计算神经科学

Computational Neuroscience

研究大脑的工作运转仍然是当今科学界的一大挑战,本课程主要介绍了用于分析和建模的基本计算方法,细胞与大脑回路的行为。学习这门课,无需具备神经系统科学的相关背景。

华盛顿大学

Coursera

计算机

简单(初级)

31 小时

  • 英语
  • 3491

课程概况

本课程通过对基本计算方法的介绍,了解神经系统的作用和工作机理,探索视觉控制、感官控制、学习和记忆等能力的计算原理;主要内容包括:尖峰神经元的信息表现形式,神经网络中的信息处理和自适应学习算法,利用Matlab语言进行编程示范与练习,从而深入理解相关概念和计算方法。本课程主要针对本科三/四年级生、研一新生,专业人士以及对大脑信息处理能力感兴趣的远程学习者。

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.

你将学到什么

1.基础神经生物学

2.神经编码和译码方法

3.信息论与神经编码

4.(生物物理学简化的)单神经元模型

5.(神经元的)突触和(前馈&递归)神经网络模型

6.突触可塑性与学习记忆

课程大纲

周1
完成时间为 4 小时
Introduction & Basic Neurobiology (Rajesh Rao)
This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
6 个视频 (总计 89 分钟), 6 个阅读材料, 2 个测验

周2
完成时间为 4 小时
What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)
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.
8 个视频 (总计 167 分钟), 3 个阅读材料, 1 个测验

周3
完成时间为 3 小时
Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)
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.
6 个视频 (总计 114 分钟), 2 个阅读材料, 1 个测验

周4
完成时间为 3 小时
Information Theory & Neural Coding (Adrienne Fairhall)
This module will unravel the intimate connections between the venerable field of information theory and that equally venerable object called
our brain.
5 个视频 (总计 98 分钟), 2 个阅读材料, 1 个测验

周5
完成时间为 4 小时
Computing in Carbon (Adrienne Fairhall)
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.
7 个视频 (总计 114 分钟), 2 个阅读材料, 1 个测验

周6
完成时间为 3 小时
Computing with Networks (Rajesh Rao)
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!
3 个视频 (总计 72 分钟), 2 个阅读材料, 1 个测验

周7
完成时间为 3 小时
Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)
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.
4 个视频 (总计 86 分钟), 2 个阅读材料, 1 个测验

周8
完成时间为 3 小时
Learning from Supervision and Rewards (Rajesh Rao)
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!

预备知识

了解线性代数、微积分、概率分布的基本概念和概率理论,尤其是要理解含有向量和矩阵的线性方程,能区分阶梯函数。习题方面,要熟悉Matlab或Octave编程语言,无需具备神经科学领域的相关背景。

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