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The Journal of Neuroscience, 2002, 22:RC198:1-6
RAPID COMMUNICATION
"Keeping on Track": Firing of Hippocampal Neurons during
Delayed-Nonmatch-to-Sample Performance
Robert E.
Hampson,
John D.
Simeral, and
Sam A.
Deadwyler
Department of Physiology and Pharmacology and the Neuroscience
Program, Wake Forest University School of Medicine, Winston-Salem,
North Carolina 27157
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ABSTRACT |
Hippocampal neurons that encode critical events during a
delayed-nonmatch-to-sample (DNMS) task were proposed to have functional topography as demonstrated by Hampson et al. (1999b) . Functional cell types (FCTs) that encode similar task features were located within
alternating transverse segments along the hippocampal longitudinal axis. On this basis, Redish et al. (2001) suggested that firing of
populations of CA1 neurons recorded from the same hippocampal locations
in animals running on linear or curvilinear tracks should be spatially
and temporally correlated; however, they failed to find such
correlations. The current study addresses the issues raised by Redish
et al. (2001). Initially we found that modeling of simulated place
fields revealed absences in temporal correlations in the study by
Redish et al. (2001) that should have been present given the reported
spatial correlations. In addition, the correlation methods used by
those investigators failed to detect robust but transient event-related
cross-correlations between FCTs in the DNMS task. Furthermore,
demonstration of such transient, short-latency correlated firing
between similar CA3 and CA1 FCTs corroborated the anatomic scheme
proposed by Hampson et al. (1999b) and reaffirmed the potential
existence of a functional topography within hippocampus.
Key words:
ensemble; learning; memory; behavior; place field; cross-correlation
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INTRODUCTION |
Theories
of the function of the hippocampus range from strictly mapping the
environment (O'Keefe and Nadel, 1978) to representing relationships
between stimuli (Cohen and Eichenbaum, 1993). Paradoxically, because
the hippocampus is assumed to encode places, routes, and destinations,
it might be expected that a topography of place representation
would emerge, but this type of topography has not been identified.
However, a previous report by Hampson et al. (1999b) showed that
neurons that encoded task-relevant features of a
delayed-nonmatch-to-sample (DNMS) task were distributed or "clustered" within defined segments of hippocampus, providing supportive evidence of a functional topography (Eichenbaum et al.,
1989).
Recently, Redish et al. (2001) contested this finding, showing that
ensembles of hippocampal CA1 neurons recorded in animals traversing
elevated linear and curvilinear tracks failed to exhibit clustering of
spatial or temporal characteristics. Their results countermand the
notion of a functional topography, suggesting that the role of the
hippocampus as stated is to "make arbitrary associations," the
capacity for which would be compromised if synaptic inputs to adjacent
or localized groups of pyramidal cells were correlated (Redish et al.,
2001).
In the following report we address these issues directly by (1)
exploring whether temporal clustering between cells with overlapping place fields could be detected within data modeled from Redish et al.
(2001); and (2) examining the sensitivity of the cross-correlation technique to the temporal domain across which the correlation is
calculated (i.e., entire spike train vs perievent epochs). New evidence
for temporal correlations between clusters of functionally identified
hippocampal neurons is also introduced that validates the findings of
Hampson et al. (1999b).
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MATERIALS AND METHODS |
Simulation of place fields on linear tracks. Monte
Carlo methods were applied to simulate track-running experiments using place-cell parameters derived from previous published reports (Skaggs
and McNaughton, 1996; Skaggs et al., 1996; Redish et al., 2000, 2001).
The parameters used in the simulations were: track length, 180 cm;
traversal speed, 15 cm/sec; pixel size, 2.8 cm; place field width, 50 cm (all pixels with above background firing rate); mean simultaneously
recorded neurons, 22; and recording time, 30 min. Fields were randomly
distributed assuming bidirectional traversal (360 cm) to allow for
neurons that fired only in one direction (Skaggs et al., 1996). Each
spike train comprised 40 laps over the track (20-30 sec each). Place
fields were simulated with a Gaussian function (Mehta et al., 2000)
centered on a single point on the track with increased firing from
background (0.5-2.0 Hz) to 15-20 Hz at the peak. Each cell was
assumed to fire in its place field on every lap. Spatial
cross-correlation coefficients ( ) between pairs of simulated place
fields were computed from spike trains sorted into 128 bins (64 pixels
for each outbound and inbound lap), normalized for time spent in each
pixel. Temporal cross-correlations were computed as per Redish et al.
(2001) from spike-train firing by dividing cell firing into 500 msec
bins and computing the Pearson cross-correlation coefficients ()
across the entire spike train. Statistical tests on the distributions of coefficients used the Komolgorov-Smirnov D statistic for
n > 2000 and the Anderson-Darling
A2 statistic (D'Agostino and
Stephens, 1986) for n < 2000 (i.e., comparison between
same vs different tetrode).
Recording techniques. Male Long-Evans rats
(n = 27, 3-11 months of age) were trained to criterion
on a two-lever spatial DNMS task with randomly occurring delays
of 1-40 sec. Recording arrays (NB Laboratories, Denison, TX) with 16 microwires (40 µm) were surgically implanted and positioned within
the CA1 and CA3 cell layers (Deadwyler et al., 1996). Arrays consisted
of eight wire pairs with 200 µm separation between pairs and 800 µm
wires within a pair. Electrode tip length was precisely trimmed to
follow the longitudinal curvature of the hippocampus (Deadwyler et al.,
1996). Recordings were obtained from 312 pyramidal neurons (9-16 per animal; mean = 11.5) as determined by firing rate criteria (Fox and Ranck, 1981). Stable extracellular action potential
waveforms from at least five DNMS sessions were monitored, and
consistent event-specific firing patterns were assessed by constructing
normalized perievent histograms of firing rate ± 1.5 sec around
each task-relevant event or leverpress. Only the largest amplitude
waveform (neuron) per electrode position was used to guarantee the best
estimation of relative location in the hippocampus. Perievent firing
frequency relative to baseline was transformed to a z score
to determine encoding as significantly elevated firing
(z  3.09; p < 0.001). Each neuron
was identified with respect to its functional cell type (FCT) depending
on which DNMS event(s) it encoded (e.g., a unique event, left nonmatch
response; or category, all left leverpresses). Cells with background
firing rates of >2.0 Hz were excluded from analysis to avoid
contamination by interneuron activity. Temporal cross-correlation
coefficients () were calculated across entire neural spike trains as
described above. Cross-correlograms were constructed from normalized
spike-triggered histograms computed at 1.0 msec resolution during
designated time epochs. Coefficients () reported for correlograms
differed from those calculated over complete spikes trains in that (1)
they only included spikes during the designated time epoch, (2) bins
were 1 msec, and (3) bins were adjusted for latency of the correlogram peak.
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RESULTS |
Assessment of hippocampal cell firing on linear tracks
Reported firing contingencies of hippocampal place cells assessed
on linear tracks indicate that discrete spatial segments are mapped as
the animal runs in a single direction along the track (Skaggs and
McNaughton, 1996; Redish et al., 2000). Using this procedure, Redish et
al. (2001) failed to observe significant spatial or temporal
correlations between cells or fields on the same versus different
tetrodes arranged in a lattice of 14 with a minimum 350 µm
separation. We explored the firing correlates in those studies by
simulating 3000 place fields recorded on linear tracks using the
descriptions of place field and track parameters published by Redish et
al. (2000).
Ensembles of 8-46 simultaneously recorded place cells (Redish et al.,
2000) were simulated assuming random distribution of place fields
within an ensemble. In that context, if seven or more 50-cm-wide place
fields were recorded, at least one field would overlap another, whereas
with >20 fields recorded, each field would have a mean of 40% overlap
with the two "nearest neighbors" (Fig.
1A). Furthermore, an
"average" ensemble containing 22 neurons (Redish et al., 2000)
would have 21 possible cross-correlation pairings for each place field:
19 (90.5%) with low spatial correlation ( < 0.05, no overlap)
because of extended separation distance on the track (Fig.
1A). However, ~10% of place-field pairs (2 of 21, 9.5%) would overlap as nearest neighbors and possess stronger spatial
correlations ( 0.2).
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Figure 1.
Monte Carlo simulation of place fields
recorded while animals traversed a linear track, derived from
parameters reported by Redish et al. (2000, 2001). The simulation
included 3000 neurons, comprising 128 ensembles and 53,936 correlated
pairs (see Materials and Methods for parameters). A,
Place-field firing was modeled with Gaussian function as illustrated.
Nearest neighbor fields (red/blue)
produced a mean 40% overlap (purple
regions) and nonadjacent fields
(red/green) produced <15% overlap
(green regions); indicates spatial
cross-correlation. B, Temporal dispersion (rasters) of
cell firing in A. Neural spike trains (colored
ticks) were binned in 500 msec increments (30 min total); is temporal cross-correlation. Overlapping fields
(red/blue) were separated by 1.09 sec and
nonoverlapping fields (red/green) were
separated by >3.5 sec. C, Distribution of spatial
cross-correlation coefficients (blue line) from
simulation. The dotted line indicates correlations
reported by Redish et al. (2001). Peak at =  0.10 reflects
80% of pairs with nonoverlapping place fields. Asymmetry biased toward
positive correlations reflects ~20% of pairs with overlapping
place fields. D, Temporal cross-correlation coefficient
distribution (blue line). The main peak ( = 0.10) reflects nonoverlapping fields; the secondary peak ( = 0.46, arrow) indicates 10% of cell pairs with >40%
overlapping place fields (i.e., nearest neighbors). The dotted
line indicates the distribution published by Redish et al.
(2001). Reduction in field size (25 cm) or traversal speed (10 cm/sec)
shifted the secondary peak to the left (red trace)
without decreasing amplitude.
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The results of the simulation are shown in Figure 1C. The
distribution of spatial correlation coefficients (Fig.
1C, blue trace) for the simulation was
identical to the graph (Fig. 1C, dotted trace)
shown by Redish et al. (2001, their Fig. 3a). Temporal correlations between pairs of spike trains (Fig. 1B)
were stronger (mean of = 0.46) in the 10% of cell pairs with
overlapping fields (Fig. 1D, blue trace,
arrow) compared with the 90% of cell pairs with fields that
did not overlap (  0.05). Interestingly, Figure 3b
of Redish et al. (2001) does not show this secondary peak (Fig. 1D, dotted trace) or any indication of
increased correlations produced by overlapping fields. Considering that
they reported spatial correlations identical to the simulation (Fig.
1C, dotted trace), the absence of this secondary
peak for overlapping fields is puzzling.
Additional investigation of the strong correlations in Figure
1C revealed that field overlap must be reduced to 15% to
eliminate the secondary peak. Decreasing place field size by one-half
produced the same percentage of overlapping fields with only slightly
reduced correlations ( = 0.33; Fig. 1D,
red trace). Alternatively, reducing traversal speed to10
cm/sec also reduced the magnitude of correlation ( = 0.36) but
not the percentage of correlated pairs. Only a significant reduction in
both field width (15 cm) and speed (5 cm/sec) eliminated the second
peak in Figure 1D (Komolgorov-Smirnov D
statistic = 0.59; Ksa = 0.68; p > 0.25). Two other factors were also examined:
the Gaussian nature of place-field firing (Mehta et al., 2000) and
consistency on each pass through the field (Fenton and Muller, 1998).
Reduced secondary peaks ( < 0.20) were produced only if one of
the neurons in a pair failed to fire on 80% of passes through the
field, or if random firing in the field occurred. From the above
"unrealistic conditions" required to eliminate the secondary peak
of temporal correlations in the model given the distribution of spatial
correlations (Fig. 1C), it appears that there were
inconsistencies in the data reported by Redish et al. (2001).
Finally, cells recorded from the same tetrodes were examined as to
whether they showed stronger spatial or temporal correlations (clustering) than cells on different tetrodes (Redish et al., 2001).
Simulated place cells were separated into different tetrode sets and
analyzed. When neurons recorded from the same tetrode (within) were
clustered, the secondary peak (Fig. 1D,
arrow) of strong temporal correlations between nearest
neighbor place fields was increased from 9.5 to 31%
(A2 = 0.87; p < 0.05), compared with a decrease from 9.5 to 5.6% for
between-tetrode pairs. Thus, to resolve the issue, it is necessary for
a secondary population of strong correlations to be present for
overlapping fields, to identify clustering if it exists. Because this
peak was not present in the data obtained by Redish et al. (2001), it
would have been impossible to detect clustering if it were present.
Transient versus continuous spike train correlations
Redish et al. (2001) computed cross-correlation coefficients
across complete spike trains for the entire sessions. We constructed cross-correlations between FCTs (see Materials and Methods) that were either (1) confined to temporal epochs surrounding the appropriate behavioral events or (2) computed across the entire trial. Figure 2 shows superimposed rastergrams and
cross-correlograms from two different FCTs analyzed in relation to
their behavioral correlate (nonmatch response). The small rectangular
boxes (Fig. 2A) capture instances in which one neuron
discharged within 20 msec of the other. It is clear that both cells
increased firing just before the response (400-0 msec) and also
exhibited a transient increase in the number of correlated or
"captured" spikes (spike-triggered cross-correlogram, Fig.
2C). However, correlated firing was minimal in the 800 to
400 msec epoch (before the response) and the 0 to +400 msec epoch
(Fig. 2B,D). Thus calculation of the mean correlation coefficient between the spike trains of the same two cells over the
entire trial was not significant ( = 0.08). Coefficients calculated indiscriminately across the entire spike train tend to
dissolve the robust transient temporal correlations between FCTs during
task-relevant events (Fig. 2).
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Figure 2.
Transient cross-correlations between
simultaneously recorded FCTs encoding the same DNMS behavioral event.
A, Rastergrams of single trial firing. Black
dots, CA3 FCT; red dots, CA1 FCT. Each row is a
single trial, 800 msec before to 400 msec after nonmatch response (0 msec). Rectangles indicate when both neurons fired
within 20 msec; increased rectangle frequency reflects a significant
increase (X2(8) = 32.41; p < 0.001) in correlated firing.
Bottom panels, Cross-correlograms of CA3
spike-triggered CA1 firing, constructed over 100 trials within each of
the three indicated time epochs: B, 800 to 400 msec;
C, 400-0 msec; D, 0 to +400 msec.
Coefficients () indicate correlation measured at peak of normalized
correlogram. Mean across complete trial, = 0.08.
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FCTs and interneurons fire differently during DNMS trials
Recently, Hirase et al. (2001) showed that local coherence could
occur in hippocampal pyramidal cell firing as a result of shifts from
theta (wheel running) to nontheta (sleep or immobility) behavioral
states. They also suggested that these states coexist during certain
behavioral contexts and could provide the basis for reciprocal firing
between interneurons and FCTs during the DNMS task. Coherent firing
during nontheta states could thus account for the apparent clustering
of FCTs within defined hippocampal regions (Hampson et al.,
1999b). We investigated this by examining simultaneous
interneuron and FCT firing during the DNMS task. The panels in Figure
3 show activity of three different
interneurons (Fig. 3, top) with associated FCTs (Fig. 3,
bottom). The autocorrelations show that the three
interneurons had intrinsic 80-160 msec periodicity indicative of
"theta" cell firing (Csicsvari et al., 1998). The increased FCT
firing relative to their appropriate events was not associated with a
marked change in interneuron firing in either of the three cell pairs
(Fig. 3, top). Alternatively, the substantial decrease in
firing of each of the three interneurons after completion of the
response was also not associated with a reciprocal increase in FCT
discharge (Fig. 3A, arrow).
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Figure 3.
Comparison of interneurons and FCTs.
Top, Rastergrams and perievent histograms for three
different interneurons. Bottom, Rastergrams and
perievent histograms for three simultaneously recorded FCTs.
A, Interneuron and FCT firing recorded during (±4 sec)
right nonmatch response. Arrow, Interneuron firing
decreased 50% from baseline during drinking. The periodicity of the
interneuron autocorrelogram (inset) was 120 msec (8 Hz).
B, A different interneuron-FCT pair recorded during
left nonmatch response. The periodicity of interneuron was 90 msec (11 Hz). C, A third interneuron-FCT pair recorded during
left sample response. The interneuron firing rate decreases during
delay. The periodicity of the interneuron was 100 msec (10 Hz).
Insets, Autocorrelograms ±200 msec; the refractory
period at the center is 2 msec.
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Hippocampal FCTs are clustered and correlated
Hampson et al. (1999b) defined clusters on the basis of
groups of FCTs recorded with linear multielectrode arrays, within interleaved transverse zones or sectors along the septotemporal axis of
the hippocampus. To test the stipulation by Redish et al. (2001)
regarding clustering (cited in the introductory remarks), we
constructed cross-correlograms between FCTs within their appropriate epochs, as shown in Figure 2. The regions designated right sector and
left sector in the foldout map of the hippocampus in Figure 4 correspond to locations of FCTs that
encoded right and left leverpresses in the task. Figure 4 shows
correlograms and mean peak correlations for respective identical
(encoded same event, n = 24), compatible (encoded at
least one event feature; e.g., left or sample, n = 56)
and incompatible (encoded no common features, n = 296)
pairs of CA3 and CA1 FCTs sorted by electrode position on the array.
The correlograms on the right correspond to comparisons (Fig. 4,
solid arrows) between identical FCTs in the left sector (Fig. 4, triangles). In all three cases, correlated firing
of CA1 FCTs was significant (0 µm, = 0.31 ± 0.07; 200 µm, = 0.71 ± 0.11; 400 µm, = 0.49 ± 0.09; X2(80) = 379.2; p < 0.001), with the strongest correlation
between pairs at 200 µm of septotemporal offset, CA1 relative to CA3.
An important finding in this regard was that two identical CA1 FCTs,
both with strong correlations to the same CA3 FCT, showed strong
correlations with each other (Fig. 4, CA1-CA1; = 0.058 ± 0.19) at negligible peak latencies (1.25 ± 0.39 msec), indicating potential coactivation by that same CA3 neuron
(Hirase et al., 2001).
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Figure 4.
Anatomic distribution of cross-correlations. The
hippocampal foldout map shows pairs of FCTs classified as identical
(triangles, stars), compatible
(circles), or incompatible (different
symbols) located in registered electrode array positions
(inset). Left (shaded) and right
(unshaded) sectors (600 µm) refer to encoding of left
versus right leverpresses in DNMS. Left sector,
Correlograms from three pairs of identical CA3-CA1 FCTs
(triangles, solid arrows), measured
within ±1.5 sec epochs surrounding task events, summed over 100 trials. Bottom panel, Correlogram for pair of
CA1-CA1 FCTs (0 and 200 µm) correlated with the same CA3 FCT
(filled triangle) and each other (note: 0 msec
peak latency). Right sector, Correlogram
(top) constructed between incompatible (i.e., different
sectors) CA3-CA1 FCTs (dotted arrow). Bottom
panel, Correlograms for two pairs of identical FCTs
(stars, dashed arrows). Center
bottom panel, Correlogram for pair of compatible
(sample and left) FCTs during a commonly encoded left sample
event.
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The top left correlogram in Figure 4 shows the expected nonsignificant
correlation ( = 0.06 ± 0.05) between incompatible FCTs
(Fig. 4, dotted arrow) located in different hippocampal
sectors. Below that appear highly significant correlations between
identical FCTs (Fig. 4, stars) located in the opposite
(right) sector. Finally, correlations between compatible FCTs,
illustrated in the center bottom panel (Fig. 4, circles),
were significant ( = 0.33 ± 0.10), but less so than
between identical FCTs.
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DISCUSSION |
In the above analysis, we have demonstrated that the failure of
Redish et al. (2001) to find the clustering reported by Hampson et al.
(1999b) requires serious reconsideration on the basis of (1) an
inability to demonstrate significant correlated firing in place cells
given the spatial overlap parameters they reported, (2) their use of
cross-correlation techniques that could not resolve robust but
transient correlated cell firing, and (3) additional evidence provided
here demonstrating short-latency correlated firing between FCTs in
accordance with the topographic scheme proposed by Hampson et al.
(1999b).
Redish et al. (2001) found no clustering in animals running on linear
tracks; however, evidence for clustering by their definition could
occur in only 10% of the cell pairs tested (i.e., those with fields
that overlapped on the track). Their report did not reflect the
necessary percentage of strong temporal correlations dictated by their
distribution of spatial correlations, despite previous demonstrations
of strong correlations as a necessary consequence of place-field
overlap (Wilson and McNaughton 1994; Skaggs and McNaughton 1996; Skaggs
et al., 1996). Differential clustering either between or within
tetrodes in the model did not account for this absence (Fig.
1D). Given their method of correlation across
continuous spike trains on linear tracks, detection of clustering is
difficult because the majority of coefficients are negligible or zero
because of the large number of non-overlapping fields.
The definition of clustering proposed by Redish et al. (2001) stated
that neurons recorded from the same location should be both spatially
and temporally correlated. Given this interpretation, we showed that
transient correlations between FCTs were robust and differential
depending on their anatomic location in the hippocampus (Fig. 4);
however, these correlations were eliminated if calculated indiscriminately across the entire trial (Fig. 2). The 200 µm offset
in the gradient of strongest correlations (p = 0.71; p < 0.001) between CA3 and CA1 FCTs in the same
animals (Fig. 4) is supported by the original description of
"lamellas" by Andersen et al. (1969). This description was
reassessed recently (Andersen et al., 2000) to show that the densities
of functional synaptic projections from CA3 to CA1 form a diagonal
"ridge" between the two subregions that is most dense at the 200 µm offset between the two regions. Such evidence strongly supports
the functional topography proposed by Hampson et al.
(1999b).
Redish et al. (2001) also suggested that the two behavioral contexts,
running on linear tracks and DNMS performance, are fundamentally the
same with respect to hippocampal correlates. This assumption can be
questioned from several perspectives. We have in the past shown the
necessity of hippocampal cell firing in the DNMS task by demonstrating
that (1) successful performance requires an intact hippocampus as well
as several weeks of training (Hampson et al., 1999a), (2) FCTs encode
information critical to correct DNMS performance (Hampson and Deadwyler
1996; Deadwyler and Hampson, 1997; Hampson et al., 1998), and (3) DNMS
performance is facilitated by drugs that enhance FCT firing (Hampson et
al., 1998). In contrast, the hippocampal activity reported by Redish et
al. (2001) involved little behavioral plasticity or complexity, and
where complexity existed (Redish et al., 2000), the necessity of
hippocampal cell firing for accurate performance was not disclosed.
Fox and Ranck (1981) originally reported the differences between theta
cell and presumed pyramidal cell firing in awake moving animals. Figure
3 illustrates spontaneous background firing rates (>3 Hz) and
oscillations similar to those reported previously for theta cells
(Christian and Deadwyler, 1986; Csicsvari et al., 1998; Hampson et al.,
1998; Wiebe and Staubli, 2001), which differed markedly from
simultaneously recorded pyramidal cells (FCTs). Buzsaki et al. (1983)
showed that pyramidal cell firing is released during nontheta
(behaviorally immobile) states. This circumstance could explain the
increased FCT firing observed in the DNMS task if animals were inactive
during the lever press. Figure 3 indicates that FCT firing was not
dependent on theta or nontheta states entrained at critical times
during the DNMS task, as evidenced by the lack of reciprocity in
FCT-interneuron firing either at the time of the response or during
reduced locomotion after completion of the response.
A seemingly endless recurring methodological issue relates to the
touted differences between tetrode and conventional single electrode
recording methods in the hippocampus. We and others have addressed this
issue in several previous publications (Nicolelis et al., 1993;
Deadwyler et al., 1996; Hampson and Deadwyler, 1998; Chapin et al.,
1999). Suffice it to say that ironically, if our recording techniques
did reflect multiple and not single FCT activity, it would provide the
best possible evidence for clustering. This is because neuronal firing
is categorized with respect to when it occurs in the DNMS task. Such
multiple FCT firing restricted to specific events would reflect
multiple functional cell types with correlated firing active at the
same electrode location [i.e., clustering as defined by Redish et al.
(2001)].
In summary, the modeled outcomes derived from their data (Fig. 1), the
insensitivity of their assessment procedures to strong transient
temporal correlations (Fig. 2), and the demonstration here of temporal
correlations between FCTs located in hippocampal sectors defined by the
demands of the DNMS task (Fig. 4) mitigate against the negative
conclusions of Redish et al. (2001) and provide additional support for
the topographic scheme proposed previously by Hampson et al.
(1999b). With restricted, precise correlation methods we
verified that FCT firing follows an independently determined gradient
of functional synaptic connectivity between CA3 and CA1 cells (Andersen
et al., 2000) and that such clustering is specific to the cognitive
dimensions of the DNMS task. However, it should not necessarily be
expected that the clustering observed for one behavioral context (i.e.,
DNMS) implies a similar relationship in other behavioral contexts
(i.e., linear tracks).
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FOOTNOTES |
Received July 27, 2001; revised Sept. 10, 2001; accepted Sept. 17, 2001.
This work was supported by National Institutes of Health (NIH) Grants
DA08549 and MH61397 to R.E.H. and DA03502 and DA00119 to S.A.D. All
animal procedures followed NIH and Society for Neuroscience guidelines
for care and use of laboratory animals.
Correspondence should be addressed to Dr. Robert E. Hampson, Department of Physiology and Pharmacology, Wake Forest
University School of Medicine, Medical Center Boulevard, Winston-Salem,
NC 27157-1083. E-mail: rhampson{at}wfubmc.edu.
This article is published in
The Journal of Neuroscience, Rapid Communications Section,
which publishes brief, peer-reviewed papers online, not in print. Rapid
Communications are posted online approximately one month earlier than
they would appear if printed. They are listed in the Table of Contents
of the next open issue of JNeurosci. Cite this article as:
JNeurosci, 2002, 22:RC198 (1-6). The
publication date is the date of posting online at
www.jneurosci.org.
| |
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INTRODUCTION
