Week 1, Lecture 2

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Computer Science and Engineering
CSE 528
Rajesh Rao

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
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