An artificial neural network for neural spike classification. Bioengineering Conference

Abstract pulse amplitude and frequency. The Medial Inositol neuron produces a spike train with a pronounced phasic response. We discuss an Artificial Neural Network capable of sorting neural spikes contained in a mixed spike train. The ANN performs very well when compared with conventional optimal methods of Template Matching and Principal Components. The recordings are digitized and the resulting sequences filtered to detect the occurrence of a spike. Once a spike is detected, the sample values are extracted from the spike train and added to the trial's ensemble of spikes for subsequent classification. The remainder of the paper discusses results for the medial sensillum although a similar method exists for the lateral sensillum. The results of several spike classification techniques are examined to determine the best method for this application. INTRODUCTION Peripheral chemosensory organs analyze environmental conditions and transmit this analysis along parallel fibers to the CNS where a specific decision center can further process the information. These organs typically contain multiple sensory neurons each of which will respond to distinct chemical stimuli. Thus an across fiber pattern of activity that is transmitted to the CNS provides a chemical analysis. In insects, the summed responses of neural activity can be obtained by recording from the exterior of a taste organ (sensillum) of an intact animal. These multiunit recordings are commonly used to understand sensory and behavioral physiology

Additional file 1: of Report of three imported cases of neurocysticercosis in Guadeloupe

Case 3 CT-angiography images. (ZIP 762 kb

Bioengineering and Bioinformatics Summer Institutes: Meeting Modern Challenges in Undergraduate Summer Research

Summer undergraduate research programs in science and engineering facilitate research progress for faculty and provide a close-ended research experience for students, which can prepare them for careers in industry, medicine, and academia. However, ensuring these outcomes is a challenge when the students arrive ill-prepared for substantive research or if projects are ill-defined or impractical for a typical 10-wk summer. We describe how the new Bioengineering and Bioinformatics Summer Institutes (BBSI), developed in response to a call for proposals by the National Institutes of Health (NIH) and the National Science Foundation (NSF), provide an impetus for the enhancement of traditional undergraduate research experiences with intense didactic training in particular skills and technologies. Such didactic components provide highly focused and qualified students for summer research with the goal of ensuring increased student satisfaction with research and mentor satisfaction with student productivity. As an example, we focus on our experiences with the Penn State Biomaterials and Bionanotechnology Summer Institute (PSU-BBSI), which trains undergraduates in core technologies in surface characterization, computational modeling, cell biology, and fabrication to prepare them for student-centered research projects in the role of materials in guiding cell biology