In order to process the information in a human way (natural way), the foundation of the Artificial General Intelligent software architecture is as close as possible to how the biological neuron is processing information.

The human brain is one of the most complex organ. The cerebral cortex contains something like 10 to 20 billion neurons and the cerebellum between 55 to 70 billion neurons. A neuron is connected by synapses to several thousand other neurons so that they are able to communicate with one another and form a network.

The cerebral cortex has several different “zones” which are involved in different cognitive and behavioral functions such as memory, cognition or thoughts.

Those functions communicate together so that the brain can process stimuli received by sensory receptors, extract information about the structure of the environment and process information with a set of rules, being able when necessary, to adapt response patterns.

We started from there and designed our Smart Neuron Component Architecture to be able to support core cognitive or psychological processes.

Smart Neurons have three functional parts: the first is dedicated to process incoming data (Dendritic Processing Level), the second to execute specific rules (Soma Processing Level) or cognitive processes and the third (Synaptic Processing Level) is in charge of sending the processed information to the receiving Smart Neurons.

We added at the dendritic and synaptic processing level an analogical data processing unit so that these communication channels are processing data like humans are processing those transmissions (based on fuzzy logic and a specific algorithm handling words).

The heart of the Smart Neuron component (Soma Processing Level) is made of different functional parts dedicated to support the execution of one function and has a direct access to the long-term memory (database, here the VirtualBrain for needs). This complex internal rule engine can be compared to a problem space where you typically find many possible impasses or paths and where cognitive processing can be used.

One Smart Neuron can receive inputs from many others Smart Neurons and can sent data to many others Smart Neurons to form a functional network dedicated to process a wider cognitive or psychological feature. Based on the context, those connections between Smart Neurons can be reinforced or settled.

We have specified the Smart Neuron component in a detailed documentation explaining how to implement it within a software and the product database.

Design an Artificial General Intelligent engine using the Smart Neuron Component Architecture will have as a result to process information in a natural way, be closer to how humans are processing information and will also make it possible to implement psychological features. Our Artificial General Intelligent engines are all using SmartNeurons.