Charting Cartographies of Resistance: Line of Flight in Women Artists’ Narratives

In this paper I chart lines of flight in women artist’s narratives. In focusing on the complex interrelations between the social milieus of education and art, what I suggest is that they should be analysed as an assemblage where power relations and forces of desire are constantly at play in creating conditions of possibility for women to resist, imagine themselves becoming other and for new possibilities in their lives to be actualised. As a novel approach to social ontology the theory of assemblages offers an analytics of social complexity that accounts for open configurations, continuous connections and unstable hierarchies, structures and axes of difference. In reconsidering resistance as immanent in dispositifs of power and assemblages of desire, what I finally argue is that women artists’ narratives contribute to the constitution of minor knowledges and create archives of radical futurity.

Spiking neural network model of sound localisation using the interaural intensity difference

In this paper, a spiking neural network (SNN) architecture to simulate the sound localization ability of the mammalian auditory pathways using the interaural intensity difference cue is presented. The lateral superior olive was the inspiration for the architecture, which required the integration of an auditory periphery (cochlea) model and a model of the medial nucleus of the trapezoid body. The SNN uses leaky integrateand-fire excitatory and inhibitory spiking neurons, facilitating synapses and receptive fields. Experimentally derived headrelated transfer function (HRTF) acoustical data from adult domestic cats were employed to train and validate the localization ability of the architecture, training used the supervised learning algorithm called the remote supervision method to determine the azimuthal angles. The experimental results demonstrate that the architecture performs best when it is localizing high-frequency sound data in agreement with the biology, and also shows a high degree of robustness when the HRTF acoustical data is corrupted by noise.