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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/computational-neuroscience
课程评论：没有评论
课程名称：计算神经科学 概述：本课程旨在介绍基本的计算方法，以理解神经系统的功能及其运作原理。我们将探索支配视觉、感觉-运动控制、学习和记忆等各个方面的计算原则。具体主题包括神经元的脉冲编码信息、神经网络中的信息处理，以及适应和学习算法。课程将结合Matlab/Octave/Python演示和练习，以加深对所介绍概念和方法的理解。本课程主要面向三、四年级本科生、初级研究生以及对脑部信息处理感兴趣的专业人士和远程学习者。 课程大纲： 1. **介绍与基础神经生物学（Rajesh Rao）** - 该模块包括计算神经科学的介绍以及基础神经生物学的入门知识。 2. **神经元编码：神经编码模型（Adrienne Fairhall）** - 该模块介绍神经信息编码的迷人世界，学习记录脑活动的技术，发展数学公式以描述神经元脉冲作为编码，并研究大脑中的变异性和噪音。 3. **从神经元提取信息：神经解码（Adrienne Fairhall）** - 模块探讨神经解码，即如何从神经活动中估计大脑正在看到、意图或体验的内容。该领域在神经假体和脑-计算机接口等应用中越来越重要。 4. **信息理论与神经编码（Adrienne Fairhall）** - 探索信息理论与我们大脑之间的密切联系。 5. **碳计算（Adrienne Fairhall）** - 该模块介绍神经元的生物物理学，并学习著名的霍奇金-赫克斯利模型及其他神经元模型，包括树突的结构建模。 6. **网络计算（Rajesh Rao）** - 探索如何将神经元模型连接成网络模型，涉及突触的建模、集成-发火神经元的简单网络，以及动态递归网络的世界。 7. **学习的网络：大脑中的可塑性与学习（Rajesh Rao）** - 研究大脑中突触可塑性与学习的模型，包括赫布学习理论和基于稀疏编码与预测编码的脑功能理论。 8. **监督与奖励学习（Rajesh Rao）** - 通过监督学习与强化学习，了解反向传播算法、预测奖励以及在特定大脑区域（如基底神经节）中选择最佳动作以最大化奖励的可能神经实现。最后，通过强化学习操控直升机飞行。 该课程适合希望深入了解神经系统计算方法的学生和专业人士。
Name:Introduction & Basic Neurobiology (Rajesh Rao)
Description:This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
Name:What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)
Description:This module introduces you to the captivating world of neural information coding. You will learn about the technologies that are used to record brain activity. We will then develop some mathematical formulations that allow us to characterize spikes from neurons as a code, at increasing levels of detail. Finally we investigate variability and noise in the brain, and how our models can accommodate them.
Name:Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)
Description:In this module, we turn the question of neural encoding around and ask: can we estimate what the brain is seeing, intending, or experiencing just from its neural activity? This is the problem of neural decoding and it is playing an increasingly important role in applications such as neuroprosthetics and brain-computer interfaces, where the interface must decode a person's movement intentions from neural activity. As a bonus for this module, you get to enjoy a guest lecture by well-known computational neuroscientist Fred Rieke.
Name:Information Theory & Neural Coding (Adrienne Fairhall)
Description:This module will unravel the intimate connections between the venerable field of information theory and that equally venerable object called our brain.
Name:Computing in Carbon (Adrienne Fairhall)
Description:This module takes you into the world of biophysics of neurons, where you will meet one of the most famous mathematical models in neuroscience, the Hodgkin-Huxley model of action potential (spike) generation. We will also delve into other models of neurons and learn how to model a neuron's structure, including those intricate branches called dendrites.
Name:Computing with Networks (Rajesh Rao)
Description:This module explores how models of neurons can be connected to create network models. The first lecture shows you how to model those remarkable connections between neurons called synapses. This lecture will leave you in the company of a simple network of integrate-and-fire neurons which follow each other or dance in synchrony. In the second lecture, you will learn about firing rate models and feedforward networks, which transform their inputs to outputs in a single "feedforward" pass. The last lecture takes you to the dynamic world of recurrent networks, which use feedback between neurons for amplification, memory, attention, oscillations, and more!
Name:Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)
Description:This module investigates models of synaptic plasticity and learning in the brain, including a Canadian psychologist's prescient prescription for how neurons ought to learn (Hebbian learning) and the revelation that brains can do statistics (even if we ourselves sometimes cannot)! The next two lectures explore unsupervised learning and theories of brain function based on sparse coding and predictive coding.
Name:Learning from Supervision and Rewards (Rajesh Rao)
Description:In this last module, we explore supervised learning and reinforcement learning. The first lecture introduces you to supervised learning with the help of famous faces from politics and Bollywood, casts neurons as classifiers, and gives you a taste of that bedrock of supervised learning, backpropagation, with whose help you will learn to back a truck into a loading dock.The second and third lectures focus on reinforcement learning. The second lecture will teach you how to predict rewards à la Pavlov's dog and will explore the connection to that important reward-related chemical in our brains: dopamine. In the third lecture, we will learn how to select the best actions for maximizing rewards, and examine a possible neural implementation of our computational model in the brain region known as the basal ganglia. The grand finale: flying a helicopter using reinforcement learning!
This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.
