In the first set of experiments, we combined large-scale extracellular recordings using silicon multi-site electrodes (‘silicon probes’) (Csicsvari et al., 2003
) with simultaneous juxtacellular recording (Pinault, 1996
) in urethane-anesthetized rats. We analyzed responses to pure tones of varying frequency and intensity and 1s-long ‘click-trains’ of varying click frequency (Kilgard and Merzenich, 1999
;Wang et al., 2008
), and spontaneous activity during periods without sound presentation.
Morphologically identified PCs recorded with juxtacellular electrodes (“juxtacells”) were grouped based on somatic location: L2/3 PCs (N=10); L4 PCs (N=10; consistent with a previous report in auditory cortex (Smith and Populin, 2001
), PCs were the dominant cell type in L4); L5 PCs (N=28); and L6 PCs (N=6). L5 PCs were further divided into two classes based on apical dendrite diameter: L5 thick PCs (L5 tPCs; apical dendrite diameter>2.5 μm; N=9) and L5 slender PCs (L5 sPCs; apical dendrite diameter<2.5 μm; N=19). Single-units recorded extracellularly with silicon probes (“extracells”) were divided into four groups based on spike waveform and estimated somatic location: out of 1379 extracells, 97 were putatively classified as superficial PCs, 655 as deep PCs, 13 as superficial INs, and 100 as deep INs (see Experimental Procedures and Figures S1-3
for further details; note that to reduce the risk of misclassification, only a subset of extracells were assigned to groups).
Cell-type dependent sparseness of auditory-evoked activity
To investigate whether coding sparseness differs between cell classes, we first characterized the auditory tuning of individual neurons. illustrate the tuning of five juxtacells with responses typical of their class (see also Figure S4
). In general, L2/3 PCs exhibited highly selective responses in both the spectral and temporal domains, while L5 tPCs were broadly tuned to both stimuli. L4 PCs and L5 sPCs were intermediate between two classes, with L5 sPCs showing heterogeneous response profiles. Intriguingly, L6 PCs showed strikingly different response profiles than other classes, typically without clear frequency-tuned responses (5/6 cells), but sometimes responding to tones and clicks after a ~200ms delay (2/6 cells; ). While our juxtacellular recordings did not yield a large enough number of morphologically identified interneurons to perform statistical analyses (N=4), putative INs could be identified in large-scale extracellular recordings by spike waveform (Figure S3
). shows the spectral tuning of illustrative putative PCs and INs of superficial and deep layers (see also Figure S5
). The firing rates and tuning sharpness of putative PCs did not differ significantly from those of morphologically identified PCs of the corresponding layers (Figure S6
); the tuning of putative INs, however, differed from that of PCs, with superficial putative INs showing broader tuning, more similar to deep PCs than to superficial PCs.
Tuning profiles of example neurons
The above examples thus suggest that coding sparseness differs between cortical cell classes. We next set out to quantify this impression. Several measures of coding sparseness have been described (Olshausen and Field, 2004
;Willmore and Tolhurst, 2001
). Because we will later compare the sparseness of evoked responses and spontaneous events, for which standard measures are not applicable, we used a “response probability” measure, defined as the probability that a neuron would fire at least one spike in response to any given stimulus presentation (). For juxtacells, this analysis supported the visual impression conveyed by the example neurons, with L2/3 and L6 PCs showing sparsest activity (i.e. lowest response probabilities), and L5 tPCs the densest activity (i.e. highest response probabilities). For extracells, consistent results were observed: response probability of putative superficial PCs closely matched that of morphologically identified L2/3 PCs, and deep PCs showed response probability intermediate between identified L5 sPCs and tPCs, consistent with the extracellularly recorded population being primarily a mixture of these classes. Putative INs of both layers showed response probability similar to that of deep PCs rather than superficial PCs (). The dependence of response probability on cell class was similar for both tone and click train stimuli (; see Experimental Procedures
). Analysis of covariance (ANCOVA) revealed that this did not simply reflect a common effect of cell class (p
<0.0001), suggesting sparseness was correlated between stimulus types even within neurons of a single morphological class. Similar results were obtained using several other sparseness measures (Figure S7
Sparseness of sensory responses varies between cell classes
Sparseness measures do not fully summarize the character of a neuron s sensory tuning; for example, while the measured sparseness of L2/3 and L6 PCs was not significantly different, visual examination of their spectral tuning suggested that L6 PCs (but not L2/3 PCs) carry little information about stimuli during onset periods. To quantify this, we adopted an information-theoretic approach, to estimate how well we could predict the cells’ response (i.e., spike count) from the presented tones on single trial basis (; see Experimental Procedures). The results of this analysis were again consistent with the examples of : predictability measured in bits per spike was highest in L2/3 PCs (), consistent with sharpest tuning in these neurons; L6 PCs typically carried little information about tone identity (). Thus, we found clear laminar differences in auditory responses, with sparse and more informative activity in L2/3 PCs, and denser activity in larger L5 PCs and putative INs.
Cell-type dependent sparseness of spontaneous activity
We next asked whether the patterns of sparseness described above also apply to spontaneous activity patterns. We began by examining patterns of multi-unit activity (MUA), recorded with linear multisite electrodes (). As previously described (Luczak et al., 2009
), spontaneous activity within presumptive L5 consisted of an alternation between periods of network silence (“downstates”) and generalized spiking activity (“upstates,” see Figure S8
for further information; note that what we refer to as “upstates” are likely to include the “bumps” described by DeWeese and Zador (2004
)). Some, but not all upstates visible in deep layers were accompanied by activity in the immediately overlying superficial layers. shows a histogram of normalized MUA rate in both layers during the first 50ms of all detected upstates. For deep layer activity, the histogram had a broad distribution; for superficial layer activity, however, the histogram showed a major peak at 0, confirming that many upstates did not cause measurable superficial spiking activity (see also Figure S9
). To investigate the participation of different identified cell classes in upstates, we again used a response probability measure, here defined as the probability a cell would fire at least one spike in any given upstate. A similar pattern of sparseness was seen as for auditory responses, with L2/3 and L6 PCs showing the lowest response probability, and L5 tPCs the highest (). Response probability was correlated on a cell-to-cell basis between spontaneous and auditory-evoked activity (). Again, this did not simply reflect the common effect of cell class on response probability (ANCOVA, p
<0.05 for tones and upstates; p
<0.05 for click trains and upstates), suggesting that consistent variations in sparseness both between and within cell classes were preserved in spontaneous and evoked activity.
Sparseness varies between cell classes during spontaneous activity
Difference in propagation of activity across cortical layers
The above analyses showed that the pattern of sparseness across cortical cell classes was similar between evoked and spontaneous activity. However, we observed a clear difference between these two types of events in the propagation of activity between layers. shows examples of laminar MUA traces during successive evoked and spontaneous spiking events. At the onset of auditory responses, activity originated in the upper middle and a part of deep layers, locations which also corresponded to the locations of early sinks revealed by current source density (CSD) analysis (Figure S10
). Activity at upstate onset, however, was first seen in the deep layers and spread upward (). To quantify the difference between these patterns, we computed a “peak latency” measure, defined as median MUA spike time in a 50ms window after event onset, as a function of putative laminar location (; for different measures of latency see Figure S11
). This analysis confirmed a significant difference in laminar temporal profile between the onset of upstates and auditory-evoked responses (). The observed laminar profile of auditory responses was similar across responses to clicks and multiple tone frequencies and intensities that evoked spiking responses (Figure S12
), as well as between experiments (Figure S10
Spread of activity across layers differs between sensory responses and upstates
Spatial- and laminar-dependence of correlated activity
The results described above focused on the properties of individual neurons, and the coordination of neurons within a single cortical column, but not the organization of activity across multiple columns. For example, sparse firing of superficial PCs could reflect either spatially localized or distributed activity (). Because primary auditory cortex is tonotopically organized (Schreiner et al., 2000
), we expected that for tone responses, activity should be spatially localized; however, the spatial structure of click responses and upstates, and the organization of trial-to-trial response variations was not obvious a priori
Laminar-dependent structure of correlated activity
To address this issue, we used spike-sorted extracellular population activity recorded from multi-shank silicon probes (). Visual examination of rasters suggested spiking activity in superficial layers to be locally clustered compared with deep layers, for upstates as well as responses to tones and clicks (). To quantify this impression, we performed a correlation analysis on spike counts in the 50ms period following event onsets (). For upstates, correlations between pairs of superficial cells were much stronger for local pairs recorded from the same shank (estimated spacing <~50μm)(Henze et al., 2000
) than for distal pairs from separate shanks (estimated spacing >~ 200μm); in deep layers, however, there was only a subtle difference between local and distal correlations. For sensory coding, two types of correlations are typically distinguished: similarity of average tuning curves (“signal correlations”), and correlated variability between response to repetitions of a particular stimulus (“noise correlations”) (Averbeck et al., 2006
;Gawne and Richmond, 1993
). For all correlation types, a qualitatively similar pattern was observed as for upstates: in superficial layers, correlations were stronger for local than distal pairs, while in deep layers, there were only subtle differences between local and distal correlations ().
Spatiotemporal dispersion of evoked and spontaneous activity
The spatial dependence of cell-to-cell correlations was therefore consistent with sparse, spatially localized activity in L2/3, occurring on top of broader activity in L5. To confirm this possibility more directly, we employed a different experimental approach (), in which multisite electrodes were inserted parallel to the layers of auditory cortex. show examples of MUA traces recorded with this approach, for upstates and evoked responses, respectively. There was considerable variability between events in the set of sites at which activity was induced, particularly in the superficial layers. Consistent with the correlation analyses described above, activity was more spatially localized in superficial than in deep layers, which was statistically confirmed by a measure of spatial dispersion (). In addition, the speed with which activity spread across recording sites was on average faster for evoked events than for upstates, consistent with the tendency for the latter to sometimes propagate as traveling waves (Luczak et al., 2007
;Petersen et al., 2003
) ( and S13
Spatiotemporal dispersion of population activity
Sparseness and propagation of population activity in unanesthetized animals
To determine how well the above results, collected under urethane anesthesia, generalized to unanesthetized animals, we performed further recordings using silicon probes in head-restrained, unanesthetized rats (N=7; see Experimental Procedures
). Out of 235 spike-sorted single units, we identified 48 putative superficial PCs, 121 putative deep PCs, 4 putative superficial INs, and 14 putative deep INs. Because the number of recorded superficial INs was too small for statistical analysis, we pooled these two IN populations for further analysis. Consistent with previous results (Luczak et al., 2007
;Petersen et al., 2003
;Poulet and Petersen, 2008
), we observed coordinated spontaneous fluctuations of population activity in this data (). Also consistent with a recent study (Greenberg et al., 2008
), we noticed several quantitative differences in neuronal activity between unanesthetized and urethane-anesthetized conditions including higher mean firing rates and weaker spontaneous correlations in superficial and deep layers (Figure S14
Cell-type dependent sparseness of spontaneous and evoked activity in unanesthetized animals
To gauge whether our main observations under anesthesia generalized to this dataset, we repeated the analyses described above. First, we investigated the cell-type dependent sparseness of both auditory-evoked and spontaneous activity (). As under anesthesia, visual examination of spectral tuning suggested sharper tuning of superficial PCs than deep PCs (), which was statistically confirmed using the response probability measure (). Putative INs showed higher response probability than both classes of putative PCs. Similar results were found for click-evoked responses and spontaneous events, and also confirmed using other measures of sparseness (Figure S15
). Moreover, response probability was correlated on a cell-to-cell basis between events (; ANCOVA, p
< 0.01 in all cases; c.f. and ). Thus, although firing rates were higher for all cell classes in the unanesthetized data, the relative pattern of sparseness between cell classes was similar in both cases.
Second, examination of the laminar profile of evoked and spontaneous activity onset across layers suggested a pattern similar to that found under anesthesia, with spontaneous events spreading upward from deep layers, and auditory evoked activity originating in the upper middle and a part of deep layers (). This impression was statistically confirmed using the “peak latency” measure (; c.f. ); also similarly to the anesthetized case, the laminar locations of earliest stimulus-evoked spiking corresponded to early current sinks as revealed by CSD analysis (Figure S16
Spatiotemporal structure of evoked and spontaneous activity in unanesthetized animals
Finally, analysis of data collected with electrodes parallel to the cortical laminae (N=2) confirmed that the spatial dispersion of population activity in superficial layers was narrower compared to deep layers (; c.f. ). The spread of activity across cortical columns was also faster for evoked activity (120.5 ± 5.1 mm/sec in superficial layers; 100.3 ± 4.9 mm/sec in deep layers) than spontaneous events in the corresponding layers (54.0 ± 9.5 mm/sec in superficial layers; 65.5 ± 5.1 mm/sec in deep layers) (ANOVA and post-hoc lsd test, p<0.01). Thus, while we found quantitative differences between anesthetized and unanesthetized conditions, our experiments suggested the general laminar structure of evoked and spontaneous activity was similar in both cases.