Neural Engineering Center, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106
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ABSTRACT |
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Stacey, William C. and Dominique M. Durand. Synaptic Noise Improves Detection of Subthreshold Signals in Hippocampal CA1 Neurons. J. Neurophysiol. 86: 1104-1112, 2001. Stochastic resonance (SR) is a phenomenon whereby the detection of a low-level signal is enhanced in a nonlinear system by the introduction of noise. Studies of the effects of SR in neurons have suggested that noise could play a prominent role in improving detection of small signals. Most experimental SR research has focused on the role of noise in sensory neurons using physiological stimuli. Computer simulations show that signal detection in hippocampal neurons is improved by the addition of physiological levels of noise applied extracellularly to synaptic inputs. These results were confirmed experimentally. We now report that endogenous noise sources can also improve signal detection. The noise source was generated by modulating the random synaptic activity on the apical dendrites of CA1 cells in rat hippocampal slices using subthreshold cathodic current. Intracellular recordings of CA1 cells showed that even small increases of synaptic noise are able to greatly improve the detection of an independent, synaptic, subthreshold stimulus as predicted by the simulations. The noise variance in the CA1 cell was compared with the resting variance and with variance changes caused by several endogenous noise sources. In all cases, the increased noise variance was well within the physiological range. These results were supplemented and analyzed with a CA1 computer model. The improved signal detection with small amounts of endogenous noise suggests that the diverse inputs to CA1 are able to improve detection of subthreshold synaptic signals and could provide a means to modulate detection of specific inputs in the hippocampus.
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INTRODUCTION |
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Noise is often seen as
detrimental to signal detection. However, in certain systems, noise can
enhance detection of some signals. The effect is called stochastic
resonance (SR) and predicts that noise can enhance the detection of
subthreshold signals in nonlinear, threshold-detecting systems
(Benzi et al. 1981; Fauve and Heslot 1983
; McNamara et al. 1988
; Wiesenfeld
and Moss 1995
). The theory has been applied to several neural
systems (Braun et al. 1994
; Bulsara et al.
1991
; Collins et al. 1996
; Douglass et
al. 1993
; Gluckman et al. 1996
; Levin and
Miller 1996
; Pei et al. 1996a
,b
; Stacey
and Durand 2000
) and has been shown to have significant effects
in vivo (Russell et al. 1999
). The implications of SR are several, including the paradigm shift that noise present in the
neurons may actually serve to enhance signal detection.
SR, as shown in Eq. 1, describes a proportional relationship
between the signal-to-noise ratio (SNR) of the output to the noise
intensity (D, noise intensity; , signal strength;
U, threshold barrier height)
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(1) |
The importance of studying SR in the CNS arises from a consideration of
the nature of signal detection in a typical pyramidal cell such as CA1.
Possessing extensive dendritic trees, CA1 cells receive tens of
thousands of synaptic inputs and require up to 300 simultaneous
synaptic events to produce an action potential (Andersen
1990). The effects of attenuation also greatly affect the
detection dynamics (Spruston et al. 1993
). There is
therefore a high probability of receiving subthreshold signals. Because random synaptic events have the same quantal size as single evoked excitatory postsynaptic potentials (EPSPs) (Larkman et al.
1997
), the effect of noise on signal detection is significant.
CA1 cells thus function in an environment well suited for SR.
The physiological relevance of SR was demonstrated most clearly in
sensory systems, where both signal and noise sources are readily
apparent (Braun et al. 1994; Douglass et al.
1993
; Levin and Miller 1996
). In the crayfish
mechanoreceptor, for instance, it is logical to describe the noisy
ripples in a stream as noise and the approach of a predator as a
signal. Central neurons also exhibit SR activity (Gluckman et
al. 1996
; Stacey and Durand 2000
); however,
because the inputs are more complicated, it is difficult to design an
experiment that directly can test SR using the endogenous sources of
signal and noise.
Recently it has been shown that hippocampal CA1 cells are capable of
producing SR behavior (Stacey and Durand 2000). A
computer model of a CA1 cell was used to determine the role of
physiological noise sources in signal detection. Experimental
verification of this model utilized applied current pulses to create
noise events. The pulses created random, global EPSPs. While the
experiment did demonstrate that detection of subthreshold signals could
be improved by increasing the noise, the applied noise was only an approximate representation of physiological noise sources. Noise in the
CNS involves multiple, independent sources and locally uncorrelated
noise events (Lindner et al. 1995
). The goal of the present paper is to test the hypothesis that physiological levels of
endogenous noise within the hippocampal slice can improve signal detection. The noise variance needed to evoke SR was measured and
compared with baseline physiological noise and the augmentation in
variance produced by several different noise sources. The improvement in signal detection was then evaluated at the different noise levels.
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METHODS |
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Preparation of slices
Sprague-Dawley rats (20-30 days old) were anesthetized with
ethyl ether and decapitated according to protocol approved by the
University Animal Resource Center. The brains were quickly removed and
placed in aerated (95% O2-5%
CO2), iced sucroseartificial cerebrospinal
fluid (ACSF, in mM: 220 sucrose, 3 KCl, 1.25 NaH2PO4, 2 CaCl2, 2 MgSO4, 26 NaHCO3, and 10 dextrose). Hippocampal slices 400 µm thick were cut in this solution using a Campden 752 M Vibroslice. Slices were then transferred to a submersion chamber and bathed for
over 1 h in room temperature ACSF (in mM: 124 NaCl, 3.75 KCl, 1.25 KH2PO4, 2 CaCl2, 2 MgSO4, 26 NaHCO3, and 10 dextrose) and aerated with 95%
O2-5% CO2. Each slice was
then transferred to a perfusion chamber and incubated to 35°C for implementation.
Electrode placement and signal generation
A tungsten microelectrode was used to generate periodic signal
events in the Schaffer collateral layer (see Fig.
1A). The tip of another
tungsten electrode was bent 90° and positioned parallel to the slice
across the entire CA3 layer. A sharp (60-120 M) glass
microelectrode filled with 2 M potassium acetate was used for
intracellular recording of CA1 cells. The electrode was mounted on a
Burleigh piezoelectric motor (Inchworm) with a 6000 controller. Data
were recorded using a Warner IE-210 amplifier and recorded onto digital
audio tapes.
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The "signal" consisted of a periodic train of 250-µs current
pulses applied at 1 Hz. By injecting the signal pulses into the Schaffer collaterals, the signal reached CA1 cells as synaptic events
on the apical dendrites. The amplitude of the signal was adjusted to
produce a subthreshold EPSP (3-8 mV, threshold 10 mV) in the CA1
cell being recorded.
The "noise" was induced by injecting a long (10-15 s),
low-amplitude (20-100 µA) cathodic current pulse on the electrode
near the CA3 region. This pulse raised most of the neurons in the CA3 region closer to threshold and induced increased random synaptic activity in the CA1 layer. However, it also produced a large (~10 mV), uniform, negative baseline shift artifact across the entire preparation. This artifact was measured extracellularly and subtracted from the intracellular recordings to determine the change in
transmembrane voltage. None of the cells experienced any change in
resting voltage within an accuracy of 1 mV. To determine whether noise
variance had changed in CA1 cells due to a DC pulse, F-tests
were performed on variance samples before and during the pulse
(Microsoft Excel 97). Samples were significantly different if
P < 0.01. Finally, the thermal noise from the
electrode in the extracellular space (~20,000-60,000
µV2) was subtracted from intracellular variance
to determine the true noise variance in the cell (Wahl et al.
1997).
Experiments were not performed unless the CA1 cell had a stable resting
membrane voltage below 60 mV. In some cases, a holding potential
(<10 mV) was necessary to maintain resting voltage (holding current
<1 nA). Cells were not used if evoked action potentials were <60 mV
or >5 ms at half-amplitude. Recorded voltage data were digitized at
4,000 Hz.
Evaluation of detection
Raw voltage data were analyzed by determining the occurrence of
action potentials and generating an output time series (Douglass et al. 1993; Gluckman et al. 1996
;
Wiesenfeld and Moss 1995
). This time series was used to
compute the power spectrum of the output. The signal artifact was used
as a marker for time index for the splicing (Fig. 1B; also
see next paragraph). Signal-to-noise ratio (SNR) of the data was
computed by dividing the power at stimulus frequency by the baseband
power in the vicinity of the stimulus spike (Fig. 1C), as
previously described for SR (Stacey and Durand 2000
;
Wiesenfeld and Moss 1995
). To normalize the increase in
noise variance among different cells, the variance during the DC pulse
was divided by the variance at rest. This value is called the
normalized noise variance and used throughout the paper.
Since noise pulse duration was <15 s to avoid tissue damage, it was
difficult to obtain sufficient frequency resolution in the spectrum
near 1 Hz for a measure of background noise. The data windows were
spliced assuming that the output signals were stationary for at least
50 s. This is a valid assumption because the data were wide-sense
stationary to both the signal input and to the noise pulse.
Stationarity was determined by noting that the cellular response to the
periodic signal was unchanged for >2 min (all tested cells: failing
cells were rejected as unstable) and that both the mean and covariance
of the response to the noise pulse were independent of the time index
(3/3 cells tested) (Leon-Garcia 1994). The splicing did
introduce another periodic component (<0.1 Hz) that did not affect the
SNR calculations, as tested in both experimental and simulated data.
Noise modulation
To analyze the effect of physiological noise on signal detection
in neurons, endogenous noise sources must be modulated without affecting synaptic transmission. This was accomplished by changing the
excitability of CA3 cells without affecting CA1 cells or any synaptic
properties. Negative extracellular electric fields or cathodic currents
are known to depolarize cells (Jefferys 1995). It has
long been known that current injection can increase activity of CA3
cells (Wong et al. 1979
). When applied to the CA3
region, electric fields should therefore increase the neuronal firing of CA3 cells and thus also the synaptic noise on the apical dendrites of CA1.
Computer simulation
A simulation was performed of a single CA1 cell with distributed
noise sources and a subthreshold periodic signal. The CA1 cell was
implemented in NEURON software (Hines 1993) by using the
CA1 model in Stacey and Durand (2000)
(Table
1). The model includes the following: an
active soma with one sodium, one calcium, and four active potassium
channels (Warman et al. 1994
); passive dendrites; and
-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) synapses
(Destexhe et al. 1998
), some of which fired at
random intervals (Stacey and Durand 2000
) (Fig.
1D). The amplitude of synaptic events was modulated by
changing the maximum conductance (gmax) of the AMPA current. A periodic
synaptic signal was introduced on the apical dendrite. The amplitude
was adjusted so that it was subthreshold
(gmax = 5 nS) for a baseline somatic
noise of 12,000 µV2, the minimum baseline in
CA1 cells in the cell (Turner 1988
; Wahl et al.
1997
). Noise was added (gmax = 0.22 nS) at varying mean frequency. Noise events were scaled by a
Poisson distributed number (mean = 0.3). Synaptic noise variance
was computed by recording the current at the synapses
(pA2). Somatic noise variance, which is the value
recorded in the hippocampal slices, was evaluated by computing the
voltage variance (µV2) at the soma. The
synaptic current variance and somatic voltage variance were found to be
directly proportional. Simulated data were analyzed exactly as
explained above for the brain slice experiments, with the exception
that the simulations contained 50 s of data and did not need to be
spliced.
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RESULTS |
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Brain slice experiment
NOISE INCREASE DURING CURRENT PULSES. The effect of the application of the current pulse on the intracellular noise is shown in Fig. 2A. The noise was characterized by an increase in the frequency of depolarizing events resembling excitatory postsynaptic potentials (1.5-3 mV peak, 5-15 ms duration). During the application of a 50-µA pulse, the noise variance increased from 90,000 µV2 to 270,000 µV2. The increase was characterized by higher frequency and/or higher amplitude EPSPs. Occasionally multiple noise events superposed, causing longer, higher amplitude events (Fig. 2A, insets). The pulse does not cause any measurable change in resting membrane voltage. Pulses >100 µA evolved hydrogen bubbles and could not be used.
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SIGNAL DETECTION IMPROVEMENT.
The effect of noise on detection of subthreshold signals was
measured with intracellular recordings in CA1 neurons. A subthreshold, periodic input stimulus was applied to the stratum radiatum, generating an EPSP between 3 and 8 mV in the recorded cell. Addition of noise via
the DC pulse raised the response to the signal above threshold in 17 cells from 12 different slices. Recordings were performed for 55 cells,
but in 38 of them there was no increase in either noise or detection
during the DC pulse and the data were discarded. The large number of
failures was attributed in large part to the poor connectivity that
exists in a slice between CA3 and CA1 (Bernard and Wheal
1994). In a slice with poor connectivity, it is impossible to
produce synaptic noise by this method, so it would be impossible to
test for the presence of SR. All failed slices are documented here as a
conservative measure because it was not possible to determine in real
time whether each failure was due to lack of SR or of connectivity.
Therefore noise improved detection in at least 31% of the slices
tested, a number that could be much higher if failures due to poor
connectivity could have been ignored. The effect of adding the DC
current in a cell is shown in Fig. 3A. Before the addition of
noise, the signal (which causes negative artifacts in the raw data) is
not detected. During the DC pulse interval, 50% (6/12) of the pulses
are "detected," causing action potentials. Detection vanishes when
the pulse ends. The data are from the same cell and DC amplitude as
Fig. 2A. Two possible EPSPs, marked by stars, are visible
during the pulse. The calculated SNR from 13 of the successful
experiments are shown in Fig. 3B. (Four of the 17 cells
tested were not included in these measurements because the input
currents could not be measured.) Of the 13 cells that were tested for
multiple known current levels, there were only 2 instances in which the
SNR did not increase when the current was raised. A nonparametric sign
test was performed to test the hypothesis that increasing the current
increases SNR. For a null hypothesis that current and SNR are not
related, the probability of failure is at least 50% for each cell,
depending on the number of observations. Using this conservative
probability, the sign test for 13 cells and 2 failures shows that
increasing the DC current significantly increases the SNR
(P < 0.011). Since increased current also
significantly increased the noise (see previous section), it follows
that increasing the noise within the range of this experiment improves
detection of subthreshold signals in CA1 cells. It is interesting to
note that the SNR for 20 µA was clearly improved compared with 0 µA
despite the fact that noise (Fig. 2B) was not significantly
increased at that current level.
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EFFECT OF DC ELECTRODE LOCATION. The position of the DC electrode was changed in three cells to test the hypothesis that the improvement in signal detection is due to increased noise and not to an artifact of the DC pulse. As shown in Fig. 4A, the DC electrode was moved away from CA3 cells to a position equidistant from the CA1 region. A DC pulse administered from the secondary position had no effect on detection (n = 3). After replacing the DC electrode over CA3, the improved detection returned (Fig. 4B), showing that the improved detection was dependent on CA3 depolarization and not due to any field effect on CA1.
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EFFECT OF SEVERING SCHAFFER COLLATERALS. To test the hypothesis that increased noise/detection was dependent on synaptic activity in CA3 cells, three slices with increased noise during the DC pulse and improved detection were incised as shown in Fig. 4C, severing the Schaffer collaterals. Following the incision, intracellular readings were obtained for several neurons. In every case there was no change in noise or in signal detection for all of the postincision cells during the DC pulse (see Fig. 4D, Table 2). From the results shown in the two experiments in Fig. 4, we conclude that the increased noise variance in CA1 is due to axonal signals in the Schaffer collaterals and the improved signal detection is dependent on this endogenous noise source.
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Simulation results
A simulation was performed to examine the effect of adding noise to the cell beyond the range possible in the brain slice experiment. The simulation allowed performing multiple trials on the same cell as well as modulation of noise to levels much higher than those possible in the slice. As in the brain slice, there was no detection for the baseline noise, and the noise variance was augmented by increasing the mean frequency of Poisson distributed synaptic events.
Noise was added to the cell with a range from 12,000 to 700,000 µV2. The signal amplitude was set below threshold for noise variance of 40,000 µV2. The signal threshold and the low end of the noise range are similar to values measured experimentally. Increasing the noise amplitude to 120,000 µV2 progressively improved signal detection in the model (Fig. 5A). Noise variance of 70,000 µV2 improved detection to about 40% of the signal pulses (SNR = 21, compared with zero detection at 40,000 µV2). The maximum SNR occurred with noise at 112,000 µV2, which detected 70% of the pulses (SNR = 118). The steep increase in SNR for small noise intensity is characteristic of SR theory and is very similar to the results obtained experimentally.
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Higher levels of noise begin to corrupt the signal and lower SNR.
The data for this cell are slightly below those predicted by the SR
equation. This is in contrast to the response of neurons to
low-frequency, amplitude-modulated noise, which can exceed the
predicted values at high variance (Stacey and Durand
2000; Wiesenfeld and Moss 1995
). The decreased
values in this simulation were due in large part to an effect not
addressed by SR theory: oscillatory neuronal behavior. As the noise
variance increased to higher values, the neuron began to fire nearly
periodically, as shown in Fig. 5A (bottom). The
firing frequency increased with increasing noise, independently of the
signal frequency. The power spectrum in Fig. 5A shows that
the signal frequency is present in the output but is overwhelmed by the
large hump centered at 6 Hz. The oscillations due to high noise greatly
diminished the ability of the cell to detect the signal, lowering the
SNR below the value predicted by SR.
The results of the simulated data were fit to the SR equation and compared with the intracellular data obtained from four CA1 cells (Fig. 5B). The noise variance in each cell was normalized to its baseline level to facilitate comparison of the different cells. All five cells show similar qualitative effects within the experimental range of noise up to 10 times baseline. The figure shows that the SNR in this range increases with increasing noise. This positive slope lies on the left side of the SR curve. One cell (cell 3) actually detected 100% of the inputs during the 50- and 80-µA pulses. As noise increases beyond the experimental range, detection in the simulated results is corrupted as the neurons begin to oscillate but is still similar to SR theory.
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DISCUSSION |
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Choice of noise sources
The key element in testing the effects of SR on signal
detection is to ensure that the noise levels used are within the
physiological range. Previous work dealing with SR in the hippocampus
(Gluckman et al. 1996; Stacey and Durand
2000
) did not report measurements of intracellular noise. In
both cases, noise was evoked using random stimuli that created global
dendritic signals, which are not good representations of the noise in
CA1 (Lindner et al. 1995
). We have sought to overcome
these shortcomings by modulating the endogenous noise sources in the
slice. These sources include random release of neurotransmitter as well
as uncorrelated true synaptic events (Stacey and Durand
2000
), which have roughly the same quantal size (Larkman
et al. 1997
). Other methods for modulating synaptic noise such
as application of phorbol esters (Hestrin et al. 1990
; Malenka et al. 1987
), hyperosmolar solution
(Malgaroli and Tsien 1992
; McBain and Dingledine
1992
; Stevens and Tsujimoto 1995
; Wyllie
et al. 1994
), or protocol to evoke long-term potentiation (LTP)
(Malgaroli and Tsien 1992
) are not suitable for this
experiment because they also change the synaptic transmission
properties of the input signal itself. It was therefore necessary to
develop a novel method of noise modulation that does not affect
synaptic transmission. By applying a cathodic extracellular current to the CA3 region, the CA3 cells were depolarized without affecting the
CA1 cells. Depolarizing the CA3 cells increased their probability of
firing spontaneously. Since each CA3 cell could fire independently at
random intervals, this method created an increase in independent, random synaptic events spatially and temporally distributed on the CA1
apical dendrites. The noise produced was therefore locally uncorrelated, a good representation of the physiological CA1 noise. The
range of noise presented to the cells should lie within physiological limits for the improvement in signal detection to be considered relevant. The noise variance measured at rest varied from 4,000 to
90,000 µV2. This variance is comparable to
published values of 10,000-40,000 µV2 for CA1
cells in slices (Sayer et al. 1989
; Wahl et al.
1997
). With the application of the current pulse, the variance
reached maximum levels of 300,000 µV2, which
was six times the baseline noise. That level of noise was too small to
generate action potentials in the CA1 cells. It was not possible to
raise the noise any higher. Thus the noise evoked in vitro was below
the level of a detected synaptic event, clearly within the range of
inputs normally presented to a CA1 cell.
The simulation also produced noise by increasing the frequency of
distributed, independent noise sources. To produce the full range of SR
activity, the noise was raised to higher levels than those generated in
the slice. The plot in Fig. 5B shows the variance reaching
values 58 times the resting variance (700,000 µV2). This high level of noise could exist in
the cell if the hippocampus were in a very active state. Several
conditions in the slice increase noise variance, such as bath calcium
(Raastad et al. 1992), temperature (Finch et al.
1990
), paired pulse modulation (Mennerick and Zorumski 1995
), LTP (Malgaroli and Tsien 1992
), and
random axonal firing (Turner 1988
; Wahl et al.
1997
). Some of these sources alone are capable of increasing
the variance significantly, up to 30 times. Uncorrelated action
potentials from CA3 produce even higher noise variance. The variance in
a slice can potentially increase by at least 100 times with
combinations of these effects.
Even larger noise sources are present in vivo. They are behavior
dependent and contain theta rhythms (White et al. 1998), random pulse trains (Leung 1982
), and sharp waves
(Kamondi et al. 1998
), and they may express active and
passive states like other areas of the brain (Wilson and
Kawaguchi 1996
) that increase baseline variance 100-fold
(Destexhe and Paré 1999
). Thalamic signals are
also quite significant (Bertram and Zhang 1999
). These high levels, however, probably would be detrimental to normal brain
function after extended periods because they evoke constant, random
firing in CA1. All these noise sources provide a wide range of variance
that may be present in a CA1 cell, potentially reaching very high
levels at times. The simulations reported here generated noise within
only a small portion of this range, evoking noise that is easily within
the physiological range of the slice and only a fraction of the
potential range in vivo.
SR in simulated and in vitro CA1 cells
SR predicts that SNR will increase steeply for low noise levels
and then fall gradually after peak detection. The experimental data
(Fig. 3B) show significant improvement in signal detection as current is increased. Somatic noise variance also increases as the
current is raised, but the changes are less significant (Fig.
2B). Comparing these two figures indicates that average detection improved even for current that was too small to increase the
noise significantly (20 µA). The observation that small changes in
noise produce significant SNR improvement is a hallmark of SR. Due to
the limits on input current to avoid tissue damage and the limited
connectivity that exists between CA3-CA1 in a slice preparation
(Bernard and Wheal 1994), it was not possible to produce
noise large enough to evoke an action potential due to noise alone.
Because of this, the analysis of the in vitro experiment was limited to
the left side of the SR curve where noise is subthreshold.
The simulation was able to generate the entire range of noise amplitudes to allow curve fitting with the SR equation. Although the simulated cell does deviate slightly from the equation at high noise levels, it shows the characteristic properties of SR. Comparison with the experimental data suggests that some neurons actually have higher detection at low noise amplitude than the simulated cells (cells 3 and 4). We predict that, if presented with higher noise levels, the in vitro CA1 cells would begin to fire randomly, inevitably decreasing the SNR. We hypothesize based on the simulations that the cells' response would approximate the SR curve in Fig. 5B.
Physiological relevance of SR in CA1 neurons
CA1 neurons perform the complicated task of integrating tens of
thousands of synaptic inputs. Since CA1 neurons are the final integrators in the hippocampal circuit (Traub and Miles
1991), evaluating signal detection in these neurons should have
broad implications in understanding basic hippocampal function: the formation of memory. For a synaptic event to produce an action potential, up to 300 EPSPs must occur simultaneously (Andersen 1990
; Sayer et al. 1989
). Many of these synapses
are electrotonically distant and greatly attenuated at the soma
(Spruston et al. 1993
). This results in nearly countless
combinations that can evoke an action potential (Bernard and
Wheal 1994
). What determines whether a particular signal will
be detected? A straightforward method of signal detection is for 300 CA3 cells to fire a deterministic and simultaneous barrage at a
particular CA1 cell. But the broad spectrum of activity and the
propensity for random events in the CNS suggest that this deterministic
model is not sufficient to explain all signal detection in CA1. SR
provides a means to explain the stochastic nature of the system.
Recently, SR was suggested as a possible means of both using noise to
improve signal detection and overcoming the attenuation effects in
passive CA1 dendrites (Stacey and Durand 2000). Special noise modalities have also been shown to be able to further improve the
response beyond traditional SR (Feng and Tirozzi 2000
;
Locher et al. 2000
). To show that SR is actually
involved in signal detection in CA1 cells, it was necessary to show
that the effect occurred under physiological conditions with endogenous
noise. Improvement in signal detection occurred with very small amounts
of noise, clearly in the physiological range. Detection is not solely
dependent on 300 presynaptic neurons simultaneously firing but, for a
signal close to threshold, also can be improved by any synaptic
activity uncorrelated to the signal. The varied noise sources within
the hippocampus thus potentially provide the CNS with a method to control detection.
Conclusion
We have developed a method to modulate synaptic noise in CA1 cells
without changing the synaptic properties of the network. This method
allowed us to test the role of noise in improving signal detection in
rat hippocampal CA1 cells, as predicted by SR theory (Stacey and
Durand 2000). A computer model verified the experiment and
extended the results to higher noise levels. These results show that
noise can influence signal detection significantly. The effect is
especially prominent for signals close to threshold even with small
changes in endogenous noise. The large number of synaptic connections
and noise sources in the hippocampus can produce a broad range of
variance, providing the CNS with a powerful method to influence
detection of subthreshold signals merely by changing the
characteristics of the noise in the system.
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ACKNOWLEDGMENTS |
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The authors thank J. Hammel for helpful suggestions.
This work was supported by The Whitaker Foundation and the Medical Scientist Training Program at Case Western Reserve University.
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FOOTNOTES |
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Address for reprint requests: D. M. Durand, Dept. of Biomedical Engineering, Case Western Reserve University, CB Bolton Rm. 3510, 10900 Euclid Ave., Cleveland, OH 44106 (E-mail: dxd6{at}po.cwru.edu).
Received 5 September 2000; accepted in final form 16 May 2001.
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REFERENCES |
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