![]() Your grade will be determined by biweekly quizzes, homework assignments (drop the lowest) and a take-home final exam. Applications: sensory systems, language, decision-making, ….Machine and deep learning: model fitting, GLM, CNN, RNN.Plasticity & learning: short & long-term plasticity, reinforcement learning.Network models: firing rate models, feedforward/recurrent models, stochastic networks.Morphological neuron models: synaptic conducances, cable equation, multi-compartment models.Point neuron models: LIF, Izhikevich neurons, Hodgkin-Huxley neurons.Neuroelectronics: Electrical properties of neurons, Nernst equation.Neural encoding: spike trains and firing rates, early visual system.Intro to CompNeuro: concepts, properties of neurons, cell types.prior exposure to differential equations,.The actual necessary background includes: Necessary is the introduction to matrix algebra. The formal prerequisite is PSY-221B, but the only part of that course that is Lab sections will feature Python & math tutorials, hands-on examples, and guided programming sessions. However, coding examples of the concepts is the best way to demonstrate (and facilitate) your knowledge of them. Qspace Production - Q Space Pro Media Production We are a media production company. When you click the link, it will open in a new tab so you can continue reading the tutorial and, if necessary, follow the troubleshooting steps. However, this is not primarily a programming course - that is, the main goal is to learn the concepts, not to learn a programming language or particular programming techniques. Navigate to the gaucho space login official login page using the link provided below. You will gain experience both conceptually and practically, by homework assignments that involve solving problems and implementing computational models. fit a computational model to experimental data.computationally model the biophysics of single neurons and the dynamics of neural networks,. ![]() describe different methods that computational neuroscientists use to model neural coding,.We will cover both classical (e.g., GLM, LIF, Hodgkin-Huxley model) and state-of-the-art methods (i.e., deep learning).īy the end of this course, you should be able to: This is a lecture course that surveys computational neuroscience, which is a branch of neuroscience that employs mathematical models, theoretical analysis, and abstractions of the brain to understand the principles that govern development, structure, physiology, and cognitive abilities of the nervous system. PSY-221F is the new course number for PSY-265 formerly taught by Greg Ashby Course Description ![]()
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