Brain-inspired Distributed Cognitive Architecture
This research addresses the need for bio-realistic attention and sensory selection in AI systems, representing an incremental step toward more realistic artificial intelligence.
The paper tackles the problem of creating a brain-inspired cognitive architecture for artificial intelligence, demonstrating that it models attention through high- and low-salience modes and introduces processing efficiencies.
In this paper we present a brain-inspired cognitive architecture that incorporates sensory processing, classification, contextual prediction, and emotional tagging. The cognitive architecture is implemented as three modular web-servers, meaning that it can be deployed centrally or across a network for servers. The experiments reveal two distinct operations of behaviour, namely high- and low-salience modes of operations, which closely model attention in the brain. In addition to modelling the cortex, we have demonstrated that a bio-inspired architecture introduced processing efficiencies. The software has been published as an open source platform, and can be easily extended by future research teams. This research lays the foundations for bio-realistic attention direction and sensory selection, and we believe that it is a key step towards achieving a bio-realistic artificial intelligent system.