Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Neurosci. Author manuscript; available in PMC 2012 October 9.
Published in final edited form as:
PMCID: PMC3467103

‘Slow activity transients’ in infant rat visual cortex: a spreading synchronous oscillation patterned by retinal waves


A primary feature of the preterm infant electroencephalogram is the presence of large infra-slow potentials containing rapid oscillations called Slow Activity Transients (SATs). Such activity has not been described in animal models, and their generative mechanisms are unknown. Here we use direct-current and multi-site extracellular, as well as whole-cell, recording in vivo to demonstrate the existence of regularly repeating SATs in the visual cortex of infant rats before eye-opening. Present only in absence of anesthesia, SATs at post-natal day 10-11 were identifiable as a separate group of long-duration (~10s) events that consisted of large (>1 mV) negative local-field potentials produced by the summation of multiple bursts of rapid oscillatory activity (15-30 Hz). SATs synchronized the vast majority of neuronal activity (87%) in the visual cortex before eye-opening. Enucleation eliminated SATs, and their duration, inter-event interval and sub-burst structure matched those of phase III retinal waves recorded in vitro. Retinal waves, however, lacked rapid oscillations, suggesting they arise centrally. Multi-electrode recordings showed that SATs spread horizontally in cortex and synchronize activity at co-active locales via the rapid oscillations. SATs were clearly different from ongoing cortical activity, which was observable as a separate class of short bursts from P9. Together our data suggest that in vivo, early cortical activity is largely determined by peripheral inputs--retinal waves in visual cortex--which provide excitatory input, and by thalamocortical circuitry, which transforms this input to beta oscillations. We propose that the synchronous oscillations of SATs participate in the formation of visual circuitry.

Keywords: EEG, preterm, development, spontaneous, plasticity, spindle-burst


Correlated spontaneous neuronal activity regulates a number of developmental processes, including the formation, retention and elimination of synapses, as well as proper connectivity and map-formation (Zhou et al., 2003; Moody and Bosma, 2005; Colonnese and Constantine-Paton, 2006; Ben-Ari et al., 2007; Huberman et al., 2008). Consequently, a number of unique patterns of network activity are observed during early development (Khazipov and Luhmann, 2006). In sensory cortical areas the most robust network activity observed in rats during the first post-natal week are ‘spindle-bursts,’ a 0.5 – 3 second burst of rhythmic, alpha-beta (5-30 Hz) activity that is triggered in somatosensory cortex (S1) by spontaneous myoclonic twitches and in visual cortex (V1) by phase II retinal waves (Khazipov et al., 2004; Hanganu et al., 2006; Yang et al., 2009). Electroencephalographic (EEG) studies in human pre-term infants reveal a homologous pattern, the ‘delta-brush’ which occurs spontaneously, but also, like spindle-bursts, in response to spontaneous myoclonic twitches (Milh et al., 2007).

Recent advances using direct-coupled (DC) EEG recordings in preterm infants have demonstrated the existence of developmentally regulated slow activity transients (SATs), which organize most of activity in premature human neonates in temporal and occipital regions (Vanhatalo et al., 2005; Tolonen et al., 2007). SATs are multi-band events, characterized by a large (up to 700μV), infra-slow (<0.5Hz) negative wave which nests activity at several higher frequency bands. Currently there is no animal model of SATs, and their mechanistic basis of this critical network activity is unknown. Several long-duration activity patterns occurring in immature cortex suggest two different models for SATs. A cortical generator is suggested by the existence of long-duration migrating calcium waves and electrical oscillations identified in isolated cortical slices (Garaschuk et al., 2000; Dupont et al., 2006; Allene et al., 2008) and verified in vivo (Adelsberger et al., 2005; Yang et al., 2009). On the other hand, long-duration, multi-frequency ‘mega-bursts’ occurring before eye-opening in ferret visual cortex in vivo are eliminated by enucleation (Chiu and Weliky, 2001), suggesting such activity is dependent on peripheral input, in this case spontaneous retinal waves. Furthermore, the similar oscillatory structure of SATs and spindle-bursts suggests a common generative mechanism dependent on peripheral stimulation, but to date infra-slow potentials and long-duration activity similar to SATs have not been observed in the rat.

To identify and study SATs we used DC recordings of spontaneous activity in the visual cortex of unanaesthetized rats before eye opening. We show the development of long-duration infra-slow waves containing rapid oscillations during the second post-natal week that match the description of human SATs. Generation of SATs in visual cortex results from the long-lasting excitation provided by phase III retinal waves (Blankenship et al. 2009; Kerschensteiner and Wong, 2008), but also from the thalamocortical circuitry which converts this input into a highly periodic beta-oscillations that synchronize cortical activity.

Materials and Methods

Animal care

All experiments were performed in accordance with INSERM and NIH guidelines for the care of animals in research, and were approved by the local review committee. P0 was the day of birth, and 90% of the rats opened their eyes between P13 and P14. The majority of experiments were performed with Wistar rats for correspondence with previous experiments. Albinism is associated with a reduction in visual acuity (Prusky et al., 2002) and an increased crossing of RGC axons (Lund, 1965). In light of this we recorded from the monocular region of visual cortex and limited our analysis to the earliest visual development. Cortical activity in nine Long-Evans rats examined at the critical P9-11 time point could not be differentiated from that of Wistar rats. These rats were used for the experiments reported in Figures 4D and and5C.5C. Recording techniques have been extensively presented (Hanganu et al., 2006; Minlebaev et al., 2007, 2009). In brief head-fixed rat pups were prepared for extracellular recording under isoflurane anesthesia (2-3% depending on age and verified by toe pinch). Xylocaine analgesia was applied to all cut surfaces and buprenorphine (0.02 mg/kg I.P.) was given to manage pain. Recordings were made one hour after recovery from anesthesia. Pups were kept restrained for less than 5 hours, and were closely monitored for signs of stress and that they spent a normal proportion of their time sleeping. Body temperature was maintained between 35 and 36°C. The animals were kept in low-light conditions throughout all experiments. For encleation experiments the animals were re-anesthetized with 2-3% isoflurane in the recording set-up and the eye removed. Local anesthetic (2% xylocaine) was applied to the wound and 10 min allowed before removal of anesthesia. For eye injection, the eyelid was opened during the initial surgery. Before injection xylocaine (1%) in PBS was applied to the eyeball every 20 minutes. A puncture was made at the ciliary margin, a Hamilton syringe needle (30ga) inserted into the vitreous, and solution injected (5-10 μl).

Figure 4
Characteristics of spontaneous retinal waves and visual cortex SATs
Figure 5
Minimal anesthesia levels eliminate SATs

Electrophysiological recordings and analysis

Recordings were made either with pulled glass microelectrodes (1-2 MΩ) coupled to a direct-current amplifier (Axon Instruments), or multi-site linear array ‘Michigan Probes’ (Neuronexus tech) coupled to a custom build AC amplifier (1000×, bandpass 1 Hz-5 kHz). V1 recordings were localized at 2.8 - 3.2mm lateral to midline, and 0.0-0.5 rostral to lambda. Electrode location was verified post-mortem via tissue damage (glass electrodes) or dye localization (silicon probes). In animals older than P8 the electrode was further confirmed to be in V1 via monitoring of the response to 100ms light flashes (Colonnese unpublished data). Frequencies below 1-2 Hz were poorly transmitted by the Michigan probes and our amplifier; therefore all multi-electrode recordings were high-pass filtered at 2 Hz. All recordings were amplified 1000×, recorded in Axoscope, and analyzed with Clampfit (Axon Laboratories) and Matlab.

Layer identification was accomplished via multiple criteria. Anatomical sections show layer 4 to be located 300-500μm below the pial surface. As shown here, and by others, current sinks to thalamic input are maximal at the layer 3-4 border at these ages (Molnár et al., 2003). In almost all animals older than P8 we observed large units with a high rate of tonic spontaneous activity between 500-600μm depth, previously identified as layer 5a (Le Bon-Jego and Yuste, 2007). For purposes of this study, single electrodes were placed in layer 4 by locating the maximal field potential deflection for the fast-oscillations 300-500μm below the pial surface. For multi-electrode recordings the electrode was lowered until the top most contact just touched the cortical surface. Layer 4 was defined as the electrode 300-500 μm deep that was also 100-200μm above presumptive layer 5a. Layers 2/3 were defined as the electrodes more than 100 μm from the pial surface and separated by one electrode from layer 4 (i.e. we left a ‘buffer’ electrode). Layers 5/6 were the remaining electrodes below 5a and above 1000μm depth. Electrodes were labelled with diI and the electrode tracks confirmed to be in V1 by post-mortem analysis. Multi-unit firing was identified by high-pass filtering above 300Hz and simple threshold discrimination (more than 4.3 times SD of baseline noise). Good discrimination was verified for each channel.

Blind whole-cell patch-clamp recordings were performed using an Axopatch 200A amplifier (Axon Instruments, Union City, CA) using an in vivo patching technique similar to that previously described (Khazipov et al., 2004). The pipettes were filled with the following solution (in mM): 135 Cs-gluconate, 2 MgCl2, 0.1 CaCl2, 1 EGTA, and 10 HEPES, pH 7.25. Membrane potential values were corrected for liquid junction potential of +12 mV and series resistance.

For retinal recordings retinas were acutely dissected in ice cold oxygenated ACSF (in mM:126 NaCl, 3.5 KCl, 1.2 NaH2PO4, 26 NaHCO3, 1.3 MgCl2, 2.0,CaCl2, and 10 D glucose,pH 7.4) under red light. For recording a retina was placed ganglion cell layer up and continuously perfused (3ml/min) with 34°C ACSF in the dark. Insulated nivachrome wire(50 μM) electrodes were lightly touched to the surface to record action potentials. Between one and four large amplitude units could be distinguished at each location.

Multielectrode recordings

10 SATs were manually selected from each of 8 P10-11 pups, each with a duration 8-10 s and clear beta-oscillations, and confirmed to have unit activity on each channel. For trough-triggered averaging, all layer 4 events with a negative deflection greater than 75 μV and lasting less than 10 ms were automatically detected. The current source density of the average current was calculated for each animal using the technique of Mitzdorf (1985). An average spike rate was also calculated and normalized to the peak spike-rate for that pup. Population averages were calculated from the animal averages (n=8). Cross-correlation analysis was performed for each animal using a 5 ms spike-rate bin after normalization for the mean spike rate for each channel. The population average was calculated from the animal averages. The recordings used for Fig. 8A-B were performed with wire electrode arrays (500 μm tip separation) as described (Hanganu et al., 2006).

Figure 8
SATs synchronize superficial layers as a spreading oscillation

Statistical analysis

All population distributions were evaluated for normalicy (KS-test) and the appropriate descriptive statistic chosen on this basis, as denoted in the text (S.D. = standard deviation).


Spontaneous neuronal activity was recorded from the monocular region V1 of head-fixed unanesthetized rats between post-natal day (P)5 and 13. Recordings were made during all states of vigilance, though periods of active movement were not routinely analyzed as a caution against movement artefacts. To fully characterize network activity we used DC extracellular recordings of the local field potential (depth EEG) and multiple unit activity (bandpass 0-5000 Hz) from layer 4 (depth 300-500 μm from the cortical surface).

In P10 and P11 rats, the most prominent feature of the depth EEG was the presence of steadily recurring large-amplitude negative infra-slow potentials (Fig. 1 and Fig. 3A for Wistar examples, and Fig. 4D and and5C5C for Long-Evans examples). These negative potentials had a median duration of 8.75 s (6-18 s min-max, n = 80 events from 8 pups), and a median interval of 60 s (41-72 s min-max) that were not different between strains. The infra-slow wave was composed of multiple shorter events (Fig. 1B & C) that resembled ‘spindle-bursts’ previously described during the first post-natal week (Hanganu et al, 2006). The leading negative phase contained 3-15 (median = 5) spindle-bursts (duration 0.5- 3 s, median = 0.95 s) of field-potential oscillation in the beta-band (17 – 29 Hz peak frequency, median = 21 Hz). These bursts of beta-band oscillation were separated by short periods (0.5 – 1.5 s) of reduced activity within the larger infra-slow potential. Multi-unit activity (MUA) occured largely during the spindle-bursts, and was strongly correlated with the negative troughs of the beta oscillations (Fig 7).

Figure 1
Recurring Slow Activity Transients (SATs) in visual cortex of infant rats
Figure 3
Enucleation eliminates SATs
Figure 7
Beta-oscillations during SATs synchronize activity in superficial layers

As described above, shorter (0.5-3 s) spindle-shaped bursts of alpha/beta-band oscillation, named ‘spindle-bursts’ have been previously recorded during the first post-natal week in V1 and S1. When DC recordings are made a delta-band component of spindle bursts is observed in S1 (Marcano-Reik and Blumberg, 2008; Minlebaev et al., 2009). However these negative potentials are much shorter than the infra-slow waves, described here in an immature animal model for the first time. In fact, these infra-slow events events resemble multiple spindle-bursts occuring in rapid succession. While spiking events with similar characteristics have been described as ‘macro-bursts’ in ferret cortex (Chiu and Weliky, 2001, 2002), for the purposes of this paper we will refer to these events as ‘rat visual cortex (rv)SATs’, or just SATs for short, to emphasize their similarity to the human EEG.

As observed for the first post-natal week (Hanganu et al., 2006), spontaneous activity was characterized by long-periods of quiescence between SATs, and isolated action potential activity in was rare with the exception of a population of large units in presumptive layer 5a (see methods) which demonstrated persistent tonic activity. Unlike the first post-natal week, however, from P9 we observed spontaneous neuronal activity not associated with spindle bursts/SATs (Fig. 1C). These shorter electrographic events consisted of simple field negative shifts with strong multiple-unit activity. Unlike SATs and spindle-bursts, these short bursts did not display rhythmic oscillations of either the field potential or MUA. We have not analyzed these events in depth in the present study, but instead focus on the origins and characteristics of the SATs.

Development of SATs and other cortical activity patterns

To characterize the development of SATs and other activity, we systematically recorded spontaneous activity between P5 and P13 (one day before eye opening). Spontaneous activity between P5-7 (n=50 events from each of 11 animals) was also marked by the presence of recurring SATs (Fig. 2A). The appearance and duration of the infra-slow component was more variable at these ages, but we observed many SAT with long duration (>5 s) negative waves similar to those observed during the second week; however, the beta oscillations and MUA activity within these waves was shorter (median 2.2 s) resulting in a long return of the field potential to baseline during which there was no significant neuronal activity. The other primary difference between the P5-7 and P10-13 animals was the absence of short bursts. While shorter events (400 - 1000 ms) were common, these events always had a prominent oscillatory component similar to the longer events, suggesting they are similar network events of varying length (Fig. 1D).

Figure 2
Development of SATs and short bursts

We examined the developmental trajectory of this apparent dichotomisation of activity by quantifying the duration of all observed events between P5 and 13 (at least 200 events from 3-4 animals at each age). Duration was defined by the continued negative deflection of the DC field potential occurring in combination with an elevated MUA rate. The most apparent change during this period was a widening of the dynamic range for event duration (Fig 2B). Very long events (>5 s) were first observed on P8 and continued until the end of the recording period. Short events (<400ms) were first commonly observed on P9 and become promient on P10. Their occurrance continued to increase until P13 by which time they were the large majority of spontaneous events (Fig. 2E). Another dramatic change in the statistics of spontaneous activity was the establishment of a clear separation of short bursts and SATs, as the number of medium length (2 – 5 s) events was strongly reduced around P10 (Fig. 2C).

This splitting of events with maturation was also observed in the relationship between event duration and beta-oscillations (Fig. 2D). From P5-7 all events, regardless of duration displayed a strong rhythmic oscillation with a peak frequency between 8 and 31 Hz (n=50 events from each of 11 animals). There were two prominent distributions centered at 22Hz and 10Hz, though duration was not a determining factor in distinguishing events at each frequency, which likely arose as harmonics of the same oscillation. Thus in terms of duration and frequency all events between P5-7 appear to be drawn from a single distribution, quantitatively confirming previous assertion that spindle-bursts constitute a singular event in during the first week in visual cortex (Hanganu et al., 2006). Between P10-11 the two populations of events were clearly sseparable by both peak frequency and duration, with almost all events greater than 5 s displaying a peak frequency between 18 and 30 Hz, while shorter events almost always had peak frequencies below 10 Hz. Thus by the second post-natal week at least two populations of events can be clearly distinguished.

We further estimated the amount of neuronal activity associated with SATs. Total counts of action potentials over entire recording session revealed that 87 +/- 5% (mean, S.D.; n=8 30 minute recordings P10-11) of layer 4 MUA activity occurred during SATs.

Begining at P12 (at least 100 events from 8 animals) spontaneous activity became much more continuous and the occurence of SATs become less regular. We observed long periods of repeating short events that now resembled the slow waves observed during intermediate and deep sleep. At these ages, while SATs still occurred, they were no longer the dominant activity pattern and could often not be separated from ongoing cortical activity. No SATs were observed in the rats older than P14 (30 min from n=15 P14 – P19).

The clear segregation of SATs from other activity patterns between P10-11 allowed us to study these events in isolation.

Retinal drive of SATs

We used enucleation of the contralateral eye to test the role of retinal activity in generating SATs. Baseline spontaneous activity was first recorded. Then, as a control for surgery, animals were anesthetized, allowed to recover for 30 minutes, and baseline spontaneous activity was again recorded. Following this, animals were enucleated under the same anesthesia and allowed the same recovery time. Transient anaesthesia had no effect on SAT production, but enucleation completely eliminated SATs (Fig. 3). Simultaneous recordings made in V1 ipsilateral to the enucleation showed no change (not shown). The distribution of event duration was quantified for 50 events from each of the 5 pups before and after enucleation (KS-test, control vs. enucleated, p < 0.0001). The elimination of SATs was accompanied by an increase in events of moderate duration (1-3 s; Fig. 3B). Interestingly, these moderate length events sometimes displayed prominent rapid oscillations in the field potential, and where similar to spindle-bursts recorded during the first postnatal week. We examined the effect of enucleation on the generation of rapid oscillations by measuring total frequency power (1-50 Hz) during 30 minute periods before and after enucleation. Enculeation strongly attenuated the frequencies associated with the rapid component of SATs (10-40 Hz; peak attenuation at 22 Hz), but increased in power in below 10 Hz, reflecting the increased occurence of short-duration events. Similar results were obtained after suppression of retinal network driven activity by intraocular injection of urethane (see below). In total these experiments suggest that while oscillatory events of short duration can be generated in visual cortex in the absence of sensory input, the generation of long-duration SATs requires retinal input.

Phase III retinal waves have a similar macro-structure as SATs, but lack beta-oscillations

We examined spontaneous activity in acutely excised retinas in vitro (n=84 locations from 10 P10-11 retinas) for evidence of SAT-like activity (Fig. 4). Spontaneous retinal activity in the rats at this age was similar to the patterns decribed for phase III retinal waves in mice (Blankenship et al., 2009; Kerschensteiner and Wong, 2008). Spiking actvity in the ganglion cell layer was grouped into regularly repeating burst-clusters (median interval 53.5 s; median duration 9.5 s) composed of multiple sub-bursts (median 6). The distributions for the bursts-cluster duration and inter-burst-cluster intervals revealed extensive overlap and no significant differences from V1 SATs (Mann-Whiney, p>0.05; Fig. 4B). We examined the spike rate auto-correlations of the retinal bursts (5 ms bins; 500 ms window) for evidence of rapid oscillations similar to those observed during SATs. Unlike V1 SATs, retinal autocorrelation coefficients revealed only a steady decrease with time, and no evidence of beta-band oscillations (Fig. 4C).

We tested the hypothesis that the occurrence of SATs is determined by retinal activity by increasing the occurrence of retinal waves via intra-ocular injection of bicculine (Bic) and strychnine (Str) which increases the rate of wave initiation in vitro (Blankenship et al., 2009). Contralateral Bic (20μM) + Str (40μM) injection(5-10 μL) massively increased cortical activity, resulting in the constant production of alpha-beta oscillations (spindle-bursts) separated by short (1-5s) silent periods. Well defined SATs could not be resolved, and were replaced by constantly modulating negative potentials (Fig. 4D). To quantify the effect we calculated the interval between individual bursts of alpha-beta oscillation (> 3 cycles separated by > 500 ms) following control injection of ACSF and again following Bic + Str injection. In 5/5 P10-11 pups the distribution of event intervals (50 per animal) was significantly shifted (K-S test, p < 0.001 in all cases) from a bimodal distribution to an exponential distribution (Fig. 4E). In 4/4 animals in which we made simultaneous recordings from ipsilateral V1, no change was observed (p > 0.05). The increased activity resulted in a selective increase of frequencies between 20 – 30 Hz (30 minute recordings per pup; Fig. 4F).

These results, in combination with the enucleation and intraocular urethane injection experiments, suggest that spontaneous retinal activity is transferred though LGN to cortex to drive SATs. They further predict that the length, sub-burst structuring, and occurrence of V1 SATs (i.e. the macro-structure) are determined by the characteristics of retinal activity, while the prominent rapid oscillations that occur during SATs are generated in thalamus or cortex as a result of the burst excitation provided by retinal input.

We examined the forepaw region of S1 for evidence of SAT-like activity (Supplementary Fig. 1). DC recordings between P10-11 (30 min from n=3 pups) showed no sign of the regularly recurring SATs observed in V1. The only infra-slow activity observed was infrequent, irregular and of very long duration (> 30 s). Thus retinal wave driven SATs do not appear to propagate to S1, where activity is instead driven by the topographically appropriate somatic receptors.

SATs are eliminated by low levels of anaesthesia

Because SAT-like mega-bursts were observed in unanaesthetized ferret (Chiu and Weliky, 2001) but not anesthetized mouse V1 (Rochefort et al., 2009), we examined the effects of isoflurane and urethane anaesthesia on the generation of SATs at P10-11 (Fig. 5). SATs were eliminatd by concentrations of either anaesthetic that reduced spontaneous movement, but did not prevent foot withdrawl to light squeeze. Under either anesthetic short bursts remained, and the event distribution was shifted from two clear peaks, to a single peak below 1s duration (n=50 events for each of 3 pups for each anesthetic, KS-test vs unaenesthetized, p < 0.0001 for both groups). Thus in V1 SATs are more sensitive to anesthesia than short bursts and their examination requires the use of unanesthetized animals.

We examined the locus of the anesthetic effect by intra-ocular injection of urethane (5-10 μL 100mM). Retinal injections rapidly eliminated SATs (<5 min), but not short-bursts, in 4/4 rats (Fig. 5C; n=50 events per animal per condition, KS-test ACSF vs injected, p < 0.0001). Animals that received retinal urethane injections had a more frequent short-bursts than I.P. injected rats, suggesting a central locus for short-burst generation but a retinal locus for SATs. These data are complementary to the enucleation experiments as they show that SAT elimination can be accomplished by a less damaging disruption of retinal activity. Furthermore in total this data strongly suggests our short-bursts are the same activity recorded by Rochefort et al (2009) and determined to be a primitive version of ‘slow-wave’ activity.

Synaptic correlates of SATs

We examined the synaptic composition of SATs with whole-cell voltage clamp recordings made in close proximity (200-400 μm) to the field electrode (Fig. 6). Neurons located 300-600 μm below the cortical surface (n=5 from 3 animals) showed a close corresponence between negative field-potential deflections and whole cell glutamatergic synaptic currents (-80 mV holding potential). Both the infra-slow and rapid components of the SAT could be observed in the single cell currents. The infra-slow potential was associated with a similar slow current flow, while the rapid oscillations were closely locked with fast synaptic currents. Cross-correlation of whole-cell currents and the rapid field oscillation (both signals high-pass filtered above 2 Hz) showed a strong peak correlation (mean correlation coefficient of 0.4 +/- 0.1 S.D., 5 SATs per animal). Cross-correlation during the inter-SATs times showed an equally strong correlation, but spread over a wider time-base. These results together with previous finding in barrel cortex suggest that the rapid oscillations of SATs are generated by rhythmic AMPA receptor mediated synaptic currents at beta-frequency range, while the infra-slow potential is likely generated by summation of NMDA, AMPA and kainate receptor mediated currents (Minlebaev et al., 2009).

Figure 6
Synaptic correlates of SATs

Beta oscillations of SATs synchronize activity in superficial layers

The depth profile of SAT-related activity was examined with multi-site linear electrode arrays (‘Michigan probes’, 100 μm spacing; Fig. 7A). These recordings showed that MUA was elevated in all cortical layers during SATs, and this firing, as in layer 4, was organized into multiple sub-bursts (Fig. 7B). One exception to this was presumptive layer 5a (500-600 μm depth) which often contained units that exhibited tonic firing between bursts as well as between SATs. The beta band field-potential oscillations, by contrast, were localized to superficial layers (L2-4), with a peak 300 - 400 um below the pial surface (Fig. 7C - D). Close examination of the temporal relationship between these beta oscillations and MUA revealed further differences between layers. In superficial layers MUA occurred primarily during the troughs of the beta oscillation, and less frequently during bursts of activity with smaller field deflections. Unit firing in deeper layers however was not strongly coordinated by the beta oscillations, and we did not observe another pattern that organized firing in the deep layers. A depth profile of the beta oscillations was constructed by phase averaging centered on the negative peak of the layer 4 trough (Fig. 7D; 1000 events from each of 8 pups). Current source densities (CSD) calculated from these triggered averages show a prominent sink in layer 4 that ascended to superficial layers. Average spike rate (5 ms bins) in layers 2-4 was highest in the trough, and suppressed between phases. Mean spike rate in the deeper layers was much lower than in superficial layers, but showed a small increase following the layer 4 trough.

Because of differences in sampling and baseline spike rate between layers we further examined the synchronization of activity by beta-oscillations by calculating correlation coefficients for normalized spike rates (5 ms bins) between layers (Fig. 7E; 10-15 SATs from each of 8 pups; correlation coefficients were generated for each animal and the reported mean is the average of 8 pups). MUA rate changes recorded at electrodes placed in layers 2/3 and 4 were strongly correlated with each other (mean cc 0.52 +/- 0.13 S.D. for adjacent electrodes), but only weakly correlated to sites located in deep layers (mean cc 0.21 +/- 0.14 S.D.;t-test p <0.0001). Even adjacent electrodes in layers 5/6 were less strongly correlated than similarly space electrodes in the superficial layers (mean cc 0.30 +/- 0.08 S.D.; p < 0.0001). The temporal relationship between the rapid (> 2 Hz) component of the field potential and MUA in each layer were further quantified by cross-correlation analysis (Fig. 7F). As expected from the trough averaging, spike rates in superficial layers were positively correlated at 0 ms lag with spike-rate modulations on adjacent electrodes, and negatively correlated to the field potential in superficial layers. Peak correlation coefficients for layer 6 occurred with slight delay (5.4 s +/- 2.5 S.D) relative to layer 4, a relationship that was also apparent in the negative correlation to field potentials at the same delay.

In total our data show that the beta-oscillations specifically synchronize activity within a cortical column consistent with data from the somatosensory cortex (Dupont et al., 2006;Yang et al, 2009). This effect is strongest for superficial layers, and more modest in deep layers.

Beta oscillations synchronize superficial layers as a spreading wave

Phase III retinal waves co-modulate firing at locations separated by 100's of microns (Blankenship et al, 2009). We examined how this spread correlates activity patterns within and between cortical hemispheres by recording with horizontal wire electrode arrays placed in both cortices (Fig. 8A). Electrodes within the same hemisphere (500 and 1000 μm separation, n=30 SATs from each of 10 animals P10-11) were likely to record SATs with high temporal proximity. On average 59 +/- 8% (S.D.) of SAT starts on one electrode were within 2 s of the start of a SAT on electrodes on the same side, 86 +/- 5% percent occurred within 5 s. In total 79% of SATs occurring on one electrode overlapped by at least 1s SATs 500 μm distance. SATs separated by more than 10 s were very rare, consistent with the clustering of retinal waves observed in vitro by us and others (Kerschensteiner and Wong, 2008;Blankenship et al., 2009). By contrast we observed no correlation in SAT occurrence between hemispheres, consistent with the retinal generation of SATs, poor ipsilateral representation at this age (Smith and Trachtenberg, 2007) and impaired decussations of albino rats (Lund, 1965).

The horizontal synchronization of cortical activity during SATs was examined with 4 shank multi-electrode arrays placed along the rostral-caudal axis (4×4 200 μm separations; Fig. 8C). Recordings of 20 SATs from each of 4 pups each showed the same pattern: SATs often occurred simultaneously on multiple shanks, but were also observed to spread between them consistent with the spread of activity as a random wave-front. Slow spreading activity (>100 ms per 1 mm) was always observed in the horizontal (between shanks) not vertical (between layers) direction. When SATs were recorded simultaneously on multiple shanks, the beta-oscillations in layer 4 were always synchronous (Fig. 8D-E). Sites that became engaged by the spreading SAT developed beta oscillations synchronized to the other electrodes; we never observed non-synchronous oscillations during SATs in the same hemisphere. By contrast SATs in opposite hemispheres were never synchronous (not shown, n=30 SATs from each of 10 animals P10-11). The relative role of this synchrony in coordinating activity laterally and vertically was measured by computing the correlation coefficient between normalized spike rates in layer 4 and layers 5/6 during SATs. In all four pups, electrodes on adjacent shanks in layer 4 were more highly correlated than those in the deeper layers on the same or adjacent shafts (Fig. 8F). Population averages showed that for a given separation, layer 4 electrodes were more correlated to other layer 4 electrodes than to deeper electrodes (Fig. 8G). The average correlation coefficient of layer 4 electrodes separated by 200 μm was 0.68 +/- 0.04 S.D, but 0.36 +/- 0.02 (t-test, p < 0.0001) to the electrode on the same shaft 200 μm deeper. A similar relationship held for shafts separated by 400 and 600 μm. By contrast electrodes located in layers 5 or 6 had correlation coefficients of 0.38 +/- 0.06 S.D. for electrodes with 200 μm horizontal separation, and 0.39 +/- 0.02 S.D. for the same vertical distance (t-test, p=0.93).

In total the data on horizontal spread are consistent with the hypothesis that SATs are driven by retinal waves and that they synchronize synaptic and spiking activity in superficial layers via the beta oscillations.


The main findings of the present study are that: (i) spontaneous activity in the rat visual cortex during the second postnatal week is organized in regularly occurring long-lasting episodes of highly rhythmic beta-oscillations nested in a large amplitude infra-slow negative potential, that share many characteristics with the slow-activity transients (SATs) described in preterm human EEG; (ii) rodent visual cortical (rv)SATs are driven by phase III spontaneous retinal waves and patterned by thalamocortical circuit to produce beta-oscillations which synchronize activity in the superficial layers at co-active locales. Thus rvSATs appear to be the cortical response to input from the sense organ, not an internally generated spontaneous cortical activity. Finally, our data show that rvSATs are clearly separate from ongoing cortical activity, which had a separate developmental profile.

SATs in rodent, human, and ferret

Several lines of evidence indicate that rvSATs described in the present study are homologous to preterm SATs. In both species SATs are regularly occurring events of comparable duration, associated with large infra-slow potentials and rapid oscillatory activity (Tolonen et al., 2007). Human SATs are expressed in sensory cortices, predominantly in occipital areas (Vanhatalo et al., 2005). SATs in both species occur during comparable stages and share a common developmental profile, becoming longer by accumulating multiple short events before finally disappearing around the onset of vision (birth in humans and eye opening rats). They develop in counterpoint to the diversification and increasing continuity of ongoing cortical activity and the acquisition of coherent sleep states (2nd post-natal week in rats and gestational week 36-40 in humans (Blumberg et al., 2005; Jouvet-Mounier et al., 1970; Lamblin et al., 1999)). Despite the remarkable similarity, differences also exist. Firstly, SATs in human often occur synchronously in both hemispheres whereas in the rat, SATs are uncorrelated between hemispheres. The binocularity of human vision predicts that retinal activity in both eyes should drive activity in each cortex, as observed in ferrets (Chiu and Weliky, 2002). Such a mechanism can partially explain the higher SAT rate in human (~0.1 Hz) compared to rat (~0.02 Hz), though species differences in spontaneous retinal activity will contribute in currently unknown ways (Warland et al., 2006). Finally, while rvSATs are dominated by a highly regular beta oscillation, human SATs nest activity in a wider range of frequencies. This likely due to the poor transmission of the beta-oscillations to the surface (see Fig. 7C Mz), as well as the complex propagation and summation of dipoles from multiple columns to the large surface electrodes in human. Ferret mega-bursts are similar in length, sub-burst structure, and retinal dependency as rodent SATs (Chiu and Weliky, 2001); they also demonstrated evidence of 10 Hz rapid oscillations (the lower frequency consistent with their recording location in layer 5/6).

In sum, we propose that in occipital cortex SATs in rats and humans and mega-bursts in ferrets constitute a homologous class of events whose differences arise as a result of differences in binocularity, spontaneous retinal activity, as well as recording techniques. We further suggest that these activities are not functionally distinct from spindle-bursts previously described in the rat pups during the first postnatal week in V1 (Hanganu et al., 2006) and delta-brushes observed in premature human and rat somatosensory cortex Khazipov 2004 (Milh et al., 2007; Minlebaev et al., 2009), but are long duration versions of these patterns that arise because of the developmental increase in duration of the retinal waves. rvSATs, in fact, appear to be multiple spindle-bursts occurring sequentially, which has also been suggested as a mechanism for increasing SAT length in humans (Vanhatalo and Kaila, 2006). Consistent with the hypothesis that the duration and macro-patterning of SAT-like activity (infra-slow waves and alpha-beta oscillations) is determined by cortical input, long duration alpha-beta oscillations observed in rat barrel cortex (Yang et al., 2009; Fig S1), have different duration and incidence than V1 SATs, and require repetitive stimulations to evoke them, suggesting a different driver (somatic vs. retinal).

Network Mechanisms of SATs

The following network model of rvSATs best fits our data: spontaneous retinal burst-clusters provide excitatory input to the thalamocortical circuitry (Mooney et al., 1996), which then oscillates at beta frequency. Each retinal burst likely drives a component spindle-burst whose currents summate to form the infra-slow wave. If the beta-oscillations arise in thalamus, cortex or the thalamocortical loop is not clear. The CSD profile of beta-oscillations is consistent with a thalamic origin (Molnár et al., 2003), but the ability of isolated cortex to produce similar oscillations (Dupont et al., 2006) suggests that intrinsic cortical dynamics also contributes. The horizontal spread of rvSATs in visual cortex is also consistent with their generation by retinal waves. Thus, our results suggest that V1 SAT activity is directly driven by peripheral input, but it remains possible that individual spindle-bursts within the SAT are triggered by retinal bursts. During the first post-natal week brief electrical inputs have been shown to trigger short duration spindle-bursts (Hanganu et al., 2006; Minlebaev et al., 2007; Yang et al., 2009). Thus beta-oscillations may have two modes of generation: short SATs (500ms – 1s) may be triggered by peripheral input, while longer duration activity requires, and is driven by, constant input.

Slow activity transients and the development of ongoing activity

Two hypotheses have been put forward to account the development of continuous activity in human neocortex: (i) a continuous elongation of spontaneous activity transients such as SATs, or (ii) a separate development of unrelated network activity, occurring during ‘intra-SAT’ times and the independent elimination of SATs (Vanhatalo and Kaila, 2006). By showing two patterns of activity, each with its own generative mechanism and time-course, during comparable developmental stages, our data support the later hypothesis. We observed a separate pattern of short-bursts beginning P8-10 that was clearly different from rvSATs. Our data, and recent imaging studies (Rochefort et al., 2009; Golshani et al., 2009), have indicated these bursts become more frequent and quickly transform into adult-like slow waves at the end of the second post-natal week, supporting the hypothesis that SATs and on-going cortical activity are not related.

The relatively late development of slow-waves is consistent with observations showing that spontaneous synaptic network activity in somatosensory cortical slices develops late in the first post-natal week in the form of cortical giant depolarizing potentials (cGDP) (Allene et al., 2008; Rheims et al., 2008). Surprisingly, given the prevalence of such activity in slices, in vivo such activity represented a small minority (13%) of action potentials in cortex during the second post-natal week. Thus while cortex, when isolated via enuclation, slicing or anesthesia, can compensate rapidly by generating activity internally, it appears that under normal circumstances retinal waves provide a large majority of the neuronal activation in developing V1. Interestingly we observed that clear internally generated activity in cortex does not occur until after the initial refinement of, and critical period for, the establishment of gross topography (Crowley and Katz, 2000; Olavarria and Hiroi, 2003; Cang et al., 2005). This may have important consequences for plasticity, as internal cortical activity may provide activation that retains formed (even misformed) connections even when the input is removed (Crowley and Katz, 1999).

Physiological roles for SATs

The ability of alpha and beta band oscillations to synchronize columnar activity in vivo (present paper and Yang et al., 2009) and in vitro (Dupont et al., 2006; Sun and Luhmann, 2007) has clear implications for synaptic plasticity mechanisms and circuit formation during cortical development. The firing of neighboring ganglion cells is poorly correlated by retinal bursts (Butts and Rokhsar, 2001). The thalamus and superior colliculus compensate for this with unique burst-based plasticity rules (Butts et al., 2007; Shah and Crair, 2008). While such mechanisms have not been described in visual cortex, the beta-oscillations we observe provide another avenue by which the poorly timed retinal inputs can drive cortical synaptic plasticity. Indeed, the timing of presynaptic cell firing relative to the phase of induced gamma oscillations determines the sign of synaptic plasticity in V1 (Wespatat et al., 2004). Furthermore, before eye-opening the sign of white matter-->L4 synaptic plasticity is determined by the level of post-synaptic depolarization (Jiang et al., 2007). Thus, rvSATs and associated rapid oscillations may provide perfect conditions for bidirectional synaptic plasticity in the developing visual circuitry.

By showing in the present study that cortical oscillations are driven, and not simply triggered, by retinal input, we open the possibility that fine-scale information encoded in these waves can be transmitted to cortex. For example, during phase III retinal waves, nearby on and off ganglion cells become desychronized (Kerschensteiner and Wong, 2008), a condition modeling predicts is necessary for the development of orientation selectivity in cortex (Miller, 1994). Fine-scale propagation is consistent with the role these waves in the refinement of retinal inputs to thalamus (Hooks and Chen, 2006). In sum our results suggest that the developing thalamocortical circuit transmits retinal activity, but transforms it into a network oscillation that may aid in the mechanisms of synaptic plasticity. We suggest that such transformations of peripheral input constitutes a general principle of activity transmission in cortex during early development.

Supplementary Material



This work was supported by ANR and FRM grants to RK and an NEI/NIH grant (EY016966) to MTC.

References Cited

  • Adelsberger H, Garaschuk O, Konnerth A. Cortical calcium waves in resting newborn mice. Nat Neurosci. 2005;8:988–990. [PubMed]
  • Allene C, Cattani A, Ackman JB, Bonifazi P, Aniksztejn L, Ben-Ari Y, Cossart R. Sequential generation of two distinct synapse-driven network patterns in developing neocortex. J Neurosci. 2008;28:12851–63. [PubMed]
  • Ben-Ari Y, Gaiarsa JL, Tyzio R, Khazipov R. GABA: a pioneer transmitter that excites immature neurons and generates primitive oscillations. Physiol Rev. 2007;87:1215–84. [PubMed]
  • Blankenship AG, Ford KJ, Johnson J, Seal RP, Edwards RH, Copenhagen DR, Feller MB. Synaptic and extrasynaptic factors governing glutamatergic retinal waves. Neuron. 2009;62:230–241. [PMC free article] [PubMed]
  • Blumberg MS, Seelke AM, Lowen SB, Karlsson KA. Dynamics of sleep-wake cyclicity in developing rats. Proc Natl Acad Sci U S A. 2005;102:14860–4. [PubMed]
  • Butts DA, Rokhsar DS. The information content of spontaneous retinal waves. J Neurosci. 2001;21:961–973. [PubMed]
  • Butts DA, Kanold PO, Shatz CJ. A burst-based “Hebbian” learning rule at retinogeniculate synapses links retinal waves to activity-dependent refinement. PLoS Biol. 2007;5:e61. [PMC free article] [PubMed]
  • Cang J, Renteria RC, Kaneko M, Liu X, Copenhagen DR, Stryker MP. Development of precise maps in visual cortex requires patterned spontaneous activity in the retina. Neuron. 2005;48:797–809. [PMC free article] [PubMed]
  • Carmignoto G, Vicini S. Activity-dependent decrease in NMDA receptor responses during development of the visual cortex. Science. 1992;258:1007–11. [PubMed]
  • Chiu C, Weliky M. Spontaneous activity in developing ferret visual cortex in vivo. J Neurosci. 2001;21:8906–14. [PubMed]
  • Chiu C, Weliky M. Relationship of correlated spontaneous activity to functional ocular dominance columns in the developing visual cortex. Neuron. 2002;35:1123–34. [PubMed]
  • Colonnese and Constantine-Paton. Developmental period for N-methyl-D-apartate (NMDA) receptor-dependent synapse elimination correlated with visuotopic map refinement. J Comp Neurol. 2006;494:738–51. [PMC free article] [PubMed]
  • Crowley JC, Katz LC. Early development of ocular dominance columns. Science. 2000;290:1321–1324. [PubMed]
  • Crowley JC, Katz LC. Development of ocular dominance columns in the absence of retinal input. Nat Neurosci. 1999;2:1125–1130. [PubMed]
  • Dupont E, Hanganu IL, Kilb W, Hirsch S, Luhmann HJ. Rapid developmental switch in the mechanisms driving early cortical columnar networks. Nature. 2006;439:79–83. [PubMed]
  • Garaschuk O, Linn J, Eilers J, Konnerth A. Large-scale oscillatory calcium waves in the immature cortex. Nat Neurosci. 2000;3:452–459. [PubMed]
  • Golshani P, Gonçalves JT, Khoshkhoo S, Mostany R, Smirnakis S, Portera-Cailliau C. Internally mediated developmental desynchronization of neocortical network activity. J Neurosci. 2009;29:10890–10899. [PMC free article] [PubMed]
  • Hanganu IL, Ben-Ari Y, Khazipov R. Retinal waves trigger spindle bursts in the neonatal rat visual cortex. J Neurosci. 2006;26:6728–36. [PubMed]
  • Heynen AJ, Bear MF. Long-term potentiation of thalamocortical transmission in the adult visual cortex in vivo. J Neurosci. 2001;21:9801–13. [PubMed]
  • Hooks BM, Chen C. Distinct roles for spontaneous and visual activity in remodeling of the retinogeniculate synapse. Neuron. 2006;52:281–291. [PubMed]
  • Huberman AD, Feller MB, Chapman B. Mechanisms underlying development of visual maps and receptive fields. Annu Rev Neurosci. 2008;31:479–509. [PMC free article] [PubMed]
  • Jiang B, Treviño M, Kirkwood A. Sequential development of long-term potentiation and depression in different layers of the mouse visual cortex. J Neurosci. 2007;27:9648–9652. [PubMed]
  • Jouvet-Mounier D, Astic L, Lacote D. Ontogenesis of the states of sleep in rat, cat, and guinea pig during the first post-natal month. Dev Psychobiol. 1970;2:216–39. [PubMed]
  • Kerschensteiner D, Wong ROL. A precisely timed asynchronous pattern of ON and OFF retinal ganglion cell activity during propagation of retinal waves. Neuron. 2008;58:851–858. [PMC free article] [PubMed]
  • Khazipov R, Sirota A, Leinekugel X, Holmes GL, Ben-Ari Y, Buzsaki G. Early motor activity drives spindle bursts in the developing somatosensory cortex. Nature. 2004;432:758–61. [PubMed]
  • Khazipov R, Luhmann HJ. Early patterns of electrical activity in the developing cerebral cortex of humans and rodents. Trends in Neurosci. 2006;29:414–418. [PubMed]
  • Komuro H, Rakic P. Orchestration of neuronal migration by activity of ion channels, neurotransmitter receptors, and intracellular Ca2+ fluctuations. J Neurobiol. 1998;37:110–30. [PubMed]
  • Lamblin MD, Andre M, Challamel MJ, Curzi-Dascalova L, d'Allest AM, De Giovanni E, Moussalli-Salefranque F, Navelet Y, Plouin P, Radvanyi-Bouvet MF, et al. [Electroencephalography of the premature and term newborn. Maturational aspects and glossary] Neurophysiol Clin. 1999;29:123–219. [PubMed]
  • Le Bon-Jego M, Yuste R. Persistently active, pacemaker-like neurons in neocortex. Front Neurosci. 2007;1:123–129. [PMC free article] [PubMed]
  • Lund RD. Uncrossed Visual Pathways of Hooded and Albino Rats. Science. 1965;149:1506–1507. [PubMed]
  • Marcano-Reik AJ, Blumberg MS. The corpus callosum modulates spindle-burst activity within homotopic regions of somatosensory cortex in newborn rats. Eur J Neurosci. 2008:1457–66. [PMC free article] [PubMed]
  • Milh M, Kaminska A, Huon C, Lapillonne A, Ben-Ari Y, Khazipov R. Rapid cortical oscillations and early motor activity in premature human neonate. Cereb Cortex. 2007;17:1582–94. [PubMed]
  • Miller KD. A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between ON- and OFF-center inputs. J Neurosci. 1994;14:409–441. [PubMed]
  • Minlebaev M, Ben-Ari Y, Khazipov R. Network mechanisms of spindle-burst oscillations in the neonatal rat barrel cortex in vivo. J Neurophysiol. 2007;97:692–700. [PubMed]
  • Minlebaev M, Ben-Ari Y, Khazipov R. NMDA receptors pattern early activity in the developing barrel cortex in vivo. Cereb Cortex. 2009;19:688–96. [PubMed]
  • Molnár Z, Kurotani T, Higashi S, Yamamoto N, Toyama K. Development of functional thalamocortical synapses studied with current source-density analysis in whole forebrain slices in the rat. Brain Res Bull. 2003;60:355–371. [PubMed]
  • Moody WJ, Bosma MM. Ion channel development, spontaneous activity, and activity-dependent development in nerve and muscle cells. Physiol Rev. 2005;85:883–941. [PubMed]
  • Mooney R, Penn AA, Gallego R, Shatz CJ. Thalamic relay of spontaneous retinal activity prior to vision. Neuron. 1996;17:863–74. [PubMed]
  • Olavarria JF, Hiroi R. Retinal influences specify cortico-cortical maps by postnatal day six in rats and mice. J Comp Neurol. 2003;459:156–172. [PubMed]
  • Prusky GT, Harker KT, Douglas RM, Whishaw IQ. Variation in visual acuity within pigmented, and between pigmented and albino rat strains. Behav Brain Res. 2002;136:339–48. [PubMed]
  • Rheims S, Minlebaev M, Ivanov A, Represa A, Khazipov R, Holmes GL, Ben-Ari Y, Zilberter Y. Excitatory GABA in rodent developing neocortex in vitro. J Neurophysiol. 2008;100:609–19. [PubMed]
  • Rochefort NL, Garaschuk O, Milos R, Narushima M, Marandi N, Pichler B, Kovalchuk Y, Konnerth A. Sparsification of neuronal activity in the visual cortex at eye-opening. Proc Natl Acad Sci USA. 2009. [October 6, 2009]. Available at: [PubMed]
  • Shah RD, Crair MC. Retinocollicular synapse maturation and plasticity are regulated by correlated retinal waves. J Neurosci. 2008;28:292–303. [PubMed]
  • Smith SL, Trachtenberg JT. Experience-dependent binocular competition in the visual cortex begins at eye opening. Nat Neurosci. 2007;10:370–5. [PubMed]
  • Sun JJ, Luhmann HJ. Spatio-temporal dynamics of oscillatory network activity in the neonatal mouse cerebral cortex. Eur J Neurosci. 2007;26:1995–2004. [PubMed]
  • Tolonen M, Palva JM, Andersson S, Vanhatalo S. Development of the spontaneous activity transients and ongoing cortical activity in human preterm babies. Neuroscience. 2007;145:997–1006. [PubMed]
  • Vanhatalo S, Kaila K. Development of neonatal EEG activity: from phenomenology to physiology. Semin Fetal Neonatal Med. 2006;11:471–8. [PubMed]
  • Vanhatalo S, Palva JM, Andersson S, Rivera C, Voipio J, Kaila K. Slow endogenous activity transients and developmental expression of K+-Cl- cotransporter 2 in the immature human cortex. Eur J Neurosci. 2005;22:2799–804. [PubMed]
  • Warland DK, Huberman AD, Chalupa LM. Dynamics of spontaneous activity in the fetal macaque retina during development of retinogeniculate pathways. J Neurosci. 2006;26:5190–7. [PubMed]
  • Weliky M, Katz LC. Correlational structure of spontaneous neuronal activity in the developing lateral geniculate nucleus in vivo. Science. 1999;285:599–604. [PubMed]
  • Wespatat V, Tennigkeit F, Singer W. Phase sensitivity of synaptic modifications in oscillating cells of rat visual cortex. J Neurosci. 2004;24:9067–9075. [PubMed]
  • Yang J, Hanganu-Opatz IL, Sun J, Luhmann HJ. Three patterns of oscillatory activity differentially synchronize developing neocortical networks in vivo. J Neurosci. 2009;29:9011–9025. [PubMed]
  • Zhou Q, Tao HW, Poo M. Reversal and stabilization of synaptic modifications in a developing visual system. Science. 2003;300:1953–1957. [PubMed]