Samantha V. Adams

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Self-organizing artificial neural networks are a popular tool for studying visual system development, in particular the cortical feature maps present in real systems that represent properties such as ocular dominance (OD), orientation-selectivity (OR) and direction selectivity (DS). They are also potentially useful in artificial systems, for example(More)
The mammalian visual system has been extensively studied since Hubel and Wiesel's work on cortical feature maps in the 1960s. Feature maps representing the cortical neurons' ocular dominance, orientation and direction preferences have been well explored experimentally and computationally. The predominant view has been that direction selectivity (DS) in(More)
We study a neural model of arachnid prey orientation sensing with a view to potentially using the model in Robotics. The model has been implemented using the Brian spiking neural simulator and incorporates a physics simulation of the arachnid with a simple motor model that translates sensory signals from the neural model into movement to orient towards the(More)
How exactly our brain works is still an open question, but one thing seems to be clear: biological neural systems are computationally powerful, robust and noisy. Using the Reservoir Computing paradigm based on Spiking Neural Networks, also known as Liquid State Machines, we present results from a novel approach where diverse and noisy parallel reservoirs,(More)
This work investigates self-organising cortical feature maps (SOFMs) based upon the Kohonen Self-Organising Map (SOM) but implemented with spiking neural networks. In future work, the feature maps are intended as the basis for a sensorimotor controller for an autonomous humanoid robot. Traditional SOM methods require some modifications to be useful for(More)
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