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Computational neuroscience is an exciting, constantly evolving subfield in neuroscience that brings together theories and ideas from many different areas in STEM such as physics, chemistry, math, computer science, and psychology. Through the exploration of computational models of neuronal and neural network activity, students will be introduced to a handful of quantitative STEM concepts that intersect with neuroscience. Before beginning this course students are expected to know about the action potential and synaptic transmission (see prerequisites). In this course, we will connect those biological phenomena to quantitative STEM concepts and then to computational models in Matlab. This course is designed for Neuroscience and Biology majors who want to take their first steps towards mathematical and computational models of the brain. Students interested in the computational track for the Neuroscience major should consider taking this course. By the end of this course students will be able to: ● Identify the scope of a neuroscience model and determine what it can and cannot tell us. ● Compare models and select an appropriate model for a given scientific question from among the models covered in this course. ● Make connections from the action potential and synaptic transmission to quantitative concepts from other STEM disciplines. ● Design, construct, and implement computational neuroscience models of neurons and neural networks using Matlab