head, the head model was constructed. Specifically, the head model employed is a Boundary Element Method (BEM) volume conduction model of the head89. In order to account for the increased noise with increasing distance from the sensors, i.e., towards the center of the head, the Neural Acti...
head, the head model was constructed. Specifically, the head model employed is a Boundary Element Method (BEM) volume conduction model of the head89. In order to account for the increased noise with increasing distance from the sensors, i.e., towards the center of the head, the Neural Acti...
InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand...
While humans follow the sense of what's written and figure out the pronunciation that way, computers generally don't have the power to do that, so they have to use statistical probability techniques (typically Hidden Markov Models) or neural networks (computer programs structured like arrays of ...
Lateral interactions in the field are defined by the convolution of the field output, \(g\left( {u\left( {x,t} \right)} \right)\) (where g is a sigmoid function) with an interaction kernel, \(k\). The interaction kernel describes connection weights as a function of distance in ...
One example of a black-box machine learning model is a simple neural network model with one or two hidden layers. Even though you can write out the equations that link every input in the model to every output, you might not be able to grasp the meaning of the connections simply by ...
trained on large volumes of data. This is done by connecting the proprietary AI technology Deep Tensor(1), which performs machine learning on graph-structured data, with graph-structured knowledge bases called a knowledge graph(2), which brings together expert knowledge such as academic literature....
motor generation decision-making naturalistic behavior attractor neural network multi-area networks neural decoding low-dimensional correlations temporal variability motor cortex Introduction When interacting with a complex environment, animals generate naturalistic behavior in the form of self-initiated action se...
have relied almost exclusively on the use of DSPTs that are executed with the correct ordinal structure as quickly as possible. It is likely that the nature of the DSPTs are simplified in more recent work because the primary focus is on providing important novel insights into the neural underp...
InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand...