What was the stimulus?
Lab time and location: 2/17/12, 9-11am in
Report due 2/24/12
The purpose of this laboratory exercise is to
illustrate how sound stimuli are coded in the temporal discharge patterns of
auditory-nerve fibers. The auditory
nerve is a good starting point for understanding the neural coding of sounds because responses are stable, well-characterized, and relatively easy
to interpret in terms of cochlear mechanisms.
The techniques you will be using in this lab are also applicable to
neurons in the central auditory system and other sensory systems.
Your task in this lab is very much like detective work. You are given several sets of spike (action
recorded from auditory-nerve fibers, and your task is to identify the sound stimulus
that produced each set of data. This
approach is chosen in part because it is fun, but also because this identification
task is similar to what the brain must do when decoding the pattern of auditory-nerve
activity produced by a sound stimulus. Taking the “brain’s point of view” encourages you to use
physiologically-realistic spike analysis
There are nevertheless two differences between your task and
the one the brain faces. One is that
the brain simultaneously processes information from all 30,000 auditory-nerve
fibers, whereas you are only provided with data from one fiber. Nevertheless, you will find that a surprising amount of
information about the stimulus can be gathered from the temporal
discharge patterns of even a single fiber.
Another difference is that, whereas the brain can normally identify an auditory
object or understand an utterance from a single presentation of a sound
stimulus, the same stimulus is presented many times in single-unit experiments
in order to obtain statistically-reliable estimates of the temporal discharge
patterns. This difference may not be
significant because the brain receives essentially the same information from
several auditory nerve fibers, at least the ~20 fibers which innervate a single
inner hair cell. Moreover, the responses of fibers
innervating adjacent hair cells are also very similar because the cochlear
filters are relatively broad. Thus, the temporal averaging (over stimulus
presentations) that you perform when analyzing single-unit firing statistics may
resemble the spatial averaging over multiple, similar auditory-nerve fibers
that the brain undoubtedly performs.
Histograms for analyzing neural responses
Because neural discharges (action potential or
"spikes") occur at discrete, punctate instants in time, histograms
are used to analyze and display the temporal discharge patterns.
Histograms estimate the distribution of spike times along a temporal
dimension which is
divided into small time intervals or "bins".
The course software supports more types of histograms
than you need to complete this lab. The four types you absolutely need to
understand are listed in the table and described further below. Additional
histograms that may also be useful are also described below.
Peri-Stimulus Time (PST)
Distribution of spikes over time course of stimulus
All stimuli (transient, periodic, stationary, time-varying)
Spectrum for transient and periodic stimuli
Spectrogram for time-varying stimuli
Distribution of intervals between successive spikes
Stationary and periodic stimuli, spontaneous activity
Distribution of spikes over stimulus cycle
Periodic stimuli with known period
Spectrum, Synchronization Index
Reverse Correlation (“Revcor”)
Average stimulus waveform preceding each spike
Noise token with known waveform
Peristimulus time (PST) histograms display
the distribution of spike times relative to the onset of each stimulus
presentation. They are often the most useful way to show temporal
discharge patterns. Useful bin widths range from 0.1 ms or less for
brief stimuli such as clicks to 10 ms or more for stimuli lasting several
(First-order) interspike interval histograms
display the distribution of time intervals between consecutive spikes.
They are most useful for spontaneous activity, and for periodic stimuli such
as pure tones, where they reveal the presence of phase locking.
Period (a.k.a. cycle) histograms show the
distribution of spike times over the period of a periodic stimulus.
They are useful to quantitatively assess the phase locking of spikes to the
stimulus. Specifically the synchronization index (a.k.a. vector
strength), a measure of the strength of phase locking, is the first Fourier
coefficient of the period histogram normalized by the mean discharge rate.
Unlike interval and autocorrelation histograms, however, period histogram
require the stimulus period to be known, which is not the case in this lab.
A good strategy is to first identify the unknown period using an interval or
autocorrelation histogram, then verify phase locking to this period using a
Reverse correlation ("Revcor")
functions represent the average stimulus waveform preceding each spike,
reversed in time. Mathematically, the revcor is equivalent to the
cross-correlation function between the stimulus waveform and the digitized
spike train. Revcor functions are most useful with broadband
("white") noise stimuli, where they provide an estimate of the
impulse response of the linear filter which does the best job (in a
least-squares sense) of predicting the neural response from the stimulus
(Wiener theorem). Revcor functions of low-frequency (< 4 kHz)
auditory-nerve fibers resemble the impulse response of a bandpass filter
centered at the fiber's characteristic frequency.
PST Raster (a.k.a. dot raster) histograms
show the distribution of spike times relative to stimulus onset along the
horizontal axis, and for each stimulus presentation along the vertical
axis. A PST histogram is the column-by-column sum of the PST
raster. PST rasters give a nice visual display of the raw spike data
very much as they are acquired during an experiment, and are useful for assessing the stability of the neural response over many repetitions of the
same stimulus. However, they are rarely used for quantitative analysis
of temporal discharge patterns. Appropriate bin widths for
PST rasters are the same as for PST histograms.
Autocorrelation (a.k.a. all-order interval)
histograms are similar to first-order interval histograms, except they
include intervals between non-consecutive spikes as well as intervals
between consecutive spikes. For example, if A, B, and C are the
occurrence times of 3 consecutive spikes, then a first-order interval
histogram would only include the intervals B―A and C―B, whereas an
autocorrelation histogram would include the second-order interval C―A as well. As
its name indicates, an autocorrelation histogram is mathematically
equivalent to the autocorrelation function of the digitized spike
train. Because the autocorrelation function reveals all the
periodicities in a signal, autocorrelation histograms are useful for
detecting periodicities in a spike train, including phase locking to a
stimulus with unknown period, as well as a neuron's intrinsic tendency to
fire at regular intervals.
Choosing the bin width
The bin width of a histogram must be carefully selected depending on
the stimulus, the amount of available data (number of spikes), and the type of
histogram. Choosing the wrong bin width may mask patterns that would be clearly
revealed with another bin width. Choosing a bin width is often a compromise
between temporal resolution and statistical reliability: Too large a bin width
smears the temporal pattern, while too low a bin width gives noisy histograms
where patterns are hard to discern. Some hints on choosing an appropriate bin
width are given below.
Histogram. A two step procedure is suggested.
Determine the stimulus
duration. The field Elapsed Time in the Histogram GUI gives the
total duration of the recording, while the field Number of Syncs gives
the number of stimulus presentations. Since the stimuli are presented at
regular intervals, the stimulus duration (including any silence between
stimulus presentations) is the ratio of the elapsed time to the number of
Match the histogram duration
to the stimulus duration. The histogram duration is the product of the
bin width by the number of bins. Adjust these two numbers (while keeping
their product equal to the stimulus duration) to get the best compromise
between time resolution and statistical reliability. In some cases, each
stimulus presentation is followed by a long silence, in which case the
histogram duration can be made shorter to zoom in on the actual stimulus.
Histogram. A bin width of 0.05 or 0.1 ms, together with a histogram
duration of 20-50 ms works well in most cases.
Period Histogram. The
bin width is the ratio of the period (which you specify) by the number of
bins. Make sure to have at least 20 bins per period, more if there is a large
number of spikes in the recording.
Revcor Histogram: The
bin width is automatically determined by the sampling rate of the stimulus
waveform, which is stored in a data file.
Unlike raw spike trains,
histograms are standard discrete-time signals that have a sampling rate equal to the inverse of the bin width. Therefore, they can be
processed with the powerful arsenal of digital signal processing techniques,
including linear filters and various transforms. Fourier analysis
techniques are particularly useful for revealing features of the data that are not
always apparent from time-domain histograms. For example, with some care,
the discrete Fourier transform of a PST, revcor, or interval histogram can show
the frequency components of the stimulus that a neuron phase locks to. For
time-varying stimuli, a spectrogram of the PST histogram shows how the frequency
components the fiber phase locks to evolve with time.
You are given 6 different Matlab data files named
after famous detectives (Clarice, Clouseau, Holmes, Marlowe, Poirot, and
Scully). Each file
represents spike data recorded from an auditory-nerve in response to one of 6 possible stimuli. Not all files represent data from the same
fiber, but all fibers had characteristic frequencies below
3000 Hz so as to exhibit clear phase locking to stimuli having energy near
their CF. The 6 stimuli are:
continuous, low-frequency pure tone.
A brief (100-μsec)
click presented at a rate of 10/sec.
Continuous, broadband noise (specifically, periodic
noise with a 5-sec period).
A sinusoidally frequency-modulated (FM) pure tone.
The tone's instantaneous frequency varies sinusoidally from a minimum to a
maximum and back.
A speech utterance
(about 3 sec).
Your task in this lab is to answer the following
the stimuli that produced the neural responses stored in each of the 6 data files.
the spontaneous activity, give the spontaneous discharge rate (in
spikes/sec) and estimate the fiber’s absolute refractory period.
the frequency of the pure tone. Also give the fiber's
synchronization index to the tone frequency.
the characteristic frequency (CF) of the fiber for which you have the click
response. Also estimate the cochlear traveling wave delay.
the CF of the fiber for which you have the response to broadband noise. Hint: Compute
the reverse correlation ("revcor") between the noise signal and the neural response. For this purpose, the noise waveform is
available in a Matlab file.
the FM tone, give the modulation range, i.e. the minimum and maximum
instantaneous frequencies and the modulation frequency. Also give a
crude estimate of the fiber CF in relation to the minimum and maximum
frequencies, i.e. is the CF above the maximum frequency, below the minimum,
or between the minimum and the maximum.
whether the speech utterance was produced by a male or a female speaker
and give the number of vowels in the utterance.
The file "warshawski" contains the neural response to yet another
stimulus. What is it?
– just for fun. For each famous
detective give either the author of the book or the name of the film or TV
series in which he/she first appeared as a character.
Include answers to all the questions in
your report. Explain the clues you use for
stimulus identification, and how the different
clues fit together. Several lines
of arguments leading to the same conclusion are better than one. Include all relevant plots and calculations. No credit will be given
for answers lacking a justification.
Do not repeat the lab instructions and
avoid lengthy introductions.
Your report should not exceed 4
single-spaced pages, not including figures which can be either interleaved
with the text or attached at the end.
Each figure should be numbered and
have a label.
Conclude your report with a
one-sentence summary of what you learned in the lab.
Please include any suggestions you may have on how to improve the
Histogram GUI for both ease of use and power.
You are expected to share data
and figures with your lab partner, and encouraged to discuss all aspects of the
lab with each other. However, you
must write the lab report on your own, using your own words.
After you log in onto an Athena workstation, type attach hst.723
into a shell window, then start Matlab
from the Athena menu. Run the Matlab
In the Matlab command window, enter addpath(genpath('/mit/hst.723/matlab')).
you access to the course software and data files. Alternatively, you can set the path with a GUI available from the
File menu in the Matlab desktop.
Enter HistogramGUI into the Matlab
command window. This starts the graphical user interface you will be
using to compute and plot histograms from stored neural data.
The Histogram GUI
The histogram GUI allows you to specify settings for two different histograms
called "A" and "B" that are plotted in a separate figure
window. Use the tabs to switch between Histograms A and B. Each item
in the histogram panel is described from top to bottom. Certain items are
only applicable to specific types of histograms, and are only visible when
The following items specify what the histogram computes:
|Data File. Used to select the neural data file you are
analyzing. The files are located in /mit/hst.723/matlab/LabANF/Data.
Use the Browse button to find the file. Once you select the
file, two histograms are automatically plotted in a new figure window based
on the panel settings below. |
|Type. Type of histogram. See the Histograms
section above for description of the most useful types.|
|Size. Number of bins in the histogram. For normal (1-D)
histograms enter a single integer. For 2-D histograms (such as PST
Rasters), enter two
integers separated by a space to specify the numbers of rows and columns in
the matrix, in that order. |
|Bin Width(s). Must be a positive number. See the Histograms
section above for tips on how to set the bin width. This argument is
ignored for period histograms. The product of the bin width and the number
of bins is the total duration of the histogram. For interval
histograms, a 20-40 ms duration usually works well. See description of Elapsed Time below for tips on how to set the
duration of a PST histogram.|
|Offset(s). Usually set to zero. Non-zero offsets shift
the time origin and are useful for zooming in on a temporal segment of a long
histogram with high resolution. |
|Sync and Spike Channels should always be 0 and 1, respectively.|
|Period. Used for period histograms only. Sets the
stimulus period in ms. The bin width is automatically set to the
period divided by the number of bins.|
|Interval Order. Used with interval histograms only.
Should always be 1.|
|Stimulus. Used with Revcor functions only. Specifies
the stimulus waveform that is averaged before each spike. Use the Browse
menu to find the stimulus file located in the same folder as the neural
The following items are read only. They provide information about the
data contained in the analyzed file:
|Number of Syncs. Gives the number of
|Number of Spikes. Gives the number of
spikes in the recording. |
|Elapsed Time. Total duration of the
recording in sec. The number of spikes divided by the elapsed time
gives the average discharge rate in spikes/sec. The elapsed time
divided by the number of syncs (stimulus presentation) gives the average
duration of each stimulus presentation, which is useful to set the number of
bins in a PST histogram.|
The following items affect only how the histogram is displayed:
|Plot Style. For 1-D histograms, specifies if the histogram is
plotted as vertical bars (usually the best) or as continuous lines (good for
Revcors). For 2-D histograms, specifies whether the histogram is
plotted as an image (usually the best), a set of dots (traditional for PST
rasters), or "waterfall" displays (not useful here).|
|Plot threshold. Only used for plotting 2-D histograms as
dots. Specifies the number of spikes per bin that must be exceeded for
a bin to be plotted as a dot. A zero threshold usually works well.|
|Plot Scale. Applies to 1-D histograms only. Specifies
if the ordinate represents the number of spikes per bin ("count")
or to discharge rate in spikes/sec ("rate").|
|Apply. Not useful in this lab.|
|Compute. Compute and display a histogram in a figure window
based on current settings. If the settings are incomplete or
inappropriate, an error message will appear in the Matlab command window.|
|Gate. Displays the "Histogram Gate" pop-up
menu. Gates are used to select a subset of the spikes for including into
a histogram computation. Not necessary in this lab.|
|Spectrum. Compute and display the Fourier magnitude spectrum
of the current histogram. |
|Spectrogram. Compute and display the spectrogram (short-time
Fourier transform) of the current histogram. The spectrogram is
displayed as an image, with time on the horizontal axis and frequency on the
vertical axis. A 20-ms analysis window is used to compute the
|Synchrony. Used with period histograms only. Computes
the synchronization index to the stimulus period, and displays the result in
the adjacent box. |
While the Histogram GUI
is easy to use, the numerous options, combined with the unknown nature of the
stimulus, may be somewhat baffling at first. Here are some hints on how to
Start with a PST histogram.
See the section "Choosing the bin width" to select an appropriate
bin width and number of bins.
For interval histograms,
start with 200 bins and a 0.1 ms bin width. Any
phase locking should be apparent as regularly-spaced modes in the
histogram. Decrease the bin width (and increase the number of bins) if
the histogram is not too noisy.
not even try a period histogram until you are confident that the
stimulus is periodic and you have identified the likely period.
and autocorrelation histograms are only useful for
"stationary" stimuli, whose spectrum does not change with
time. For non-stationary stimuli such as speech, use
a PST histogram computed with fine time
functions are only useful for the noise
stimulus. Start with 200 bins. The bin width is automatically
set to the inverse of the noise sampling rate (20 kHz).
For Matlab experts
Histograms are discrete-time signals that can be analyzed
using standard digital signal processing (DSP) techniques. If you are
familiar with both Matlab and DSP, you can access the data produced by the
Histogram GUI and apply further processing beyond that available in the GUI.
|Type h=HistogramGUI('GetHist'); into the
Matlab command window to get the current histogram. The Matlab
variable h is a special histogram object
containing all the histogram settings and data.|
|To access the data in the histogram, type data=double(h);
This returns a vector or a matrix for 1-D and 2-D histograms, respectively,
and containing the number of spikes in each bin.|
|To get the bin width, type bw=get(h,'BinWidth')
for 1-D histograms, or bw=get(h,'BinWidths')
for 2-D histograms.|
|You can also access many other histogram properties. Type help
@histogram/get for further information.|
The files containing the neural data are, in alphabetical
order: clarice.mat, clouseau.mat, holmes.mat, marlowe.mat, poirot.mat, and
Each file contains the response of one fiber to one stimulus as
Neural responses to each stimulus are stored as a pair of vectors
called t and ch (Figure 1). The vector t represents the times of
recorded events (either neural spikes or stimulus onsets) expressed in msec
since the beginning of recording. The
second vector ch (for channel) identifies each event as either a
stimulus onset (0) or a neural spike (1).
The file bbnoise_signal.mat contains the waveform
of the continuous broadband noise stimulus and the noise
sampling rate Fs in Hz. These
are essential for computing the revcor function.
You may ftp the data files and lab software to your
own computer. They are in /afs/athena.mit.edu/course/other/hst.723/matlab/LabANF.
Figure 1. Construction of a PST raster from stored