Measuring Sparseness in the Brain: Comment on Bowers (2009)
Rodrigo Quian Quiroga
University of Leicester
Children’s Hospital Boston, Harvard Medical School,
and Harvard University
Bowers (2009) challenged the common view in favor of distributed representations in psychological
modeling and the main arguments given against localist and grandmother cell coding schemes. He
revisited the results of several single-cell studies, arguing that they do not support distributed represen-
tations. We praise the contribution of Bowers (2009) for joining evidence from psychological modeling
and neurophysiological recordings, but we disagree with several of his claims. In this comment, we argue
that distinctions between distributed, localist, and grandmother cell coding can be troublesome with real
data. Moreover, these distinctions seem to be lying within the same continuum, and we argue that it may
be sensible to characterize coding schemes with a sparseness measure. We further argue that there may
not be a unique coding scheme implemented in all brain areas and for all possible functions. In particular,
current evidence suggests that the brain may use distributed codes in primary sensory areas and sparser
and invariant representations in higher areas.
Keywords: grandmother cell, visual perception, memory, sparseness, neural coding
Supplemental materials: http://dx.doi.org/10.1037/a0016917.supp
Understanding the principles of how the brain is capable of
different functions arguably constitutes one of the greatest scien-
tific challenges of our times. Such an enterprise requires a com-
bined effort across diverse disciplines, such as neuroscience, biol-
ogy, computer science, psychology, philosophy, and physics, to
name only a few. Along these lines, the recent contribution of
Bowers (2009) should be praised for its significant attempt to put
together knowledge derived from neurophysiological recordings,
computational models, and psychology. In this comment, we dis-
cuss a few ideas to clarify some of the neurophysiological concepts
addressed in Bowers’s (2009) review. In particular, we emphasize
the technical difficulties in addressing questions about the coding
of information by neurons based on single-cell recordings and
discuss how these experimental constraints affect claims of dis-
tributed, sparse, and grandmother-cell representations.
One of the most striking facts in visual perception is how, in a
fraction of a second, the brain can make sense of very rich sensory
inputs and use this information to create complex behaviors. It is
perhaps the easiness with which such functions are performed that
may make people typically unaware of the exquisite machinery in
the brain required for such computations. People may be amazed
at realizing that they can solve a Rubik’s cube or beat a master in
a chess game but are hardly surprised when performing something
as complex as recognizing a familiar face in a crowd. A key
question to understand how the brain processes information is to
determine how many neurons, in a given area, are involved in the
representation of a visual percept (or a memory, a motor com-
mand, etc.) and what information each of these neurons encodes
about the percept. On the one hand, the representation of a percept
could be given by the activity of a large population of neurons. In
this case, the percept emerges from the ensemble response and
cannot be understood by inspecting the responses of individual
neurons without considering the whole population. On the other
hand, the percept might be represented by very few and more
abstract cells, with each of these cells giving explicit information
about the stimulus. In neuroscience, the first scenario is usually
referred to as distributed population coding and the second one is
usually referred to as sparse coding, its extreme case—of having
one neuron coding for one percept—usually referred to as grand-
mother cell representation (but note that the term grandmother cell
can also be taken as meaning many neurons encoding for one
percept or just meaning an abstract representation). We anticipate
that these definitions may be imprecise (as noted by Bowers, 2009)
and that the same terms may be used with different meanings by
different communities of researchers. For example, we already
mentioned different uses of the term grandmother cell. Moreover,
for Bowers (2009), sparse codes are a form of distributed codes
and in neuroscience these two types of coding are taken as the
opposite. To avoid confusion, in the following we refer to distrib-
uted and localist codes, following Bowers’s (2009) notation. It is
indeed the vagueness and different meaning of these definitions
that give rise to some of the discussions in the field.
A central theme in the discussion by Bowers (2009) concerns
the biological plausibility of localist models. To address this ques-
tion, he referred to evidence from single-cell recordings, the gold
Rodrigo Quian Quiroga, Department of Engineering, University of
Leicester, Leicester, England; Gabriel Kreiman, Division of Neuroscience
and Ophthalmology, Children’s Hospital Boston, Harvard Medical School,
and Center for Brain Science, Harvard University.
We thank Simon Thorpe, David Plaut, and Jeff Bowers for very useful
comments and feedback. We also acknowledge support from Engineering
and Physical Sciences Research Council, Medical Research Council, and
the Royal Society.
Correspondence concerning this article should be addressed to Rodrigo
Quian Quiroga, Department of Engineering, University of Leicester, LE1
7RH, England. E-mail: firstname.lastname@example.org
2010, Vol. 117, No. 1, 291–299
© 2010 American Psychological Association
standard to elucidate the function of neural circuits. Such parallels
between neurophysiology and psychological models could have a
major impact in both fields. It is in this spirit that we aim to contribute
to this discussion by adding to and commenting on Bowers’s (2009)
claims from the perspective of neurophysiologists trying to extract
this type of information from single-cell recordings.
Defining Distributed and Local (or Sparse) Coding
Bowers (2009) defined distributed codes as a representation in
which each unit is involved in coding more than one familiar
“thing,” and consequently, the identity of a stimulus cannot be
determined from the activation of a single unit (Bowers, 2009; our
emphasis on “thing”). Moreover, he distinguishes between dense
distributed representations, that is, distributed coding schemes in
which each neuron is involved in coding many different things, as
commonly associated with parallel distributed processing (PDP)
theories in psychological modeling (McClelland, Rumelhart, &
Group, 1986; Rumelhart, McClelland, & Group, 1986)—for re-
lated ideas in theoretical neuroscience, see Hopfield (1982,
2007)—and coarse coding schemes, that is, distributed codes in
which single neurons have broad tuning curves, such that a single
neuron codes for a range of similar things. Although a broadly
tuned neuron may respond most strongly to a preferred stimulus,
noise would preclude identifying the stimulus precisely from the
single-cell activity (Bowers, 2009). In contrast to distributed
codes, according to Bowers (2009), a localist representation is
characterized by neurons coding for one thing, for which it is
possible to infer a stimulus from the activation of a single unit. In
between localist and coarse coding schemes, Bowers (2009) intro-
duced one more term, sparse distributed coding, but it seems that
these definitions lay within the same continuum, and the distinc-
tion between a localist and a sparse distributed code is just given
by the number of objects encoded by a neuron.
The above definitions seem at first plausible, but the distinction
among them becomes fuzzy when considering neurophysiological
recordings. We should first mention that the distinction between
distributed and coarse coding appears to be in how similar the
neuronal preferences are. Defining similarity in a rigorous way is
already quite a complex challenge in itself. For example, we can
loosely imagine that a front view of a face is similar to a profile
view of the same face and is very different from a front view of a
different face. However, such a statement is quite arbitrary: At the
pixel level, the similarity between front views of two different
faces is much larger than the similarity between a front view and
a profile view of the same face. This is far from a trivial consid-
eration: Achieving a good definition of what humans consider
similar things constitutes a central challenge in computer vision,
neuroscience, and psychology. A second problem with these def-
initions, and perhaps a more fundamental one, is given by the
ambiguity of what is meant by thing. A thing could be a face, a car,
or an animal but could also be a pixel, an oriented bar, or an
abstract concept. How thing is defined may radically alter our
conclusions regarding how distributed or local a neural coding is.
For example, a neuron in V1 may have a local representation for
oriented bars in their receptive fields but, at the same time, a
distributed representation for faces. To address this problem, Bow-
ers (2009) argued that we cannot think of a distributed represen-
tation of a complex familiar thing (e.g., a face) at a low level of the
system (e.g., the retina or V1) because the retina does not know
that there is a face. This dichotomy is usually referred to as implicit
versus explicit representation. The retina encodes information
about the face in an implicit manner (it seems farfetched to argue
that the retina does not encode the visual information at all!). In
contrast, the representation of the face at the level of the temporal
lobe becomes explicit, in the sense that single-cells can give us
reliable information about the presence or absence of a face. To be
more precise, an explicit representation can be defined as the one
in which the information can be decoded by a single layer network
Given the activity of a single V1 neuron, we can discriminate
the presence or absence of an oriented bar within the receptive
field well above chance, but we cannot tell whether a particular
face is present because this information is not explicit at the level
of V1. But even when considering only oriented bars, should an
oriented bar at 49° constitute a different thing compared with an
oriented bar at 50 degrees? How many degrees of separation is
required before an oriented bar becomes a new thing? The con-
tinuum nature of orientation makes this distinction difficult. In
higher visual cortex, it is also possible that there exists a similar
continuum of features to which neurons respond, only that it is in
general difficult to assess what those features are (Connor, Brincat,
& Pasupathy, 2007; Tanaka, 1996). This distinction is even harder
for areas such as the hippocampus, where a neuron could fire
preferentially to the different views of the tower of Pisa and the
Eiffel Tower, and another one could fire preferentially to different
pictures of Jennifer Aniston and Lisa Kudrow (both actresses of
the TV series Friends), see Figures S6 and S7 in Quian Quiroga,
Reddy, Kreiman, Koch, and Fried, 2005. Clearly, these responses
are related at some high level of abstraction, which seems plausible
given the role of hippocampus in coding associations (Miyashita,
1988; Wirth et al., 2003). However, it is unclear how different
these concepts are or whether they should be considered as the
same thing (landmarks of Europe in the first case, and the two
actresses of Friends in the second one).
Another problem with these definitions is that in real life,
identifying the stimulus encoded by the neural activity involves
setting a responsiveness criterion for defining what is a significant
response and what is not, which of course depends on the partic-
ular criterion chosen. Alternatively, it is also possible to use
decoding algorithms or information theory formalism to extract
information about the stimulus from the neural responses (Abbott,
1994; Quian Quiroga & Panzeri, 2009; Rieke, Warland, de Ruyter
van Steveninck, & Bialek, 1997). But this can be also problematic
for the above definitions because, due to trial-to-trial variability,
noise, lack of enough number of trials, and so on, decoders or
information theory do not provide yes–no answers but do provide
estimations of performance or amount of information.
To avoid defining what is a thing and whether two stimuli are
similar, it seems to us preferable to simplify the nomenclature by
describing a continuum with dense distributed representations at
one end and localist representations at the other. Central to this
discussion is to determine where neuronal representations reside
within this continuum, something that can be quantified with a
sparseness measure like the one to be discussed in the following
sections. Then, a high degree of sparseness will imply a local
coding and, conversely, a low degree of sparseness will be evi-
dence for a distributed representation.
Neural Responses and Neural Codes
Bowers (2009) discussed the interactive activation (IA) model
of visual word recognition (McClelland & Rumelhart, 1981;
Rumelhart & McClelland, 1982) to distinguish between what a
neuron responds to and what a neuron codes for. In his example
(see Figure 4 in Bowers, 2009), a unit at the top level of the IA
model responds to both blur and blue due to the similarity between
these two stimuli: They share the first 3 letters and differ only in
the last one. However, he argued that this particular unit only
codes for blur by construction. According to Bowers (2009), in
this case the neuron shows a localist code because, although it
responded to two things, the neuron encoded the meaning of only
one of them. He therefore claims that responses to multiple objects
do not provide evidence of a distributed representation. But this
argument has some problems. For example, suppose that the same
network is used with a new set of words, containing blue but not
blur. The neuron will consistently fire to blue, and in fact, from the
firing of this neuron one may accurately predict the presence of
this word. Should one then say that in spite of such an explicit
representation the neuron does not code for blue, given that it was
trained to code for a similar word in the first place? Unless we
define coding from how a network is particularly trained rather
than from the meaningful information that can be obtained from it,
to us the answer is no. These distinctions become even more
problematic with real neuronal activity because we do not have
direct access to what a neuron codes for, but rather to what it
responds to. In other words, if a neuron responds to more than one
thing, how could one know which response is meaningful and
which one is not? Moreover, if we extrapolate Bowers’s (2009)
argument based on the IA model, we could easily conclude that
every single neuron in the brain is only coding for one thing: When
the neuron responds to many things, it could simply be stated that
the neuron surely prefers only one thing and that the neuron merely
responds to the other things due to similarity. What would then
constitute evidence for a distributed representation (but a neuron
responding to multiple things)? In other words, how can one falsify
a localist coding scheme if the evidence from neurons responding
to multiple things is not accepted? In fact, it seems implausible to
argue that there is no evidence for distributed coding because it is
not known whether one should ignore most of the responses. For
any definition of localist and distributed coding, it is important to
specify what type of evidence would support or falsify each type
of code. Below, we propose to characterize neuronal responses
quantitatively by a single degree of sparseness. Although many of
our caveats described above still remain under this simple ap-
proach, this quantitative definition allows us to provide support to
verify or falsify both distributed and localist representations.
Given the problems highlighted in the previous sections, it seems
preferable to refer to distributed and local (or sparse) responses, with
the understanding that neuronal responses constitute a proxy for
neuronal codes. In order to quantify the distinction between localist
and distributed responses, we need to be able to measure the degree of
sparseness of single-cell activations in a reliable way. Figures 1A and
the same microwire, whose activity could be separated after spike
sorting (Quian Quiroga, 2007; Quian Quiroga, Nadasdy, & Ben-
Shaul, 2004). Both units are nearly silent during baseline (average ?
0.01 spikes/sec) and fired with up to 40 spikes/sec to only a few of the
114 pictures shown in this recording session. The first unit responded
to two basketball players, and the second one responded to two
landmark buildings. Due to space constraints, only 10 responses are
shown. There were no responses to the other pictures not shown.
From Figures 1A and 1D the high degree of selectivity of these
neurons is clear, but how can we measure sparseness? There are
two notions of sparseness in the literature: (a) population sparse-
ness refers to the fraction of neurons of a population that respond
in a given time window and (b) lifetime sparseness refers to the
relative number of stimuli to which a neuron responds to (Ol-
shausen & Field, 2004; Quian Quiroga, Reddy, Koch, & Fried,
2007; Willmore & Tolhurst, 2001). These two notions are related
because one expects that if a cell fires to few stimuli then each
stimulus will be encoded by a relatively small population of cells.
However, it is in principle possible that most cells in a given
population respond to one stimulus (or a few stimuli) or that a
small subset of neurons is very promiscuous, responding exuber-
antly to many stimuli. Due to technical difficulties involved in
recording simultaneously from large numbers of neurons, most
studies usually assess lifetime sparseness, assuming that it will be
similar to population sparseness. In this context, lifetime sparse-
ness, also termed selectivity or specificity, means that a given cell
responds only to a small subset of the presented stimuli. On the
contrary, if a neuron responds to many stimuli, it is said to be
broadly tuned, pointing toward a distributed representation.
The notion of sparseness—and any measure to quantify it—
depends on the stimulus set. In particular, the units of Figure 1A
and 1D have sparse responses because they were activated only by
very few of the more than 100 pictures presented. However, it is
conceivable that a lower degree of sparseness would have been
obtained for the unit in Figure 1D if more views of landmarks (and
in particular of the tower of Pisa) had been used. To give a more
clear (and extreme) example, if one neuron responds to many
different faces, as in the monkey inferior temporal cortex (IT;
Gross, 2008; Gross, Rocha-Miranda, & Bender, 1972; Hung,
Kreiman, Poggio, & DiCarlo, 2005), it would appear to respond in
a highly sparse manner if the stimulus set contains only one face
and a large number of other stimuli. This seemingly trivial point
makes it difficult to compare the degree of sparseness in different
areas because different stimulus sets are typically used.
The simplest measure of sparseness would be to report the
relative number of stimuli eliciting significant responses in a
neuron. However, this number depends on the criterion used for
defining what is a significant response and what it is not. In
particular, if a very strict threshold is used, then only the few
largest responses will cross this threshold and, consequently, this
neuron will appear to be sparse. To overcome this dependence, a
novel sparseness index (S) was introduced (Quian Quiroga et al.,
2007) by plotting the normalized number of responses as a func-
tion of a threshold (Figures 1C, 1F). One hundred threshold values
between the minimum and the maximum responses were taken,
and for each threshold value, the fraction of responses above the
threshold was computed. The area under this curve (A) is close to
zero for a sparse neuron and is close to .5 for a uniform distribution
of responses (dotted line in Figures 1C and 1F). The sparseness
index was defined as S ? 1 – 2A. It is 0 for a uniform distribution
(a dense representation), and it approaches 1 the sparser the neuron
is (for a localist representation). S values in Figures 1C and 1F (.97
in both cases) confirm that the sparseness of these neurons is not
just the consequence of the arbitrary choice of a very large thresh-
old. Sparseness values for a large population of medial temporal
lobe (MTL) neurons have been reported in Quian Quiroga et al.
Related to the discussion of how localist (sparse) or distributed is
the representation of neurons in a given area, it should be noted that
highly selective neurons, like the ones presented in Figures 1A and
1D, are hard to detect without optimal data processing and recording
conditions. This basically relies on (a) the recording of broad band
continuous data allowing off-line analysis, (b) the use of an optimal
spike detection and sorting algorithm, and (c) the use of semichronic
multiple electrodes in contrast to traditional single electrode record-
ings. In fact, single electrode recordings are usually carried out with
movable probes that tend to miss sparsely firing cells—that are quiet
when the electrode passes by their vicinity unless the right stimulus is
shown—and are more likely to record the activity of neurons with
high spontaneous rates and broadly tuned responses. This introduces
a bias toward distributed representations, which is likely to be prev-
alent in multiple descriptions of apparently distributed representations
R. Quian Quiroga, L. Reddy, C. Koch, and I. Fried, 2007, Journal of Neurophysiology, 98, p. 2001. Copyright
2007 by The American Physiological Society. Used with permission. A, D: Ten largest responses of two
simultaneously recorded single units in the right posterior hippocampus. There were no responses to the other
104 pictures shown to the patient. For each picture the corresponding raster plots (middle subplots; first trial on
top) and poststimulus time histograms with 100 ms bin intervals (lower subplots) are given. Highlighted boxes
mark significant responses. The vertical dashed lines indicate the times of image onset and offset, 1 s apart. Note
the marked increase in firing rate of these units roughly 300 ms after presentation of the responsive pictures. B,
E: Median number of responses (across trials) for all the pictures presented in the session. C, F: Relative number
of responses as a function of the variable threshold (see text). Note the high selectivity values for both units (S ?
0.97), thus implying a sparse representation. For copyright issues, the thumbnail pictures displayed in the figure
corresponds to similar views of the same persons or objects used in the experiment. A color version of this figure
is available on the Web at http://dx.doi.org/10.1037/a0016917.supp
Adapted from “Decoding Visual Inputs From Multiple Neurons in the Human Temporal Lobe,” by
in the literature. This issue is becoming quite relevant given recent
evidence of highly sparse neurons in different systems (for a review
see Olshausen & Field, 2004). For example, Perez-Orive and cowork-
ers (Perez-Orive et al., 2002) using multielectrode recordings found
cells in the mushroom body of the locust with a baseline activity of
about 0.025 spikes per second, which fired about 2 spikes to very few
odors. Hahnloser and coworkers (Hahnloser, Kozhevnikov, & Fee,
2002) found ultra-sparse firing neurons in the songbird using anti-
dromic stimulation. These neurons had less than 0.001 spikes per
second baseline activity and elicited bursts of about 4 spikes when the
bird sang one particular motive. As shown in Figure 1, neurons in the
human MTL can have a baseline firing of less than 0.01 Hz and
respond with up to 50 Hz to very few stimuli.
Evidence for Local and Distributed Codes in the Brain
In his overview, Bowers (2009) revisited neurophysiology evi-
dence of sparse and distributed representations and reinterpreted these
works as evidence for localist and grandmother cell codes. He par-
ticularly referred to the recordings in macaque monkeys by Young
and Yamane (1992), saying that these authors claimed to have pro-
vided evidence for a distributed code. It seems that Bowers’s (2009)
criticism of this article is due to the different meanings given to some
terms by different communities of researchers. In fact, Young and
Yamane argued for a sparse representation (in the sense of being
opposite to distributed, as generally taken in neuroscience), which is
already in the title of their well known Science article “Sparse Pop-
ulation Coding of Faces in the Inferior Temporal Cortex” (Young &
Yamane, 1992). What may be confusing is the fact that they also
referred to population coding, but this is just reflecting the fact that
even with sparse responses, a population of neurons—in contrast to a
single-cell—is needed to encode a percept.
Hung and coworkers (Hung et al., 2005) showed that neurons in
monkey IT respond to multiple images (see also Kreiman et al.,
2006). They used a statistical classifier to decode the activity of an
ensemble of hundreds of neurons. Bowers (2009) argued that the
classifier units coded for only one object and concluded that the
data do not support distributed coding arguments. Here, it is
important to distinguish between the experimental data (the re-
cordings of neurons in IT cortex) and the classifier units. The units
in IT cortex responded to multiple objects, and it was not possible
to decode the presence of individual objects with high accuracy
from only one neuron. In contrast to the case of IT neurons (the
physiological data), the classifier units that operated on the output
of hundreds of IT neurons showed sparser responses. But this does
not provide any direct evidence that such a code exists in the brain,
as the classifier is a theoretical construct. Further support to the
claim of distributed coding by these neurons is given by the fact
that decoding performance increased nonlinearly with the number
of neurons. For a pure localist code, each neuron contributes to
identify one or a few objects, and therefore, the decoding perfor-
mance or alternatively the capacity—that is, the number of objects
that can be identified at a fixed performance level—grows linearly
with the number of neurons, as it is the case for recordings in the
human MTL (Quian Quiroga et al., 2007). On the contrary, for a
distributed code, each neuron contributes to the representation of
many objects, and both decoding performance and capacity have a
nonlinear growth with the number of neurons, as observed in IT
recordings in monkeys (Hung et al., 20051). In fact, it is in
principle possible to encode 2Nobjects with a fully distributed
network of N binary neurons, but it has to be noted that the exact
nonlinear functional dependence with the number of neurons de-
pends on several factors, such as noise levels, trial-to-trial vari-
ability, and saturation of decoding performance due to limited
sampling of stimuli (Abbott, Rolls, & Tovee, 1996). In this respect,
Bowers (2009) claimed that the exponential increase of decoding
performance with the number of neurons found by Hung and
colleagues (2005; and also by Rolls and colleagues Rolls, Treves,
& Tovee, 1997, as described in the next paragraph) does not
constitute evidence of distributed representations. This argument
brings us back to the previous discussion of how to experimentally
establish what a neuron codes for, given what it responds to. In our
exponential increase of performance with the number of neurons)
gives strong evidence for distributed coding. Again, the claim that
these neurons may encode only one thing and that they fire to the
other ones by mere similarity (as in Bowers’s, 2009, argument of the
IA model) is of limited relevance because it cannot be verified or
falsified with the existing data and recording tools.
Further evidence for distributed representations in visual pro-
cessing areas comes from the recordings of Rolls and coworkers
(Rolls et al., 1997), showing also an exponential increase in
decoding performance with the number of neurons (see also
Abbott et al., 1996). Bowers (2009) criticized these results because
(a) the study was carried out on a set of face cells that were not
highly selective and (b) the same analysis carried out on our MTL
neurons would likely lead to a different conclusion. If Rolls and
colleagues had recorded data from different areas, results may
have been different because different areas may represent infor-
mation in a different way. However, we do not see this as a
problem with the experiment or the approach taken by these
authors, as claimed by Bowers (2009). Rolls and colleagues re-
ported observations based on the area they recorded from and did
not generalize their claims to other areas. In fact, they explicitly
mentioned that this encoding may be different in other parts of
cortex and for other category of visual stimuli (Rolls et al., 1997).
It is interesting to note that a similar decoding analysis was indeed
carried out with our selective responses in the human MTL (Quian
Quiroga et al., 2007). In contrast to the findings of Rolls et al.
(Rolls et al., 1997) and Hung et al. (Hung et al., 2005), in this case,
the decoding performance increased linearly rather than exponen-
tially, in agreement with a very sparse or localist coding scheme.
An extreme example of sparse coding is given by the single-cell
responses to picture presentations in the human MTL, as showed
in the examples depicted in Figure 1. In spite of the striking degree
of sparseness of these neurons, we argue that they cannot be taken
as conclusive evidence of the existence of grandmother cells—
understood in the sense that one neuron encodes only one object
(Quian Quiroga, Kreiman, Koch, & Fried, 2008; Quian Quiroga et
al., 2005). First, if there were one and only one neuron encoding
for a given person or object, the chance of finding this neuron
would be tiny. Second, the fact that a neuron responds to only one
person during an experimental session is not a proof that the
neuron encodes only for this person because it may also fire to
1See Point 15 in http://klab.tch.harvard.edu/resources/ultrafast/
some other stimuli that we did not happen to show during the
recording. It is indeed not uncommon to find very selective MTL
neurons firing to more than one person or object, as shown in
Figure 1 (for more examples see Quian Quiroga et al., 2005).
From a dataset of 1,425 MTL units recorded in 34 experimental
sessions, given the number of responsive units in a recording
session, the number of stimuli presented, and the total number of
recorded neurons, using probabilistic arguments we estimated that
from a total population of about 109neurons in MTL, less than 2 ?
106neurons (not 50–150 as incorrectly reported by Bowers, 2009)
are involved in the representation of a given percept (Waydo,
Kraskov, Quian Quiroga, Fried, & Koch, 2006). Furthermore,
assuming that a typical person can recognize between 10,000 and
30,000 objects (Biederman, 1987), we estimated that each neuron
fires in response to 50–150 different objects (Waydo et al., 2006).
Bowers (2009) argued that this estimation is flawed because (a)
multiple neurons can respond to the same image and (b) these
calculations assume that a grandmother cell should only respond to
one face or object. Briefly, the fact that multiple neurons can
respond to the same image—a possibility that we consider very
likely—is not a problem for the above calculations. In fact, it
seems highly unlikely that we happened to find the one and only
neuron that responds to a particular face. This argument was
explicit in Quian Quiroga et al. (2005) and further quantified in
Waydo et al. (2006). With regards to the second point, for our
calculations we did not assume any properties of how grandmother
cells should or should not respond at all, as claimed by Bowers
(2009), but rather estimated an upper bound for the number of
objects that a neuron may respond to. It should be also stressed that
as discussed in Waydo et al. (2006), the estimated number of
neurons responding to one concept could be much lower because
(a) images known to the participants are more likely to elicit
responses than are unfamiliar stimuli and (b) neurons with a higher
degree of sparseness are very difficult to detect in our recording
sessions lasting, on average, about 30 min.
Evidence from single-cell recordings shows that the brain may go
from distributed representations in lower sensory areas to sparse
representations in higher areas. We already mentioned the very sparse
responses to odors by Kenyon cells in the locust (Perez-Orive et al.,
2002). Kenyon cells neurons receive direct inputs from projection
neurons in the antennal lobe, which have a largely distributed repre-
sentation for odors (compare the responses in Figure 1A and Figure
1B in Perez-Orive et al., 2002). Similarly, the ultrasparse responses of
the robust nucleus of the archistriatum (RA) neurons in the zebra-
finch are driven by high vocal center (HVC) neurons with distributed
responses (see Figure 2b in Hahnloser et al., 2002). Further evidence
in other species is still scarce because as mentioned in the previous
section, to compare selectivity across different areas one should use
the same stimulus set. Barnes and coworkers (Barnes, McNaughton,
Mizumori, Leonard, & Lin, 1990) showed that neurons in the hip-
pocampus in rats responded more selectively to the rat spatial location
than neurons in entorhinal cortex. These results seem to support the
hypothesis of complementary learning systems, with higher level of
sparseness in the hippocampus than in cortex (Norman & O’Reilly,
2003), an appealing idea that would explain fast learning of new
episodic memories and associations in the hippocampus with the use
of sparse coding on the one hand and generalization in cortex with the
use of a distributed representation on the other. Bowers (2009) criti-
cized the study of Barnes et al. (1990) and its support to the comple-
mentary systems hypothesis by claiming that the entorhinal cortex is
not part of neocortex and that a proper comparison of sparseness
should be made between hippocampus and neocortex. However, the
entorhinal cortex is the main gateway to the hippocampus—that is,
most of the information from neocortex is conveyed to the hippocam-
pus through the entorhinal cortex. To us, this gives valuable evidence
of how the representation gets sparser when reaching the hippocam-
pus. Moreover, a more recent study with single-cell recordings in the
human MTL showed that the selectivity of the single-cell responses in
the parahippocampal cortex (one of the main inputs to entorhinal
cortex) was significantly lower than the one in the entorhinal cortex,
the amygdala, and the hippocampus (Mormann et al., 2008).
It seems also plausible to argue that a distributed representation
in IT is transformed into the sparser representation shown in the
MTL (compare responses of Hung et al. (2005) in IT with those of
Quian Quiroga et al. (2007, 2005) in MTL), given the close
anatomical connections between these areas. However, we empha-
size that this is still a conjecture due to the different recording
techniques, species, and stimuli used in these studies. In this
respect, it has been argued that the more distributed representations
in IT (compared with MTL) may be necessary to identify the
different views of the same person or object with a population code
(DiCarlo & Cox, 2007), in contrast to the sparse and invariant
responses in the human MTL, where neurons fire to the concept in
an abstract manner and the particular view or details of the pictures
are irrelevant. It is also possible that very sparse neurons are also
present in IT but are hard to find, partially due to the technical
difficulties described in the previous sections.
In summary, Bowers (2009) made a commendable effort to link
psychological theories and computational models to the firing of
individual neurons in the brain. This effort should be praised and
hopefully extended through further interactions across these fields.
In this commentary, we tried to emphasize the difficulties inherent
to neurophysiology and the challenges involved in distinguishing
between distributed and local codes. We also attempt to provide a
quantitative framework to describe neuronal representations resid-
ing in a continuum that ranges from distributed to local represen-
tations. Given how poor our understanding of cortex currently is,
we hope that this quantitative formulation will avoid semantic
discussions and will pave the way to comparisons across areas,
laboratories, and experimental conditions, as well as between
physiology and computational models. Unraveling the codes used
by circuits of neurons to represent information is arguably one of
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Received February 9, 2009
Revision received June 8, 2009
Accepted June 9, 2009 ?
Postscript: About Grandmother Cells and Jennifer Aniston
Rodrigo Quian Quiroga
University of Leicester
Children’s Hospital Boston, Harvard Medical School, and
A typical problem in any discussion about grandmother cells is
that there is not a general consensus about what should be called
as such. Here, we discuss possible interpretations in turn and
contrast them with what we find in our own data (arguably the
closest experimental evidence of grandmother cells so far). A first
and naı ¨ve interpretation of the term grandmother cell is that one
and only one neuron encodes for one and only one concept (a face,
an object, an animal, etc.). We agree with Bowers (2010) that this
is a straw-man version of this idea—although some people still
take this view when (incorrectly) arguing that if we would have
grandmother cells then the concept of grandma would disappear if
her dedicated cell dies—which clearly does not apply to our data.
Given that we record from a very tiny fraction of neurons in the
medial temporal lobe (MTL), if we do find a neuron firing to a
particular concept, there must be more. A more plausible version