a-fully-automated-rodent-conditioning-protocol-for-sensorimotor

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. ...

Michigan State University

Primary hydatid cyst of the parotid gland is extremely rare, even in the endemic areas. A 23-year-old woman presented with slowly progressive swelling in the right periauricular region. Computed tomography (CT) scan of the head and neck revealed a round, well-demarcated water-density mass in the right parotid gland. At the operation, the cystic mass replacing most of the superficial part of right parotid gland was demonstrated. Superficial parotidectomy was carried out. Histopathological examination confirmed the diagnosis of hydatid disease. CT scan is a valuable imaging method for diagnosis of parotid cystic lesions; however, other acquired and congenital cystic lesions of parotid gland may have similar appearance and should be differentiated. Where the incidence of the disease is high, hydatid cyst of parotid gland should be considered in the differential diagnosis of lesions causing swelling of the parotid area.

Hydatid cyst of the parotid gland.

A fundamental goal in systems neuroscience is to assess the individual as well as the synergistic roles of single neurons in a recorded ensemble as they relate to an observed behavior. A mandatory step to achieve this goal is to sort spikes in an extracellularly recorded mixture that belong to individual neurons through feature extraction and clustering techniques. Here, we propose an approach for approximating the often nonlinear and time varying decision boundaries between spike-derived feature classes based on a simple, yet optimal thresholding mechanism. Because thresholding is a binary classifier, we show that the complex nonlinear decision boundaries required for spike class discrimination can be achieved by adequately fusing a set of weak binary classifiers. The thresholds for these binary classifiers are adaptively estimated through a learning algorithm that maximizes the separability between the sparsely represented classes. Based on our previous work, the approach substantially reduces the computational complexity of extracting, aligning and sorting multiple single unit activity early in the data stream. Here we also show its ability to track changes in spike features over extended periods of time, making it highly suitable for basic neuroscience studies as well as for implementation in miniaturized, fully implantable electronics in brain-machine interface applications.

Ali Mohebi

Electrical and Computer Engineering

Michigan State University

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Ali Mohebi

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Electrical and Computer Engineering

Michigan State University

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Electrical and Computer Engineering