Congratulations to Nooraini Yusoff for successfully passing her MPhil to PhD transfer

Friday 6 August 2010

Biologically inspired computing studies the properties and mechanisms of information processing in nature and embeds this knowledge into artificial systems. Due to its adaptability to wider range of applications, neural network has been of interest in many research areas. Furthermore, the growing evidences from the neuroscience field have led to evolutions of artificial neural network (ANN). From the simple McCulloch-Pitts models, ANN has now in its third generation with spiking neuron network (SNN) models. SNN based model provides more meaningful interpretation of biological neural system. However, information encoding is a major challenge as the trade off for its realism. SNN is complex and dynamic depending on the choice of spiking model, network topology, and spike coding. Hence, there are a number of SNN models emerged today with variety of computational complexity and plausibility levels. On the other hand, this offers a new goal to SNN field that is to have a network with computationally efficient and biologically plausible. Therefore, for this study we propose a simple framework of information processing based on SNN to accurately predict human cognitive behaviour more closely. For our experiment paradigm, we study the dynamics of brain behaviour in Stroop effect phenomenon. The phenomenon demonstrates dynamics of suppressing an automatic stimulus processing which interferes with a different target task. The dynamics can be observed in terms of interference and facilitation effects that influence target response processing time. For a preliminary attempt understanding the colour-word Stroop stimuli processing, we used Hopfield neural network with varying conditions of pattern recalls. At certain level, our model is able to simulate the Stroop effect in comparison to the human performance. However, for more efficiently modelling the cognitive behaviour, we propose a simple supervised associative learning approach for SNN. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations.