Garrick Orchard

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Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labeling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based(More)
In limbed animals, spinal neural circuits responsible for controlling muscular activities during walking are called Central Pattern Generators (CPG). CPG networks display oscillatory activities that actuates individual or groups of muscles in a coordinated fashion so that the limbs of the animal are flexed and extended at the appropriate time and with the(More)
Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the(More)
There are various neuron models which can be used to emulate the neural networks responsible for cortical and spinal processes. One example is the Central Pattern Generator (CPG) networks, which are spinal neural circuits responsible for controlling the timing of periodic systems in vertebrates. In order to model the CPG effectively, it is necessary to(More)
This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing(More)
Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to(More)
Neuromorphic Vision sensors have improved greatly since the first silicon retina was presented almost three decades ago. They have recently matured to the point where they are commercially available and can be operated by laymen. However, despite improved availability of sensors, there remains a lack of good datasets, while algorithms for processing(More)
Visual motion estimation is a computationally intensive, but important task for sighted animals. Replicating the robustness and efficiency of biological visual motion estimation in artificial systems would significantly enhance the capabilities of future robotic agents. 25 years ago, in this very journal, Carver Mead outlined his argument for replicating(More)
The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using(More)
Recently there has been an increasing interest in application of bio-mimetic controllers and neuromorphic vision sensors to planetary landing tasks. Within this context, we present combined low-level (SPICE) and high-level (behavioral) simulations of a novel neuromorphic VLSI vision sensor in a realistic planetary landing scenar io. We use results from low(More)