Craig M. Vineyard

Learn More
Encoding sensor observations across time is a critical component in the ability to model cognitive processes. All biological cognitive systems receive sensory stimuli as continuous streams of observed data over time. Therefore, the perceptual grounding of all biological cognitive processing is in temporal semantic encodings, where the particular grounding(More)
We propose a neurologically plausible computational architecture to model human episodic memory and recall based on cortical-hippocampal structure and function. The model design is inspired by neuroscience findings and categorical neural semantic theory.  Fuzzy Adaptive Resonance Theory (ART) and temporal integration are used to form episodic(More)
The field of machine learning strives to develop algorithms that, through learning, lead to generalization; that is, the ability of a machine to perform a task that it was not explicitly trained for. Numerous approaches have been developed ranging from neural network models striving to replicate neurophysiology to more abstract mathematical manipulations(More)
Considerable effort is currently being spent designing neuromorphic hardware for addressing challenging problems in a variety of pattern-matching applications. These neuromorphic systems offer low power architectures with intrinsically parallel and simple spiking neuron processing elements. Unfortunately, these new hardware architectures have been largely(More)
Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks,(More)
The field of machine learning strives to develop algorithms that, through learning, lead to generalization; that is, the ability of a machine to perform a task that it was not explicitly trained for. An added challenge arises when the problem domain is dynamic or non-stationary with the data distributions or categorizations changing over time. This(More)
Hippocampus within medial temporal lobe of the brain is essentially involved in episodic memory formation. Rather than simply being a mechanism of storing information, episodic memory associates information such as the spatial and temporal context of an event. Using hippocampus neurophysiology and functionality as an inspiration, we have developed an(More)
Amidst the rising impact of machine learning and the popularity of deep neural networks, learning theory is not a solved problem. With the emergence of neuromorphic computing as a means of addressing the von Neumann bottleneck, it is not simply a matter of employing existing algorithms on new hardware technology, but rather richer theory is needed to guide(More)