Explain in your own words why these simple

VISUAL NEURAL SIMULATOR
Tutorial for the
Receptive Fields Module

Copyright: Dr. Dario Ringach, 2015-02-24
Editors: Natalie Schottler & Dr. William Grisham

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Introduction. The goal of this laboratory is to give you hands-on experience at analyzing the
responses of cortical neurons to visual stimuli. We will concentrate on only one class of such
neurons, the so-called simple cells.
Neurons communicate with each other by sending nerve impulses (or spikes). The receptive field
of a neuron is defined as the area of visual space where the distribution of light can influence the
spiking of a neuron (that is, we can make the neuron fire more or less frequently).
Simple cells have receptive fields that are composed of two different sub-regions. There are ON
sub-regions defined as those where increases in light induce the cell to fire more nerve impulses,
and there are OFF sub-regions defined as those where light decreases induce the cell to increase
its firing rate. Furthermore, these regions tend to be elongated in space and adjacent one to
another. Another characteristic of these cells is that they exhibit spatial linearity. This means, up
to the point of spike generation, the response to the sum of two stimuli is equal to the sum of the
responses to the individual stimuli. The spatial organization of the simple cell endows them with
selectivity for the orientation of a stimulus (such as a bar of light). Cortical cells are the first stage
of cortical processing where such a property is observed (it is not seen in the retina or the
thalamus).
Simple cortical cells were first discovered by Hubel and Wiesel's pioneering work in visual cortex.
Their work was so important for the advancement of sensory electrophysiology in general and for
our understanding of the wiring of the brain during development that they were awarded the Nobel
Prize in Medicine in 1981.
Specific Aims. You will be using a computer simulation (called receptive_field) to examine how
simple cortical cells respond to visual stimuli under your control. We will be using both stimuli that
can be controlled manually (including bright/dark spots and bars) as well as drifting sinusoidal
gratings, which can be thought of as a periodic sequence of light/dark 'bars'.
There are three parts for this project. The aims of the exercises are as follows:
• Part #1 - Manual mapping of receptive fields. We will observe videotapes of Hubel and
Wiesel mapping the receptive fields of neurons in the early days of visual electrophysiology.
Then, we will use the program simply to effectively simulate the manual mapping of receptive
fields.
• Part #2 - Generating tuning curves. We will be measuring the responses of cortical cells to
changes in stimulus parameters and plotting the resulting tuning curves. Thus, for example,
we will be plotting the rate of spiking of a neuron as a function of the orientation of the stimulus,
which is called an orientation tuning curve. We will also study how such curves depend on the
contrast of the stimulus.
• Part #3 - Contrast gain control. We will review some data that shows that tuning curves do not
change with the strength of the stimulus, a very important property known as contrast
invariance. We will study how is that cortical cells can actually achieve this.

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Part #1. To get stated, run Matlab by double-clicking the MATLAB R2007b shortcut on the
desktop. A MATLAB 7.5.0 (R2007b) window will appear. In the Command Window

section, type
the following highlighted commands at the prompt (>>), hitting Return on the keyboard after each
command. [Note: If you are using a digital copy of this tutorial, you may also copy the highlighted
portions and paste them into the Command Window prompt.]
cd receptive_field
receptive_field_lab(1)

FIGURE 1
A receptive_field window will appear. You can now minimize the main MATLAB window since
you will be only interacting with the receptive_field one. Click the Run button to initiate the
simulation.

FIGURE 2
There are two images shown side by side in this interface. The Receptive Field image on the right
shows the spatial organization of the receptive field of a simple cortical neuron. Here, areas in red
hues indicate ON sub-regions while areas in blue hues indicate OFF sub-regions. Green regions
are neutral (that is, they do not respond to light increases or decreases) and, therefore, are outside
the receptive field of the cell.
The Stimulus image on the left shows a visual stimulus region on the exact area of the visual field
as the receptive field is on the right side of the window. Think of this window as a 'projection
screen' where you will be projecting patterns of light, as spots or bars, which can be either lighter or
darker than the gray background. These patterns of light will appear by moving the mouse within
the visual stimulus region (gray box).

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Earlier, you began the simulation (when you clicked the Run button). This generated a receptive
field at random and displayed it (in the Receptive Field image box). It also gave you control of the
visual stimulus (in the Stimulus image box) so that you can stimulate the cell. The responses are
computed in real-time. Every time the cell fires an action potential there will be a 'click' sound from
the speakers. As you saw in the video-tape of Hubel and Wiesel, this is in fact the way that visual
neuroscientists get audio feedback in the laboratory during the actual experiments. The faster the
rate of the clicks, the better the cell is firing action potentials. The response of the cell is usually
characterized as the firing rate: the total number of action potentials evoked by a stimulus divided
by the total time the stimulus was present, and thus measured in units of spikes/sec.
On the Stimulus (left) side of the receptive_field window, make sure that the 'Dark' box is
unchecked. Try using the mouse to move across the Stimulus image box. A bright, light spot will
project where the mouse is. Attempt to find the regions on the Stimulus image box where the
bright spot causes the cell to fire. (Hint: the cell likes light in the red regions and dark in the blue
regions).
You can make the spot darker than the background by checking the 'Dark' box. Now attempt to
find the regions on the Stimulus image box where the dark spot causes the cell to fire.

FIGURE 3
The size of the spot can be increased/decreased using the slider labeled 'spot radius/bar
orientation'. Adjust this setting to try different spot sizes now.
There are a number of commands that can be given directly using keyboard shortcuts. For this
method to work you need to first click inside the Stimulus image box with the mouse. To alter the
size of the light or dark stimulus spot, simply hit the letters 'q' and 'w' on the keyboard to
respectively decrease and increase it. Note that after changing the settings via keyboard, you
won’t be able to see the updates until you move the mouse again.

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Now you are ready to map receptive fields! Use a light spot (uncheck the 'Dark' box) and try to find
a region where the cell fires. Having clicked inside the Stimulus image box (to allow for keyboard
commands) hit 'p' (for 'plus') to place a '+' sign indicating that this location was responding to a
bright spot. Move the stimulus to other locations and continue placing '+' signs in those locations
as well. Now use a dark spot (check the 'Dark' box) to do the same but this time hit the 'm' (for
'minus') to place a little triangle in those regions that respond to light decreases. If you want to
clear all of the symbols and start over again hit 'c' (for 'clear'). Once you are done, hit the Stop
button to stop the simulation. Once you do this you will see all of your mapping symbols
superimposed on top of the actual receptive field in the Receptive Field image box.

FIGURE 4
Click the Run button again to generate a new simulation, and repeat this mapping procedure.
Remember to click the Stop button after mapping to stop the simulation and display your accuracy.

FIGURE 5

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Once you have mapped the cells with bright/dark spots of light a second time, click the Run button
to generate a new simulation, then switch the stimulus to an oriented bar by selecting the 'Bar'
option in the 'Stimulus type' box. The bar may be dark or bright by respectively checking or
unchecking the 'Dark' box. The orientation of the bar will change either by adjusting the slider
labeled 'spot radius/bar orientation' or by using the keyboard commands 'q' and 'w' (remember to
click inside the Stimulus image box before using the keyboard commands and to move the mouse
after to see the settings update).
Using both light and dark options, orient the bar so that it is parallel to the elongation of the
receptive field sub-regions and drift it back and forth across the receptive field. Does the cell
respond?

FIGURE 6
Flip the bar 90° so that it is perpendicular to the elongation sub-region. Does the cell respond?

FIGURE 7
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Try different orientations, using light and dark bars, and evaluate if your cell is well tuned for the
orientation of the stimulus. The narrower the range of orientations the cell responds to the better
tuned it is. When you are finished, click the Stop button to stop the simulation.
Question #1: Explain in your own words why these simple cells give larger responses when
the bars are parallel to the axis of elongation than when they are perpendicular to them.
The cortex is the first site where neurons are selective to the orientation of a visual stimulus. The
lateral geniculate nucleus (or LGN) of the thalamus is the primary region that receives visual
information directly from the retina and subsequently projects to the primary visual cortex. Select
the 'Spot' option in the 'Stimulus type' box on the left side of the window, and the 'Simulate LGN'
option on the right side of the window. Then click the Run button to generate a new simulation
with these settings.

FIGURE 8
The LGN contains cells with receptive fields that are circularly symmetric. They are composed of a
strong center region of one sign (the example above shows an OFF center region), surrounded by
a weaker area of the opposite sign (the example above shows an ON surround).
Map the receptive fields in the LGN as you did with the cortical cells, using both spot and bar
options, both light and dark options, and the keyboard commands 'p', 'm', and 'c' as necessary.
Remember that after mapping, you may click the Stop button to stop the simulation and
superimpose your symbols in the Receptive Field image box.

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FIGURE 9
Examine how LGN receptive fields respond to light and dark bars of different orientations. Notice
that its response does not change with the orientation of the bar. In other words, the response is
untuned for orientation. Remember to click the Run/Stop button to start/stop the simulation.
Question #2: Explain in your own words why the receptive fields of LGN cells are not
selective for the orientation of a stimulus.
Question #3: Diagram a possible circuit where inputs from the LGN can be summed
together to yield a cortical receptive field with elongated receptive fields.
Now it is time to play the Mystery Receptive Fields game! While keeping the 'Simulate LGN' box
checked, also check the 'Mystery Receptive Field Test' box. Click the Run button to generate a
new simulation where the receptive field is masked (so you can't actually see it).

FIGURE 10
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Map the receptive fields here as you did with the visible cortical and LGN cells, using both spot and
bar options, both light and dark options, and the keyboard commands 'p', 'm', and 'c' as necessary.
BEFORE you click the Stop button to stop the simulation and reveal the solution with your symbols
superimposed in the Receptive Field image box, have an instructor come by to verify that you
have correctly mapped them. DO NOT continue until this is complete. AFTER your mapping has
been approved, uncheck the ‘Stimulate LGN’ option on the right side of the window. Then click the
Run button to generate a new simulation with these settings. Accurately map and have verified
TWO mystery cortical receptive fields.
Cells in the visual cortex have a spontaneous rate of firing. That is, they will fire at some constant
rate even when there is a uniform gray in the Stimulus image box. Uncheck the 'Mystery
Receptive Field Test' box on the right side of the window. Then increase the 'Spontaneous rate'
slider to the center setting on the right side of the window to allow the cell to fire spontaneously at a
baseline rate even in the absence of a visual stimulus. Click the Run button to generate a new
simulation with these settings.

FIGURE 11
Now examine how the presence of spontaneous activity influences the resulting mapping of a
receptive field. Without a visual stimulus present (here, where no spots or bars are visible within
the Stimulus image window), you should hear the cell firing spontaneously at about 4 spikes/sec.
Map the receptive fields here as you did with before, using both spot and bar options, both light
and dark options, and the keyboard commands 'p', 'm', and 'c' as necessary. Do this a few times
with both cortical and LGN cells, and be sure to check your mapping ability by stopping the
simulation and observing the superimposed images. Remember to click the Run/Stop button to
start/stop each simulation.
Now play the Mystery Receptive Fields game again, but at this higher spontaneous rate. Check
the ‘Stimulate LGN’ box, and also check the 'Mystery Receptive Field Test' box on the right side of
the window. Click the Run button to generate a new simulation where the receptive field is
masked (so you can't actually see it).
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Map the receptive fields here as you did before, using both spot and bar options, both light and
dark options, and the keyboard commands 'p', 'm', and 'c' as necessary. BEFORE you click the
Stop button to stop the simulation and reveal the solution with your symbols superimposed in the
Receptive Field image box, have an instructor come by to verify that you have correctly mapped
them. DO NOT continue until this is complete. AFTER your mapping has been approved, uncheck
the ‘Stimulate LGN’ option on the right side of the window. Then click the Run button to generate a
new simulation with these settings. Accurately map and have verified FOUR mystery cortical
receptive fields.
Question #4: Do you feel it is easier or more difficult to map the receptive field when the
cell has a spontaneous rate higher than zero? What did you notice was different? Explain
your answer in your own words.
Congratulations! You have successfully completed the Part #1 of the lab. In the Part #2, you will
begin using more quantitative methods to analyze the tuning properties of these neurons.

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Part #2. To get started, run Matlab by double-clicking the MATLAB R2007b shortcut on the
desktop. A MATLAB 7.5.0 (R2007b) window will appear. In the Command Window section, type
the following highlighted commands at the prompt (>>), hitting Return on the keyboard after each
command. [Note: If you are using a digital copy of this tutorial, you may also copy the highlighted
portions and paste them into the Command Window prompt.]
cd receptive_field
receptive_field_lab(2)

FIGURE 12
A receptive_field window will appear. Click the New receptive field button ONLY ONCE to
display a cell’s receptive field.

FIGURE 13
Now click the Run button to initiate the simulation.

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FIGURE 14
The interface is very similar to the one you interacted with in Part #1. The Receptive Field image
on the right shows the spatial organization of the receptive field of a simple cortical neuron. Again,
areas in red hues indicate ON sub-regions while areas in blue hues indicate OFF sub-regions, and
green areas are neutral and thus outside the cell’s receptive field.
The Stimulus image on the left once again shows a visual stimulus region on the exact area of the
visual field as the receptive field is on the right side of the window. This time, however, you will be
implementing well-controlled visual stimuli similar to that used in modern visual experiments, rather
than the pattern of flashing lights used by Hubel and Wiesel. Sinusoidal drifting stimuli will be
presented which you can control only across three parameters: (1) orientation (or drift direction),
(2) spatial frequency, (3) contrast.
Sinusoidal drifting stimuli (or sinusoidal grating) are periodic patterns of bright and dark 'bars' of
light that are moving (drifting) in a particular direction. The easiest way to grasp this concept is to
work with the stimuli by manipulating its parameters.
The orientation parameter, which operates as units of degrees, controls the direction of the drift. At
0° the stimuli move from left to right, at 90° the stimuli move from bottom to top, at 180° the stimuli
move from right to left, and at 270° the stimuli move from top to bottom. In the 'Orientation' field on
the left side of the window type 45, then hit Return on the keyboard. Watch how the directional
movement of the stimuli changes.

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FIGURE 15
Now type 270 in the 'Orientation' field, then hit Return on the keyboard. Watch how the directional
movement of the stimuli changes again.

FIGURE 16
The spatial frequency parameter controls the number of 'bars' (or cycles) appearing within a given
frame (here, the Stimulus image box). In the 'Spatial Frequency' field on the left side of the
window type 2.0, then hit Return on the keyboard. Watch how the number of bars (light and dark
separately) appearing in the frame decreases from four to two.

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FIGURE 17
In the 'Spatial Frequency' field on the left side of the window type 5.0, then hit Return on the
keyboard. Watch how the number of bars (light and dark separately) appearing in the frame
increases from two to five.

FIGURE 18
The contrast parameter, which operates as a percent, controls the depth of the luminance (or
brightness) modulation in the pattern. One could easily consider it analogous to stimulus 'strength',
in that the stronger the contrast is (by using a larger percentage), the bigger the visual contrast
between the light and dark bars. At 100% the stimuli display the most contrast, as they are
modulated from complete darkness (in the center of the dark bars) to the highest possible
luminance achievable by the computer monitor (in the center of the light bars). At 0% the stimuli
display the least contrast, and appear as a gray background. In the 'Contrast' field on the left side
of the window type 20, then hit Return on the keyboard. Watch how the contrast between light and
dark bars decreases.

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FIGURE 19
In the 'Spatial Frequency' field on the left side of the window type 1.5, and in the 'Contrast' field
type 100, then hit Return on the keyboard. Stop the simulation by hitting the Stop button once.

FIGURE 20
Notice that this interface provides you with two additional numeric features on the right side of the
window. Displayed adjacent to the New receptive field button is the average rate of firing per
second that occurred during the last run. In this case, the cell fired an averaged of 4.41 spikes/sec.
Displayed adjacent to the Run button is the total run time of the last run. In this case, the cell was
actively stimulated for 3735.0 sec. Soon, you will be measuring the mean firing rate of neurons as
a function of different stimulus parameters.

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Measuring the orientation tuning curve. Your first goal is to measure the orientation tuning
curve of a cell. This is a curve that shows the response of the cell (in spikes/sec) as a function of
the stimuli orientation (in degrees). You will be recording the responses for a sequence of angles
from 0° to 180°, given in intervals of 20°. A total of ten intervals will be recorded. You will run the
simulation for at least 20 sec for these different orientations and record the response rate in each
case.
Before you begin, click the New receptive field button several times until you have found a
receptive field that does NOT look like the one pictured in this tutorial and that does NOT look like
your other classmates’ receptive fields, but that DOES have at least one clear ON sub-region and
one clear OFF sub-region displaying in the Receptive Field image box. [Note: Since you need to
continue working with the same receptive field just generated, it is important that you DO NOT click
the New receptive field button again after you’ve found one that is appropriate.] Capture a screen
shot of the receptive_field window for your lab report along with an appropriate caption. To do
this, press Command-Shift-4 on the keyboard, and use your mouse to select the rectangular area
you wish to copy to the computer’s clipboard. A .png file of this image will appear on the Mac
Desktop. Rename the file with something descriptive, insert it into a document, and add a caption
to label the ON sub-regions and the OFF sub-regions of the receptive field.
Go back to the receptive_field window. In the 'Orientation' field on the left side of the window type
0, and in the 'Spatial Frequency' field type 4.0. Click the Run button and allow the simulation to
run for at least 20 sec before you hit the Stop button to stop it. Be sure you WRITE DOWN the
average rate of firing for your records.

FIGURE 21
In the 'Orientation' field on the left side of the window type 20, then click the Run button and allow
the simulation to run for at least 20 sec before you hit the Stop button to stop it. Be sure you
WRITE DOWN the average rate of firing for your records.

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Continue to modify the orientation parameter in intervals of 20° (0-done, 20-done, 40, 60, 80, 100,
120, 140, 160, 180), running each for at least 20 sec, until you complete the simulations of all ten
orientations (ending at 180°). Make sure you have a written record of all of your values.
Once you have compiled a set of orientation-response pairs you can plot them in Matlab. Go to the
MATLAB 7.5.0 (R2007b) window. In the Command Window section, input your responses as a
list (as shown below) at the prompt (>>). Make sure that your list of ten average firing rates is
contained in brackets, in order of orientation from 0° to 180°, each separated by a single space.
[Note: The values used in this tutorial are given as example only. Your data should be unique to
you. If you are using a digital copy of this tutorial, you may copy and paste the following command
into the Command Window prompt, but then you MUST be sure to swap out the sample values
for your own.] Hit Return on the keyboard after this command to allow Matlab to input your list.
response = [2.16 6.46 11.57 10.15 3.43 0.68 0.20 0.09 0.29 1.48]

FIGURE 22
Next you will need to plot an orientation tuning curve based on your results. In the Command
Window section, type the following command at the prompt (>>), then hit Return on the keyboard.
figure, plot(0:20:180,response,'b-o'); xlabel('Orientation (degrees)'); ylabel('Response (spikes/sec)')

FIGURE 23
A Figure window will appear, containing the orientation tuning curve of the cell you used. This is a
graph of your acquired responses (in spikes/sec) as a function of orientation of the grating (in
degrees).

FIGURE 24

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Put your name and a description of the graph in the Figure window. Go to Insert on the menu bar,
then select Title. In the white label box that appears at the top of the graph, type your name and
an appropriate description of the graph (you may use the description shown below), then click
outside the label (on any other part of the window) to save the changes. Make and save a copy of
the Figure window for your lab report along with an appropriate caption. Also provide a picture of
the receptive field of this cell.

FIGURE 25
In this example, the best orientation was around 40° since it yielded the highest average rate of
firing. Therefore one would label this the optimal (or preferred) orientation of the cell. The worst
orientation was around 140° since it yielded the lowest average rate of firing. Therefore one would
label this the null (or non-preferred) orientation. Notice that the optimal and null orientations are
approximately 90° apart from each other.
Go back to the receptive_field window. In the 'Orientation' field on the left side of the window type
the optimal orientation of your cell. In this example only, the optimal orientation is 40°. Your
optimal orientation may differ.
Measuring the spatial frequency tuning curve. Your second goal is to measure the spatial
frequency tuning curve of a cell while keeping the sinusoidal grating fixed at its optimal orientation.
This is a curve that shows the response of the cell (in spikes/sec) as a function of the...