Week 1, Lecture 2

6 Pages
57 Views
Unlock Document

Department
Computer Science and Engineering
Course
CSE 528
Professor
Rajesh Rao
Semester
Spring

Description
What is Computational Neuroscience? - “The goal of computational neuroscience is to explain in computational terms how brains generate behaviors” (T. Sejnowski) o Dissecting this definition leads us to the following analysis: Computational neuroscience provides tools and methods for “characterizing what nervous systems do, determining how they function, and understanding why they operate in particular ways ” (P. Dayan and L. Abbott) o This definition corresponds quite nicely to the models that we discussed  Descriptive Models (What)  Mechanistic Models (How)  Interpretive Models (Why) An Example: Models of “Receptive Fields” - Responses of a Neuron in an Intact Cat Brain (Hubel and Wiesel, c. 1965) - Short video demonstrating neuronal response to a bar of light oriented at a 45 degree angle - Hubel and Weisel converted the responses into sound so that you can hear them. It makes a kind of crackling - When the bar moves over the brain cell at a 45 degree angle, there is a lot of crackling: the brain cell “likes” this sort of stimuli - It does not “like” when a large square of light covers it entirely of when the bar moves at a different orientation - It does respond when the edge of the square is covering the area - When the bar is horizontal, there is not much of a response, hence the spikes are widely dispersed - When the bar is oriented at a 45 degree angle in the orientation that the cell “likes,” this causes the most robust response - When the bar of light is at a different 45 degree angle, the response is lesser Question 1 This describes a model of a specific neuron in a cat responding to visual stimuli. Which of the following functions most accurately depict the model we are talking about here? A) Amplitude of spikes = f(Light bar's orientation) B) Frequency of spikes = f(Light bar's orientation) C) Light bar's orientation = f(Frequency of spikes) D) Light bar's orientation = f(Amplitude of spikes) Receptive Field - Definition: Specific properties of a sensory stimulus that generate a strong response from the cell - Examples: o Cell responds very robustly to a spot of light that turns on at a particular location on the retina o Robust cell response to a bar of light that turns on at a particular orientation and location on the retina Receptive Field Models - Let’s look at three different types of computational models for receptive fields: o A Descriptive Model of Receptive Fields o A Mechanistic Model of Receptive Fields o An Interpretive Model of Receptive Fields I. Descriptive Model of Receptive Fields - The Retina: o A layer of tissue at the back of your eyes. When you look at a particular object, the image is reflected onto the retina. o When you’re recording from a group of cells called the Retinal Ganglion Cells, you will find that it conveys information to other areas of the brain (particularly the Lateral Geniculate Nucleus
More Less

Related notes for CSE 528

Log In


OR

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


OR

By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.


Submit