![]() ![]() ROB KASS: STATISTICAL BACKGROUND AND STATISTICAL MODELS IN COMPUTATIONAL NEUROSCIENCE: WHAT IS COMPUTATIONAL NEUROSCIENCE?Ĭomputational neuroscience emerged from converging ideas that would now be associated with computer science, mathematics, neuroscience, psychology, and statistics. The Professional Development Workshop on Teaching at the 2019 annual meeting of the Society for Neuroscience gathered together experts to discuss options for teaching computation in neuroscience, with the goal of helping faculty plan or revise courses in this area, particularly for undergraduates. At one time this curriculum seemed beyond the reach of most undergraduates ( Grisham, 2014), but a reconsideration of the zeitgeist forces us to conclude that now is the time to develop such courses. Statistical methods of analyzing data are best learned in pursuit of scientific questions, and such experience and skills in data science have never been in greater demand. Indeed, the National Science Foundation (NSF) and American Association for the Advancement of Science Vision and Change ( AAAS, 2011) document urges educators in biological science to augment students’ quantitative reasoning by using modeling and simulation to describe living systems. Teaching computational neuroscience endows students with valuable skills as they enter the workforce ( Grisham et al., 2016). The BRAIN Initiative’s BRAIN 2025 report ( Bargmann et al., 2014) put theory, modeling and data analysis at the core of expected future advances in neuroscience, and the 2019 BRAIN review ( du Lac et al, 2019) underscored the ongoing pressing need for training in these areas. In addition, stunning advances in recording, molecular, and anatomical techniques provide us with data sets of ever-increasing complexity, pushing the need for tools and concepts to extract meaning from these data. Thus, if we are to capture this reality, we need effective models, and the only models that could reasonably fulfill this role are computational ones. Real brains, by contrast, are complex, dynamic, and interactive - often in a nonlinear fashion across time. A plastic model of a human brain reflects reality and can explain neuroanatomy, but it is static. Models can fit this need if they reflect important aspects of reality. As educators, we need ways of conceptualizing the brain so that we can explain it and its function to students. The task of understanding brains is a central aim of neuroscience. This course is 2 hours of lecture and 3 hours of laboratory and is accepted for 3 units of transfer credit at CSU and UC.- George Edgin Pugh, The Biological Origin of Human Values, 1977 ![]() You'll surely enjoy this Introduction to Engineering class at Laney College that will open your eyes to so many possibilities and careers! You do not need to have any prior background in engineering, design, or programming to succeed in this class. You will also gain experience using the 3D printers and the laser cutters in the Laney College FabLab. In ENGIN 10 you will learn about the different careers and opportunities in engineering, you will have the chance to build and program a battle robot, and you will learn design processes that you’ll put in practice to design a solution for a real-world problem. Whatever your interests, there is an engineering major to fit those interests and you'll get to find that major in Introduction to Engineering. Engineers design everything around you-from your cell phone to the treatment plant that provides you with clean drinking water or to solar powered airplanes that can circumnavigate the globe. ![]()
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