Spatio-temporal and multimodal processing in a spiking neural mind of a robot

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multi-modal novelty and familiarity detection

Journal Special Issue on Computational Neuroscience Meeting CNS 2013 Presented is further development of the architecture presented in [1] where a top-down feature-based and spatial attention have been incorporated in a large scale visual module and novelty and familiarity detectors based on the model presented in [2]. These have been developed in the perceptual (visual and auditory) and motor modalities. In addition to the novelty/familiarity detection shown in [2, 3], the architecture is able to partially recognise familiar features in each perceptual modality, and furthermore in a distributed fashion activate associated familiar features from one perceptual modality to another and/or to the motor programmes and affordances. The architecture is implemented on a mobile robot operating in a dynamic environment. The proposed distributed multi-modal familiarity detection integrated in the architecture improves the recognition and action performance in a noisy environment, as well as contributing to the multi-modal association and learning of novel objects and actions

A Neural Network Model of the Impact of Political Instability on Tourism

This paper presents an empirical integration of the dimensions of political instability with traditional exogenous variables, which are usually employed in econometric tourism demand forecasting, within a tourism demand model in order to investigate causal relationships between political instability and tourism. The work uses the POLINST Database, which contains events of political instability from 1977 to 1997 that took place in the Middle East - Mediterranean region. The model is based on a Focused Tapped Delay Line Neural Network (FTDNN) with a sliding time window of 12 months. The evaluation results show that our model can be used to achieve a good estimation of the effects of political instability on tourism. In an extended set of experiments we were able to show the relative importance of the political instability factors on tourism. Finally, our model also allowed to estimated the time lag between a political instability/terrorist event and the reduction of tourist number to the destination

Spike-Timing-dependent Synaptic Plasticity: From Single Spikes to Spike Trains

We present a neurobiologically motivated model of a neuron with active dendrites and dynamic synapses, and a training algorithm which builds upon single spike-timing-dependent synaptic plasticity derived from neurophysiological evidence. We show that in the presence of a moderate level of noise, the plasticity rule can be extended from single to multiple presynaptic spikes and applied to effectively train a neuron in detecting temporal sequences of spike trains. The trained neuron responds reliably under different regimes and types of noise