Amit K Mishra

Leendert A Remmelzwaal, Amit K Mishra, George F R Ellis

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.

Leendert A Remmelzwaal, Amit K Mishra, George F R Ellis

Sensory predictions by the brain in all modalities take place as a result of bottom-up and top-down connections both in the neocortex and between the neocortex and the thalamus. The bottom-up connections in the cortex are responsible for learning, pattern recognition, and object classification, and have been widely modelled using artificial neural networks (ANNs). Here, we present a neural network architecture modelled on the top-down corticothalamic connections and the behaviour of the thalamus: a corticothalamic neural network (CTNN), consisting of an auto-encoder connected to a difference engine with a threshold. We demonstrate that the CTNN is input agnostic, multi-modal, robust during partial occlusion of one or more sensory inputs, and has significantly higher processing efficiency than other predictive coding models, proportional to the number of sequentially similar inputs in a sequence. This increased efficiency could be highly significant in more complex implementations of this architecture, where the predictive nature of the cortex will allow most of the incoming data to be discarded.

Leendert A Remmelzwaal, George F R Ellis, Jonathan Tapson et al.

In this paper we introduce a novel Salience Affected Artificial Neural Network (SANN) that models the way neuromodulators such as dopamine and noradrenaline affect neural dynamics in the human brain by being distributed diffusely through neocortical regions, allowing both salience signals to modulate cognition immediately, and one time learning to take place through strengthening entire patterns of activation at one go. We present a model that is capable of one-time salience tagging in a neural network trained to classify objects, and returns a salience response during classification (inference). We explore the effects of salience on learning via its effect on the activation functions of each node, as well as on the strength of weights between nodes in the network. We demonstrate that salience tagging can improve classification confidence for both the individual image as well as the class of images it belongs to. We also show that the computation impact of producing a salience response is minimal. This research serves as a proof of concept, and could be the first step towards introducing salience tagging into Deep Learning Networks and robotics.