1.2 Computational Neuroscience: Descriptive Models

(دانلود اسلایدها در https://t.me/neurophysic) Welcome back. Let's begin by asking what is computational neuroscience? According to Terry Sejnowski, who was one of the founding fathers of the field and was also my postdoctoral advisers, the goal of computation neuroscience is to explain in computational terms how brains generate behaviors. Now, let's dissect this definition a little bit. This leads us to a definition proposed by Peter Dayan and Larry Abbott. According to them, computational neuroscience is the field that provides us with the tools and methods for doing three different things. One is characterizing what nervous systems do. The second is Determining how they function. And finally, the third is, understanding why they operate in particular ways. Now, this actually corresponds quite nicely to the three types of computational models that we looked at earlier in the, in a previous lecture. This corresponds to descriptive models, mechanistic models and interpretive models. Now to understand these three types of models in a little bit more detail with an example, let's look at the concept of receptor fields. In order to understand the concept of receptive fields it is useful to go back to some early experiments performed by Hubel and Wiesel back in the 1960s. Now, they were interested in trying to understand the visual system of the cat. In order to do so, they implanted tiny electrodes, or tiny wires..........................

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