Characterizing Visual System Receptive Fields

Welcome to My Blog!  This particular blog post is related to, or expands on, materials covered in my book: Biopsychology (9th Edition).


In Chapter 6 of Biopsychology (9th Edition), you read about the results of scientific work that sought to characterize the receptive fields of visual system neurons.  This blog post provides a more detailed explanation of how vision neuroscientists characterize the receptive fields of neurons.

An obvious place to start any discussion of receptive field mapping methods is with an overview of how Hubel and Wiesel went about characterizing receptive fields.  If you recall, Hubel and Wiesel won the Nobel Prize for their work on the receptive fields of cat primary visual cortex neurons. In the following video, Hubel and Wiesel talk about their research technique and they also directly show you how they went about collecting their data.  Pay particular attention to the nature of the test stimuli they used.

As you can see, Hubel and Wiesel used simple dots and bars of light, in their receptive field mapping reseacrh.  They, like many vision researchers of their time, made two implicit assumptions when selecting their test stimuli.  First, they assumed that we can gain an understanding of visual system function by using simple test stimuli (i.e., dots and bars of light).  Second, they assumed that the receptive field of a visual system neuron is a fixed attribute of that cell; that is, they assumed that their map of the receptive field of a particular cell would remain the same if they were to come back and measure it days or weeks later.  Both of these assumptions are now the subject of considerable scrutiny.

With respect to the first assumption, it appears that using simple stimuli has given us a skewed portrayal of how the visual system works.  When researchers use natural complex stimuli, the responses from primary visual cortex neurons are more robust.

As for the second assumption, it appears that the receptive field of a visual system neuron is a dynamic property of that cell.  Its size, location and properties can change as a function of the changing visual scene.



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