Recent advances in neuroscience have revealed many principles about neural processing. In particular, many biological systems were found to reconfigure/recruit single neurons to generate multiple kinds of decisions. Such findings have the potential to advance our understanding of the design and optimization process of artificial neural networks. Previous work demonstrated that dense neural networks are needed to shape complex decision surfaces required for AI-level recognition tasks. We investigate the ability to model high dimensional recognition problems using single or several neurons networks that are relatively easier to train. By employing three datasets, we test the use of a population of single neuron networks in performing multi-class recognition tasks. Surprisingly, we find that sparse networks can be as efficient as dense networks in both binary and multi-class tasks. Moreover, single neuron networks demonstrate superior performance in binary classification scheme and competing results when combined for multi-class recognition.
Swallowing dysfunction, or dysphagia, occurs secondary to many underlying etiologies such as stroke and can lead to pneumonia. The upper esophageal sphincter (UES) is a major anatomical landmark that allows the passage of swallowed materials into the esophagus during swallowing. Delayed UES opening or reduced duration of opening can lead to the accumulation of pharyngeal residue, which can increase risk of aspiration. UES opening is observed through the inspection of radiographic exams, known as videofluoroscopy swallow studies (VFSSs), which expose patients to ionizing radiation and depend on subjective clinician interpretations. High resolution cervical auscultation (HRCA) is a non-invasive sensor-based technology that has been recently investigated to depict swallowing physiology. HRCA has been proposed for detecting UES opening duration through a deep learning framework. However, the proposed framework was only validated over swallows from patients. For such an algorithm to be robust, it has to be proven equally reliable for the detection of UES opening duration in swallows from both patients and healthy subjects. In this study, we intend to investigate the robustness of the HRCA-based framework to detect the UES opening in signals collected from a diverse population. The framework showed comparable performance regarding the UES opening detection with an average area under the ROC curve of 95%. The results indicate that the HRCA-based UES opening detection can provide superior performance on swallows from diverse populations which demonstrates the clinical potential of HRCA as a non-invasive swallowing assessment tool.
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