Characterizing Neurons in the Primary 
Auditory Cortex of the Awake Primate 
Using Reverse Correlation 
R. Christopher deCharms 
decharms@phy. ucsf. edu 
Michael M. Merzenich 
merz@phy. ucsf. edu 
W. M. Keck Center for Integrafive Neuroscience 
University of California, San Francisco CA 94143 
Abstract 
While the understanding of the functional role of different classes 
of neurons in the awake primary visual cortex has been extensively 
studied since the time of Hubel and Wiesel (Hubel and Wiesel, 1962), 
our understanding of the feature selectivity and functional role of 
neurons in the primary auditory cortex is much farther from com- 
plete. Moving bars have long been recognized as an optimal stimulus 
for many visual cortical neurons, and this finding has recently been 
confirmed and extended in detail using reverse correlation methods 
(Jones and Palmer, 1987; Reid and Alonso, 1995; Reid et al., 1991; 
Ringach et al., 1997). In this study, we recorded from neurons in the 
primary auditory cortex of the awake primate, and used a novel re- 
verse correlation technique to compute receptive fields (or preferred 
stimuli), encompassing both multiple frequency components and on- 
going time. These spectrotemporal receptive fields make clear that 
neurons in the primary auditory cortex, as in the primary visual cor- 
tex, typically show considerable structure in their feature processing 
properties, often including multiple excitatory and inhibitory regions 
in their receptive fields. These neurons can be sensitive to stimulus 
edges in frequency composition or in time, and sensitive to stimulus 
transitions such as changes in frequency. These neurons also show 
strong responses and selectivity to continuous frequency modulated 
stimuli analogous to visual drifting gratings. 
1 Introduction 
It is known that auditory neurons are tuned for a number of independent feature 
parameters of simple stimuli including frequency (Merzenich et al., 1973), intensity 
(Sutter and Schreiner, 1995), amplitude modulation (Schreiner and Urbas, 1988), and 
Characterizing Auditory Cortical Neurons Using Reverse Correlation 125 
others. In addition, auditory cortical responses to multiple stimuli can enhance or sup- 
press one another in a time dependent fashion (Brosch and Schreiner, 1997; Phillips 
and Cynader, 1985; Shamma and Symmes, 1985), and auditory cortical neurons can 
be highly selective for species-specific vocalizations (Wang et al., 1995; Wollberg and 
Newman, 1972), suggesting complex acoustic processing by these cells. It is not yet 
known if these many independent selectivities of auditory cortical neurons reflect a 
discernible underlying pattern of feature decomposition, as has often been suggested 
(Merzenich et al., 1985; Schreiner and Mendelson, 1990; Wang et al., 1995). Further, 
since sustained firing rate responses in the auditory cortex to tonal stimuli are typ- 
ically much lower than visual responses to drifting bars (deCharms and Merzenich, 
1996b), it has been suggested that the preferred type of auditory stimulus may still 
not be known (Nelken et al., 1994). We sought to develop an unbiased method for 
determining the full feature selectivity of auditory cortical neurons, whatever it might 
be, in frequency and time based upon reverse correlation. 
2 Methods 
Recordings were made from a chronic array of up to 49 individually placed ultra- 
fine extracellular Iridium microelectrodes, placed in the primary auditory cortex of 
the adult owl monkey. The electrodes had tip lengths of 10-25microns, which yield 
impedance values of .5-5MOhm and good isolation of signals from individual neurons 
or clusters of nearby neurons. We electrochemically activated these tips to add an 
ultramicroscopic coating of Iridium Oxide, which leaves the tip geometry unchanged, 
but decreases the tip impedance by more than an order of magnitude, resulting in 
substantially improved recording signals. These signals are filtered from .3-8kHz, 
sampled at 20kHz, digitized, and sorted. The stimuli used were a variant of random 
Visual Cortex: Reverse Correlation Auditory Cortex: Reverse Correlation 
Using 2-D Visual Patterns In Time Using I-D Auditory Patterns (Chords) In Time 
t - Omsec t - 20msec t  40meec 
 t-Omeec t-20msec t-40msec 
x x 
Spatlotemporal Receptive Field Spectroternporal Receptive Field 
Figure 1' Schematic of stimuli used for reverse correlation. 
white noise which was designed to allow us to characterize the responses of neurons 
in time and in frequency. As shown in figure 1, these stimuli are directly analogous 
to stimuli that have been used previously to characterize the response properties of 
neurons in the primary visual cortex (Jones and Palmer, 1987; Reid and Alonso, 
1995; Reid et al., 1991). In the visual case, stimuli consist of spatial checkerboards 
that span some portion of the two-dimensional visual field and change pattern with 
a short sampling interval. In the auditory case, which we have studied here, the 
stimuli chosen were randomly selected chords, which approximately evenly span a 
126 R. C. deCharms and M. M. Merzenich 
portion of the one-dimensional receptor surface of the cochlea. These stimuli consist 
of combinations of pure tones, all with identical phase and all with 5 msec cosine- 
shaped ramps in amplitude when they individually turn on or off. Each chord was 
created by randomly selecting frequency values from 84 possible values which span 
7 octaves from 110Hz to 14080Hz in even semitone steps. The density of tones in 
each stimulus was 1 tone per octave on average, or 7 tones per chord, but the stimuli 
were selected stochastically so a given chord could be composed of a variable number 
of tones of randomly selected frequencies. We have used sampling rates of 10-100 
chords/second, and the data here are from stimuli with 50 chords/second. Stimuli 
with random, asynchronous onset times of each tone produce similar results. These 
stimuli were presented in the open sound field within an acoustical isolation cham- 
ber at 44.1kHz sampling rate directly from audio compact disk, while the animal sat 
passively in the sound field or actively performed an auditory discrimination task, 
receiving occasional juice rewards. The complete characterization set lasted for ten 
minutes, thereby including 30,000 individual chords. 
Spike trains were collected from multiple sites in the cortex simultaneously during the 
presentation of our characterization stimulus set, and individually reverse correlated 
with the times of onset of each of the tonal stimuli. The reverse correlation method 
computes the number of spikes from a neuron that were detected, on average, during 
a given time preceding, during, or following a particular tonal stimulus component 
from our set of chords. These values are presented in spikes/s for all of the tones 
in the stimulus set, and for some range of time shifts. This method is somewhat 
analogous in intention to a method developed earlier for deriving spectrotemporal 
receptive fields for auditory midbrain neurons (Eggermont et al., 1983), but previous 
methods have not been effective in the auditory cortex. 
3 Results 
Figure 2 shows the spectrotemporal responses of neurons from four locations in the 
primary auditory cortex. In each panel, the time in milliseconds between the onset of 
a particular stimulus component and a neuronal spike is shown along the horizontal 
axis. Progressively greater negative time shifts indicate progressively longer latencies 
from the onset of a stimulus component until the neuronal spikes. The frequency 
of the stimulus component is shown along the vertical axis, in octave spacing from 
a 110Hz standard, with twelve steps per octave. The brightness corresponds to the 
average rate of the neuron, in spk/s, driven by a particular stimulus component. 
The reverse-correlogram is thus presented as a stimulus triggered spike rate average, 
analogous to a standard peristimulus time histogram but reversed in time, and is 
identical to the spectrogram of the estimated optimal stimulus for the cell (a spike 
triggered stimulus average which would be in units of mean stimulus density). 
A minority of neurons in the primary auditory cortex have spectrotemporal recep- 
tive fields that show only a single region of increased rate, which corresponds to the 
traditional characteristic frequency of the neuron, and no inhibitory region. We have 
found that cells of this type (less than 10%, not shown) are less common than cells 
with multimodal receptive field structure. More commonly, neurons have regions of 
both increased and decreased firing rate relative to their mean rate within their re- 
ceptive fields. For terminological convemence, these will be referred to as excitatory 
and inhibitory regions, though these changes in rate are not diagnostic of an under- 
lying mechanism. Neurons with receptive fields of this type can serve as detectors 
of stimulus edges in both frequency space, and in time. The neuron shown in figure 
2a has a receptive field structure indicative of lateral inhibition in frequency space. 
This cell prefers a very narrow range of frequencies, and decreases its firing rate for 
nearby frequencies, giving the characteristic of a sharply-tuned bandpass filter. This 
Characterizing Auditory Cortical Neurons Using Reverse Correlation 127 
a) b) 
3 
2.5 
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3.5 
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2.5 
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1.5 
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-50 0 -1 O0 -50 0 
msec rnsec 
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Figure 2: Spectrotemporal receptive fields of neurons in the primary auditory cortex 
of the awake primate. These receptive fields are computed as described in methods. 
Receptive field structures read from left to right correspond to a preferred stimulus for 
the neuron, with light shading indicating more probable stimulus components to evoke 
a spike, and dark shading indicating less probable components. Receptive fields read 
from right to left indicate the response of the neuron in time to a particular stimulus 
component. The colorbars correspond to the average firing rates of the neurons in 
Hz at a given time preceding, during, or following a particular stimulus component. 
type of response is the auditory analog of a visual or tactile edge detector with lateral 
inhibition. Simple cells in the primary visual cortex typically show similar patterns 
of center excitation along a short linear segment, surrounded by inhibition (Jones 
and Palmer, 198?;'Reid and Alonso, 1995; Reid et al., 1991). The neuron shown in 
figure 2b shows a decrease in firing rate caused by a stimulus frequency which at a 
later time causes an increase in rate. This receptive field structure is ideally suited 
to detect stimulus transients, and can be thought of as a detector of temporal edges. 
Neurons in the auditory cortex typically prefer this type of stimulus, which is initially 
soft or silent and later loud. This corresponds to a neuronal response which shows 
an increase followed by a decrease in firing rate. This is again analogous to neuronal 
responses in the primary visual cortex, which also typically show a firing rate pat- 
tern to an optimal stimulus of excitation followed by inhibition, and preference for 
stimulus transients such as when a stimulus is first off and then comes on. 
The neuron shown in figures 2c shows an example which has complex receptive field 
structure, with multiple regions. Cells of this type would be indicative of selectiv- 
ity for feature conjunctions or quite complex stimuli, perhaps related to sounds in 
the animal's learned environment. Cells with complex receptive field structures are 
common in the awake auditory cortex, and we are in the process of quantifying the 
percentages of cells that fit within these different categories. 
Neurons were observed which respond with increased rate to one frequency range at 
one time, and a different frequency range at a later time, indicative of selectivity for 
frequency modulations(Suga, 1965). Regions of decreased firing rate can show similar 
patterns. The neuron shown in figure 2d is an example of this type. This pattern 
is strongly analogous to motion energy detectors in the visual system (Adelson and 
Bergen, 1985), which detect stimuli moving in space, and these cells are selective for 
changes in frequency. 
128 R. C. deCharms and M. M. Merzenich 
2 oct/sec 6 oct/sec 10 oct/sec 14 oct/sec 30 oct/sec 100 oct/sec 
.. '' 
-2 oct/sec -6 oct/sec -10 oct/sec -14 oct/sec -30 oct/sec -100 oct/sec 
Figure 3: Parametric stimulus set used to explore neuronal responses to continuously 
changing stimulus frequency. Images are spectrograms of stimuli from left to right in 
time, and spanning seven octaves of frequency from bottom to top. Each stimulus is 
one second. Numbers indicate the sweep rate of the stimuli in octaves per second. 
Based on the responses shown, we wondered whether we could find a more optimal 
class of stimuli for these neuron, analogous to the use of drifting bars or gratings in the 
primary visual cortex. We have created auditory stimuli which correspond exactly to 
the preferred stimulus computed for a particular cell from the cell's spectrotemporal 
receptive field (manuscript in preparation), and we have also designed a parametric 
class of stimuli which are designed to be particularly effective for neurons selective 
for stimuli of changing amplitude or frequency, which are presented here. The stimuli 
shown in figure 3 are auditory analogous of visual drifting grating stimuli. The 
stimuli are shown as spectrograms, where time is along the horizontal axis, frequency 
content on an octave scale is along the vertical axis, and brightness corresponds to the 
intensity of the signal. These stimuli contain frequencies that change in time along an 
octave frequency scale so that they repeatedly pass approximately linearly through a 
neurons receptive field, just as a drifting grating would pass repeatedly through the 
receptive field of a visual neuron. These stimuli are somewhat analogous to drifting 
ripple stimuli which have recently been used by Kowalski, et.al. to characterize the 
linearity of responses of neurons in the anesthetized ferret auditory cortex (Kowalski 
et al., 1996a; Kowalski et al., 1996b). 
Neurons in the auditory cortex typically respond to tonal stimuli with a brisk onset 
response at the stimulus transient, but show sustained rates that are far smaller than 
found in the visual or somatosensory systems (deCharms and Merzenich, 1996a). 
We have found neurons in the awake animal that respond with high firing rates and 
significant selectivity to the class of moving stimuli shown in figure 3. An outstanding 
example of this is shown in figure 4. The neuron in this example showed a very high 
sustained firing rate to the optimal drifting stimulus, as high as 60 Hz for one second. 
The neuron shown in this example also showed considerable selectivity for stimulus 
velocity, as well as some selectivity for stimulus direction. 
4 Conclusions 
These stimuli enable us to efficiently quantify the response characteristics of neu- 
rons in the awake primary auditory cortex, as well as producing optimal stimuli for 
particular neurons. The data that we have gathered thus far extend our knowledge 
about the complex receptive field structure of cells in the primary auditory cortex, 
Characterizing Auditory Cortical Neurons Using Reverse Correlation 129 
-2 oct/sec -6 oct/sec - 10 oct/sec - 14 oct/sec -30 oct/sec - 1 O0 oct/sec 
Figure 4: Responses of a neuron in the primary auditory cortex of the awake pri- 
mate to example stimuli take form our characterization set, as shown in figure 3. In 
each panel, the average response rate histogram in spikes per second is shown below 
rastergrams showing the individual action potentials elicited on each of twenty trials. 
and show some considerable analogy with neurons in the primary visual cortex. In 
addition, they indicate that it is possible to drive auditory cortical cells to high rates 
of sustained firing, as in the visual cortex. This method will allow a number of future 
questions to be addressed. Since we have recorded many neurons simultaneously, we 
are interested in the interactions among large populations of neurons and how these 
relate to stimuli. We are also recording responses to these stimuli while monkeys are 
performing cognitive tasks involving attention and learning, and we hope that this 
will give us insight into the effects on cell selectivity of the context provided by other 
stimuli, the animal's behavioral state or awareness of the stimuli, and the animal's 
prior learning of stimulus sets. 
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