‘Why are there always three?’: The Gothic Occult in Dario Argento’s Three Mothers Trilogy

Thomas De Quincey’s essay ‘Levana and Our Ladies of Sorrow’ provided Dario Argento with the spark of an idea, which was further ignited by tales from his then wife, Daria Nicolodi, who told him of her grandmother’s stay at a music school which was run by a coven of witches. From these sources Argento came up with the mythology of The Three Mothers, which were to feature in three of his films: Suspiria (1977), Inferno (1980) and La terza madre/The Mother of Tears (2007). This article will examine the occult and esoteric sources of The Three Mothers trilogy, and explore how these references work to create a series of films that may superficially appear to use the supernatural and occult to create scares, but actually incorporate elements of Western Esotericism rather than traditional Christian images of evil. By doing this, these films transcend their apparent flaws (in terms of shallow plot and character development, a common complaint directed toward many Italian horror films) and instead imbue the mise-enscene itself with meaning, character and narrative. Although the films are situated within the Gothic genre, and in many respects follow traditional Gothic lines with witchcraft and the occult becoming synonymous with evil, I will argue that the films actually belong to the long tradition of art forms that have attempted to investigate the allure and the danger of occult exploration.

A Spiking Neural Network Model of the Medial Superior Olive using Spike Timing Dependent Plasticity for Sound Localisation

Sound localization can be defined as the ability to identify the position of an input sound source and is considered a powerful aspect of mammalian perception. For low frequency sounds, i.e., in the range 270 Hz-1.5 KHz, the mammalian auditory pathway achieves this by extracting the Interaural Time Difference between sound signals being received by the left and right ear. This processing is performed in a region of the brain known as the Medial Superior Olive (MSO). This paper presents a Spiking Neural Network (SNN) based model of the MSO. The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer Function data in an adult domestic cat. The results presented demonstrate how the proposed SNN model is able to perform sound localization with an accuracy of 91.82% when an error tolerance of +/-10 degrees is used. For angular resolutions down to 2.5 degrees , it will be demonstrated how software based simulations of the model incur significant computation times. The paper thus also addresses preliminary implementation on a Field Programmable Gate Array based hardware platform to accelerate system performance.