate. It presents unique opportunities for the use of NeuraBASE™ in the development of new technologies in numerous domains such as high-throughput language translation, bioinformatics, robotic movement, robotic speech, semantic analysis, and reinforcement learning, among others.