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.

제공자:

## 컴퓨터 신경 과학

## About this Course

### 학습자 경력 결과

## 18%

### 귀하가 습득할 기술

### 학습자 경력 결과

## 18%

#### 100% 온라인

#### 유동적 마감일

#### 초급 단계

#### 완료하는 데 약 31시간 필요

#### 영어

## 강의 계획 - 이 강좌에서 배울 내용

**완료하는 데 4시간 필요**

## Introduction & Basic Neurobiology (Rajesh Rao)

This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.

**완료하는 데 4시간 필요**

**6개의 동영상**

**6개의 읽기 자료**

**2개 연습문제**

**완료하는 데 4시간 필요**

## 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.

**완료하는 데 4시간 필요**

**8개의 동영상**

**3개의 읽기 자료**

**1개 연습문제**

**완료하는 데 3시간 필요**

## 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.

**완료하는 데 3시간 필요**

**6개의 동영상**

**2개의 읽기 자료**

**1개 연습문제**

**완료하는 데 3시간 필요**

## 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.

**완료하는 데 3시간 필요**

**5개의 동영상**

**2개의 읽기 자료**

**1개 연습문제**

### 검토

#### 4.7

##### 컴퓨터 신경 과학의 최상위 리뷰

I really enjoyed this course and think that there was a good variety of material that allowed people of many different backgrounds to take at least one thing away from this.

This course is an excellent introduction to the field of computational neuroscience, with engaging lectures and interesting assignments that make learning the material easy.

Great course! Really enjoyed the variety of topics and the just enough computational work in the quiz's. And that Eigen hat had me smiling and laughing about it for a week.

In my opinion, the course level ought to be intermediate, not beginner. You can take more out of the course if you already have knowledge in this, or related, areas.

interesting instructor and interesting content. Now I know more about the theoretical research related to neuro function and its connection to machine learning now.

A good look at mathematical models focusing mainly at the synapse and neuron level. The math came a little fast and furious for my 30+ years antique math training.

This is a wonderful start for a biologist , to get idea of concepts of learning . An advanced course focused more on brain circuitry is suggested.\n\nThanks a lot

Very challenging course with fascinating new content that refers to a lot of research in the area! Good start for someone considering computational neuroscience.

Starts off great but get rushed 3/4ths into the course. Too much content, too little explanation, but recovers swiftly to end on a high.\n\nRecommended

Overall - A good introductory course. But the last week, reinforcement learning and neural networks, could have involved programming questions.

As a self-paced student, I like this kind of course. I hope to see a whole specialization in this field with final capstone project. Thanks.

Pretty good. A bit of mathematical ambiguity and lax notational conventions, but the course content was solid and presented clearly.

An amazing course for people with a computer science background wishing to explore the world of human brain from CS perspective.

I found this course helpful and inspiring for my research activity. I suggest it to anyone who has basic mathematical skills.

Overall I enjoyed this class, but towards the end it gets more into machine learning and away from the neuroscience.

Excellent Course! Very in-depth and informative! Exceptional faculty and extensive supplementary material as well!

Quite nice if you follow the recommended textbook - Peter Dayan - with it. The lectures touch important point.

this course is perfect for who wants to get familiar with computational neuroscience general concepts.

Great overview of a really cool field, gives nice intuitions for ideas in computational neuroscience.

Excellent overview of the different areas of computational neuroscience taught by engaging academics.

### 제공자:

#### 워싱턴 대학교

Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.

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