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NEUROSCIENCE |
1 Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, University of Tokyo, Tokyo 113-0033, Japan
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Abstract |
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(Received 27 February 2006;
accepted after revision 7 April 2006;
first published online 13 April 2006)
Corresponding author Y. Ikegaya: Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, University of Tokyo, Tokyo 113-0033, Japan. Email: ikegaya{at}mol.f.u-tokyo.ac.jp
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Introduction |
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We used this large-scale imaging in order to examine the response of hippocampal CA1 networks to afferent stimulation in vitro. The perspicuous CA1 network architecture can be a minimal model for understanding how single neurons and their synaptic connections combine to execute a coordinated function. In addition, its stereotyped laminar structure allows separating network input and output activities at the level of single neurons. Taking advantage of them, we aimed to examine (i) the input–output (I/O) relationship, (ii) trial-to-trial variation, and (iii) frequency-dependent spike transfer, at the network level.
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Methods |
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Oregon green BAPTA 1-AM, Pluronic F-127 and sulforhodamine 101 were obtained from Molecular Probes (Eugene, OR, USA). Chromophore EL, sulfinpyrazone, D,L-2-amino-5-phosphonopentanoic acid (AP5), 6-cyano-7-nitroquinoxoxaline-2,3-dione (CNQX), picrotoxin and gabazine (SR-95531) were purchased from Sigma (St Louis, MO, USA). Drugs were dissolved in double-distilled water or DMSO so as to make 1000x stock solutions, except for picrotoxin (100x). The stock solutions were stored at –20°C and diluted immediately before use. They were all bath applied.
Slice preparations
Preliminary data show that, as compared with acutely prepared slices, organotypic cultures are more consistently loaded with calcium indicators and offer improved imaging resolution, presumably because of less cellular debris (data not shown; see Morita et al. 2003). Therefore we decided to use slice cultures, rather than acute slice preparations, in this study, although synaptic connections in organotypic cultures might undergo unnatural remodelling during the incubation period (Robain et al. 1994; Sakaguchi et al. 1994; Gutierrez & Heinemann, 1999).
Hippocampal slice cultures were prepared from postnatal day 7 Wistar/ST rats (SLC, Shizuoka, Japan) as previously described (Yamamoto et al. 1989; Stoppini et al. 1991; Ikegaya, 1999), according to National Institutes of Health guidelines for laboratory animal care and safety. Briefly, rat pups were chilled and decapitated with a small animal guillotine (SN-629, Shinano manufacturing cooperation, Tokyo, Japan). The brains were rapidly removed and cut into horizontal 300-µm-thick slices using a DTK-1500 microslicer (Dosaka, Kyoto, Japan) in aerated, ice-cold Gey's balanced salt solution (Invitrogen, Gaithersburg, MD, USA) supplemented with 25 mM glucose. Entorhino-hippocampal stumps were cultivated on Millicell-CM membranes (Millipore, Bedford, MA, USA) for 7–14 days. Cultures were fed with 1 ml of 50% minimal essential medium, 25% Hanks' balanced salt solution (Invitrogen), 25% horse serum (Cell Culture Laboratory, Cleveland, OH, USA) and antibiotics in a humidified incubator at 37°C in 5% CO2. The medium was changed every 3.5 days.
Ca2+ imaging
Experiments were performed in artificial cerebrospinal fluid (ACSF) consisting of (mM): 127 NaCl, 26 NaHCO3, 1.5 KCl, 1.3 KH2PO4, 1.4 MgSO4, 2.4 CaCl2, and 10 glucose), bubbled with 95% O2 and 5% CO2. Slices were washed three times with ACSF, transferred into a 35-mm dish filled with 2 ml of dye solution, and incubated for 1 h in a humidified incubator at 37°C in 5% CO2. The dye solution is ACSF containing 10 µl of 0.1% Oregon green 488 BAPTA-1AM/DMSO, 2 µl of 10% Pluronic F-127/DMSO, 2 µl of 5% Cremophor EL/DMSO, and 2 µl of 100 mM sulfinpyrazone (Ikegaya et al. 2005). The final concentrations were 0.0005% Oregon green, 0.01% Pluronic F-127, 100 µM sulfinpyrazone, 0.005% Cremophor EL, and 0.8% DMSO.
After being washed, slices were incubated at room temperature for > 30 min, mounted in a recording chamber and perfused with 32°C ACSF at a rate of 1.0–1.5 ml min–1. Incision was made between the CA2 and CA3 regions and between CA1 and the subiculum to reduce recurrent excitation. Images (653 pixels x 492 pixels, 16-bit intensity) were captured at 10 frames s–1 with a CSU10 Nipkow spinning-disk confocal microscope (Yokokawa Electric, Tokyo, Japan), equipped with a Cascade cooled CCD camera (Roper Scientific, Tucson, AZ, USA), a Zeiss AxioSkop2 microscope (Oberkochen, Germany), water-immersion objectives (20x, 0.5 NA, Achroplan, Zeiss), and Metamorph software (Molecular Devices, Union City, CA, USA). Fluorophores were excited with the 488-nm line from an argon–krypton laser (15–20 mW, 641-YB-A01, Melles Griot, Carlsbad, CA, USA) and visualized with a 507-nm long-pass emission filter. Bipolar tungsten electrodes were placed in the CA1 stratum radiatum sufficiently apart from the imaged area to avoid direct stimulation of dendrites of the monitored neurons, and a single pulse or burst train stimuli (50 µs, 60–270 µA) were applied every 30 s to activate Schaffer collateral axons. To minimize photodamage and photobleaching, a laser shutter was opened during the 3–5 s period around the stimulation under the Metamorph Journal control.
Electrophysiological recordings
Patch-clamp recordings were obtained from CA1 pyramidal cells with an Axopatch 700B amplifier (Molecular Devices). For cell-attached and whole-cell recordings, borosilicate glass pipettes (4–9 M
) were filled, respectively, with ACSF and internal solution consisting of (mM): 120 potassium gluconate, 20 KCl, 0.1 CaCl2, 10 Hepes, 0.2 EGTA, 3.4 MgATP, and 5 QX-314 (pH 7.2). Signals were low-pass filtered at 1–2 kHz, digitized at 20 kHz and analysed with pCLAMP 9.2 software (Molecular Devices).
Data analysis
Spikes were reconstructed from neurons by using custom-written software in NIH ImageJ (Bethesda, MD, USA) and Microsoft Visual Basic (Redmond, WA, USA), as previously described (Ikegaya et al. 2004). For each cell and each stimulus, the fluorescence change 
F/F was calculated as:
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F/F measure did not significantly underestimate spike-triggered signals even during synchronous cell activation. Spike-triggered Ca2+ signals were automatically determined as a transient with > 3% amplitude, > 3.5% s–1 maximal first derivative (t
= 0.7 s) and 1- to 2-frame peak latency. They were then inspected by eye to remove noise detected in error. Spike-related events had fast-rise and slow-decay kinetics (
> 0.3 s), so they were separable from background or optical noise (Fig. 1B and C). To estimate the total amount of presynaptic inputs, the fluorescence change F/F
= (F1
–
F0)/F0 in the presence of 20 µM CNQX and 50 µM D,L-AP5 was obtained from CA1 stratum radiatum by placing a box (100-µm x 100-µm). This measure might reflect glial Ca2+ waves as well. But the area of glial cells occupied only a small portion (less than 5%) in the entire monitored region. Moreover, glial waves were usually unlocked to electric stimuli, and if there were any, they had much slower kinetics than spike-triggered neural Ca2+ transients. Thus, glial Ca2+ waves contributed only minimally to the amplitude of presynaptic Ca2+ transients, which had a latency of 1 frame. Data were discarded when massive spontaneous activity occurred during imaging.
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(i
= 1, ..., N) of cell i is represented as a binary vector (1: spike, 0: no spike), the similarity index (type 1) between 
and 
is defined as:
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and 
, taking values between 0 and 1. Unless otherwise specified, we used this definition as a similarity index.
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as a ‘1’ and ‘–1’ binary vector (1: spike, –1: no spike), instead of a ‘1’ and ‘0’ vector, and defined the similarity index type 3 as 
, in which normalization is unnecessary because all vectors have the same length 
. In this index, simultaneous ‘activity’ and ‘silence’ of two cells are equally weighted. A hierarchical clustering algorithm was applied to the similarity matrix to construct a dendrogram that assembles all elements into a single tree (Fig. 5B). The matrix was scanned to identify the highest value, and a node was created by joining these two cells. The similarity matrix was updated with this new node replacing the two joined elements. The same process was repeated N – 1 times until only a single element remained. We also manually highlighted neuron groups with the aid of a modified K-means algorithm (Fig. 5C). Cells were randomly assigned to one of K clusters, and the K centroids were determined. Each cell was re-assigned to a cluster to minimize the distance to the cluster centroid, and new centroids were determined. This procedure was iterated until the optimum assignment was attained. Then, the number of clusters (K) was increased with K = 2M (M = 1, 2, ...), and the same algorithm was repeated.
To determine whether synaptic inputs and membrane potentials immediately before stimulation influence subsequent spike generation, we compared intracellular traces simultaneously recorded from two cells by computing their Euclidian distance (Fig. 6B). For 30–250 ms fractions of the traces xt and yt before stimulation, the modified Euclidian distance ED was calculated as:
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and 
represents the mean membrane potential xt and yt, respectively, between 550 and 30 ms before stimulation, and t is the time resolution of whole-cell recordings (t
= 50 µs).
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Results |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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F/F transient, the amplitude of which was almost constant among trials (Figs 1C left, 2B and 3A) and could be separated from optical noise (Fig. 1C left). This all-or-none nature of somatic F/F signals was not due to the dye saturation because the somatic F/F amplitude increased as the number of spikes involved in a burst increased (Fig. 1C right). Thus, the somatic F/F transients reflect spike outputs, rather than postsynaptic potential, of the observed neurons.
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F/F is proportional to the total number of presynaptic fibres activated by stimulation, i.e. the sum of presynaptic inputs (Kerr et al. 2005). Indeed, the F/F peak increased in a gradual manner as the stimulus was strengthened (Figs 1D and 2B top). They were almost unchanged for a constant stimulus intensity (Fig. 3A); the coefficient of variance (CV) in the F/F size was only 0.06 ± 0.03 for 50 constant stimuli (n
= 3 slices). Therefore, we were able to separate network input and output activities, even though not at the same time, in the same slice. Quasilinear I/O relationship at the population level
In Fig. 2A and B, spike responses were reconstructed from 101 neurons. As stimulus intensity was increased from 60 to 170 µA, more cells fired spikes. We generated a black-and-white rasterplot that indicated a spike or no spike of each cell at each stimulus intensity (Fig. 2B right) and then collapsed it into an activity histogram, which represents the percentage of activated cells at a given stimulus intensity (Fig. 2B right-bottom).
The averaged I/O relationship is illustrated by plotting the percentage of activated neurons against presynaptic activity levels, i.e. ‘optical fibre volleys’ (Fig. 2C top, n
= 6 slices, each 5 trials). The number of spiking cells increased nearly linearly with the sum of presynaptic input. Presynaptic F/F transients (abscissa) and postsynaptic spike responses (ordinate) reached 100% almost at the same point. The presynaptic 100% point was defined as the saturated F/F level obtained at the highest stimulus intensity, whereas postsynaptic 100% indicates the point at which all observed CA1 neurons fired. Therefore, the I/O relationship was almost linear across the entire activity range (see also Kerr et al. 2005).
Gabazine, a GABAA receptor antagonist, abolished the linearity; gabazine reduced the minimal stimulus intensity required to activate all neurons and the threshold intensity at which the network started to react to stimulation (Fig. 2C bottom). It did not affect the relation between stimulus intensities and optical presynaptic fibre volleys (online Supplemental material Fig. 2A). Non-averaged raw data of these experiments are shown in Supplemental Fig. 2B. Similar results were obtained with 50 µM picrotoxin, another GABAA receptor antagonist (data not shown). It is reported that unlike picrotoxin, gabazine at low concentrations (e.g. 200 nM) blocks phasic inhibition without affecting tonic inhibition, but it blocks both phasic and tonic inhibition at higher concentrations (e.g. 10 µM) (Stell & Mody, 2002). We confirmed this with whole-cell patch-clamp recordings (Supplemental Fig. 1). Gabazine-induced collapse of the I/O linearity was found at 200 nM (Fig. 2C bottom). Therefore, phasic inhibition, rather than tonic inhibition, is likely to be responsible for the I/O linearity.
Trial-to-trial variability of postsynaptic neuronal responses
Upon examining the I/O relationship, we noticed that spike responses of individual cells varied from trial to trial (Fig. 3A). An example of trial-to-trial variation of 132 spiking cells during 50 consecutive stimuli at a fixed intensity (120 µA) is shown in Fig. 3B. A few neurons responded faithfully to the repetitive stimuli, whereas others fired much less reliably. Figure 3C illustrates the distribution of the mean firing probability of each cell. Figure 3D left histogram indicates the trial-to-trial fluctuation in the percentage of activated cells, which further was collapsed into the Fig. 3D middle histogram as a distribution map. The percentage of activated cells was 38.7% on average, but it unstably went up and down across trials, with S.D., i.e. the degree of trial-to-trial variation, equal to 8.9%. Thus, the postsynaptic net CV (0.23 = 8.9/38.7) was significantly higher than that of presynaptic F/F (= 0.06 ± 0.03), indicating that presynaptic variations, e.g. stochastic release of neurotransmitter or trial-to-trial variation in the number of presynaptic fibres activated by electric stimulation, if any, cannot fully explain the postsynaptic net variability.
To estimate the chance level of S.D., we performed Monte Carlo simulation, in which we created a surrogate rasterplot with a random number generator by setting the mean firing probability to 38.7%. In this ‘mock’ rasterplot, the S.D. value was 4.1%, smaller than that of the real data (Fig. 3D right). This held true for all 1000 repetitions of simulation. Another example is shown in Fig. 4B. We repeated the same experiments with different stimulus intensities (60–270 µA) and showed the data in Fig. 3E (n = 112 experiments), in which the S.D. values are plotted versus the overall mean firing probability because S.D. depends critically on the firing probability; note that S.D. naturally takes smaller values if the firing probability is extremely high or low. In 83 of 112 experiments, S.D.s (open circles) were significantly higher than chance (linked filled circles).
In the example shown in Fig. 3D, there happened to be a trend for spike probability to decrease over time. Although in most cases, network excitability was invariant over time (for instance, see Fig. 4A), we sometimes observed such slow fluctuations (gradual increase/decrease) in firing probability, like Fig. 3D. It is possible therefore that such gradual changes in global excitability might contribute to more-than-chance variability. We can partially rule out this possibility, based on two following reasons, however. First, the data shown in Fig. 3B–D were not extraordinarily different form other data in terms of the S.D. value and its chance level (indicated by the arrow in Fig. 3E), but rather they correspond to a case in which the variability was close to the chance level. Second, statistical significance was still confirmed by a non-stationary shuffling in which spike positions were randomly exchanged with either right or left neighbouring bins in the rasterplot (P < 0.001; data not shown); note this procedure preserves the slow fluctuation in global excitability (see Fig. 4C). Thus, the net variability cannot be fully accounted for by simple stochastic behaviours of individual cells or a slow change in net excitability.
There were commonly some trials which produced extremely high or low responses (for instance, Trial no. 16 in Fig. 3B and Trial no. 12 in Fig. 4A). To examine how much such occasional outliers contributed to the measure of variability, we again utilized the non-stationary shuffling (Fig. 4C). This procedure eliminated such extreme trials with preserving the spike probability of each cell as well as slow fluctuations of global excitability over time. S.D. obtained from this simulation was significantly lower than that of the real rasterplot. Therefore, occasional hyperactive or hypoactive trials significantly contributed to trial-to-trial variation, although they seemed unlikely to fully explain the total variation.
We now hypothesize that the net variation must come from certain structured dynamics, e.g. cell-assembly-like dynamics (see Supplemental Movie 1; the stimuli no. 2 and no. 4 invoked a similar cell subpopulation). To address this possibility, we computed the similarity of activation patterns between pairs of neurons. In 148-cell recordings in Fig. 5A, we compared 21 756 (= 148 x (148 – 1)) cell pairs by computing the type-1 similarity indexes (see Methods) and depicted a dendrogram. The configuration of the dendrogram, as expected, implies the existence of neuron subgroup dynamics (Fig. 5B). We next used the K-means algorithm as another cluster analysis. This clustering algorithm is sensitive to the initial cluster assignments and often drops into different local minima so that we can identify indistinct quasi-clusters by reiterating the clustering procedure. In Fig. 5C, we illustrated some examples of neuron groups found in the rasterplot in Fig. 5A. Interestingly, identical neurons often participated in different groups (for instance, neuron no. 55 is involved in Groups no. 1 and no. 2). Therefore, the subgrouping seems to be heterogeneous and flexible in nature. Neurons in the subgroups did not have any specific architecture in network geometry and were dispersed in space (Fig. 5D). To determine whether these cluster dynamics are robust, we computed three types of similarity indexes (types 1–3) and sorted their matrices with the K-means algorithm (see Methods). Clusters were evident in all cases (Fig. 5E-G). Therefore, the cluster dynamics are not mathematical artifacts.
We next questioned whether the cluster dynamics contribute to trial-to-trial variation. We modified the Monte-Carlo method. If the neuron order in a rasterplot, which is arbitrarily assigned, is ignored, cell clusters are conceptually identical to vertically grouped active bins. We thus scattered vertically clustered active bins (3–13 consecutive bins) in a blank rasterplot by keeping the total number of active bins equal to that in the original rasterplot (Fig. 4D). The trial-to-trial variability (i.e. S.D.) seen in this simulation was now similar to that in real data if the cluster size was larger than seven bins. Thus, the subgroup dynamics can increase the net variability to a realistic point.
What shapes the cluster dynamics, then? The most probable cause is spontaneously occurring synaptic activity because we activated the CA1 circuit by applying single-pulse stimulation per trial. To examine whether spontaneous activity is temporally correlated among neurons so as to yield the subgroup dynamics, we performed dual patch-clamp recordings from pairs of CA1 pyramidal neurons. Voltage was clamped at –90 and 0 mV to singularize EPSCs and IPSCs, respectively (Fig. 6A traces). As we expected, the correlograms revealed that spontaneous EPSCs and IPSCs were both synchronized between neurons, IPSCs being more correlated (Fig. 6A). Within a 2 ms jitter, 14.8% EPSCs and 34.0% IPSCs concurred in two neurons (n = 4 pairs).
To determine how much correlated noise contributes to coherent spike dynamics, we monitored stimulation-evoked spikes of two neurons in the current-clamp configuration. Stimulus intensity was adjusted to generate 30–70% of firing probabilities in both neurons. When a spike occurred, its latency relative to stimulation was short and invariant across trials and across cells; thus it reflected a monosynaptic event. Across trials, two neurons variably responded with spikes (S) or without spikes (N). As to response combinations, four types of paired responses existed, i.e. S–S, N–N, S–N, and N–S (for example, S–S denotes that both neurons fired). They can roughly be classified into two groups, i.e. the same behaviours (S–S or N–N) and different behaviours (S–N or N–S). To examine whether these behaviours resulted from the differences in preceding membrane potentials, we compared the Euclidian distance between two intracellular traces in the period from 250 to 30 ms before each stimulus (see Methods). This measurement represents the ‘dissimilarity’ between two membrane potentials immediately before stimulation. The distance was slightly, but significantly, shorter when two neurons displayed the same responses (S–S or N–N), as compared to different responses (S–N or N–S). Thus, two neurons that received similar background noises before stimulation tended to produce the same responses (S–S or N–N).
Band-pass filtering properties in CA1 spike transmission
Firing rates of hippocampal neurons range widely from < 1 Hz to > 100 Hz and play an important role in encoding external information (O'Keefe & Dostrovsky, 1971; Wilson & McNaughton, 1993). The final series of experiments was designed to describe the input frequency-dependent responses. The stimulus intensity was lowered to a subthreshold level, which did not evoke a spike by a single-pulse stimulus, and the Schaffer collaterals were activated by a four-pulse train at various rates (5–200 Hz) in the presence of 50 µM AP5 to avoid induction of synaptic plasticity. We did not discriminate spiking activity across four train pulses because of the insufficient frame rate in our movie (i.e. 10 fps scanning versus 5–200 Hz trains); instead we focused on whether or not at least one spike occurred during a train stimulus. Trains were repeated 10 times, and the firing probabilities were averaged within each neuron to determine which frequency more or less reliably activated the neuron. Data of 93 neurons in a slice (Fig. 7A) are massed across neurons in Fig. 7B. The summed activity displayed a frequency preference. On average, the network responded more reliably in the near-gamma frequency (20–40 Hz) range, acting like a band-pass temporal filter. Individual neurons, however, responded differently to train stimuli. Their filtering properties were expediently categorized into low-pass-like, band-pass-like, and high-pass-like filters by thresholding the mean firing probability at 50% (Fig. 7C). Data from seven slices are summarized in Fig. 7D. Cells that did not show spikes or frequency preference were excluded from data analysis because in these cases, stimulation intensity might not be optimal to produce the filtering properties. Data indicate that 63.9% of neurons showed band-pass-like properties, whereas other cells behaved like a low-pass (9.5%) or high-pass (26.6%) filter in the frequency range tested here. When perfused with 200 nM gabazine, almost all neurons started to fire in the entire frequency range (data not shown). Thus, stimulus intensity was reduced to a point that filtering responses emerged in > 80% neurons. It now turned out that all these neurons had a high-pass-like transfer function (Fig. 7D). The same results were obtained with 50 µM picrotoxin (data not shown).
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Discussion |
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Central neurons are known to react to repeated presentation of the same stimulus with high variability (Henry et al. 1973; Tomko & Crapper, 1974; Rose, 1979; Softky & Koch, 1993). Evidence is accumulating that this variability results from random ongoing background activity (Arieli et al. 1996; Azouz & Gray, 1999; Kisley & Gerstein, 1999). The variability, however, has long been examined in one or a few neurons and remains to be elucidated in a large cell population. We have shown that trial-to-trial variation seen at the single-cell level does not average out even at the population level and that the variation is higher than would be expected by chance. Interestingly, this non-stationarity seemed to arise from time-varying recruitment of different neuron subpopulations because mimicking cell-assembly-like metadynamics increased the net variability to a point that met with our empirical data.
What brings about the cell-assembly-like dynamics? Axonal electrical coupling and recurrent excitation, both of which are present in CA1 pyramid–pyramid circuits (Deuchars & Thomson, 1996; Draguhn et al. 1998), are possible factors. In addition, we found that background synaptic noise is weakly correlated in space and time. The coherent noise could depolarize (or hyperpolarize) a metapopulation of neurons and yield dynamic subset behaviours. This seems consistent with work by Cobb et al. (1995) who found that single GABAergic interneurons can synchronize the firing of a set of hippocampal pyramidal cells. One direct approach to address our hypothesis is to investigate the effect of gabazine and picrotoxin. Unfortunately, these antagonist extremely enhanced the firing probability even at very weak stimulation intensities, leading the population dynamics to a nearly all-or-none form (see Fig. 2C), and we hence were unable to perform this experiment. Instead, we carried out intracellular recordings from two neurons and tried to compare their spiking behaviours with membrane potential dynamics immediately before stimulation. This idea was based on a report showing that membrane potential fluctuations reliably designate spike timings (Mainen & Sejnowski, 1995). We indeed found that, when two neurons displayed similar kinetics of ongoing fluctuations in membrane potentials, these neurons tended to produce the same responses to a subsequent stimulus. Thus, the correlated background noise is likely to contribute to the dynamics of cell-assembly formation.
Quasilinear I/O relationship
In general, the linearity reasonably helps minimize a loss of information transfer because the graded output covers a wide range of input levels. Our data show that the summed activity of individual neurons is apparently proportional to the overall network input. At the single neuron level, it has already been shown that CA1 dendrites can linearly integrate synaptic inputs (Cash & Yuste, 1999; Gasparini & Magee, 2006). The linear dendritic summation, however, occurs only within individual cells and is not necessarily linked to the net linearity; note firing is an all-or-none event of a cell and its reliability is not determined only by synaptic input levels but is also affected by many other factors, including spike threshold, channel noise, adaptation and refractory period. It is thus surprising that the non-linear units (spikes of single neurons) somehow produce the global linearity. Computational simulation shows that synaptic noise can shape a linear population response (van Vreeswijk & Sompolinsky, 1996). Consistent with this, we found that the linearity was distorted by GABAA receptor antagonists, suggesting the role of inhibitory background noise. Because even a low concentration of gabazine abolished the linearity, phasic inhibition, shown to be correlated between neurons (Fig. 6A), is required for the I/O linearity.
Band-pass filter
GABAA receptor antagonists shifted the band-pass filtering property of CA1 neurons to a high-pass function. Thus, the band-pass filtering depended on inhibitory synaptic transmission, rather than short-term monosynaptic plasticity (Fortune & Rose, 2001) or intrinsic cellular resonance (Pike et al. 2000; Fellous et al. 2001; Izhikevich et al. 2003). In other words, without inhibition, CA1 neurons simply act like integrate-and-fire units, that is, EPSP accumulation is more efficient at higher frequencies. In this case, we do not think that background noise plays a pivotal role. In these experiments, the afferent activation was repeated four times, and therefore polysynaptic inhibition contributes to spike responses. In hippocampal local circuits, inhibition flow dynamically changes depending on the input frequencies (Andersen et al. 1963; Pouille & Scanziani, 2001, 2004; Mori et al. 2004). Such a frequency-dependent switch in GABAergic feedforward/feedback routing may also contribute to the band-pass-like transfer function.
Summary
In this work, we imaged input and output activities from large neuron populations to elucidate how single CA1 neurons execute integrative dynamics at the multicellular level. The main findings are: (1) the network responds nearly in proportion to single-pulse inputs, but non-linearly to multipulse inputs, i.e. in a band-pass manner, and (2) the net responses vary from trial to trial, beyond chance levels. In conclusion, even in simple CA1 circuits, neurons can work in harmony together to make an organized output to the cortex although their individual responses are ostensibly unreliable and stochastic. To extrapolate our results, which were obtained from organotypic cultures, to in vivo brain activity, studies are underway at higher levels of experimental preparations, including the whole hippocampus in vitro and the brain of anaesthetized animals.
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