Computational intelligence and neuroscience
Oct 18, 2018
Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studie...
Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual ...
Deep neural networks (DNNs) trained in a supervised way suffer from two known problems. First, the minima of the objective function used in learning correspond to data points (also known as rubbish examples or fooling images) that lack semantic simil...
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large nu...
Neurofeedback requires a direct translation of neuronal brain activity to sensory input given to the user or subject. However, decoding certain states, e.g., mindfulness or wandering thoughts, from ongoing brain activity remains an unresolved problem...
Chunking is the process by which frequently repeated segments of temporal inputs are concatenated into single units that are easy to process. Such a process is fundamental to time-series analysis in biological and artificial information processing sy...
IEEE journal of biomedical and health informatics
Oct 4, 2018
Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, ...
Hidden Markov models (HMMs) underpin the solution to many problems in computational neuroscience. However, it is still unclear how to implement inference of HMMs with a network of neurons in the brain. The existing methods suffer from the problem of ...
Spiking neural P systems, otherwise known as named SN P systems, are bio-inspired parallel and distributed neural-like computing models. Due to the spiking behavior, SN P systems fall into the category of spiking neural networks, and are considered t...
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