Physical properties of stimuli strongly influence perception such that low-intensity stimuli are detected infrequently. As intensity increases, detection probability remains low until some perceptual threshold is crossed, after which stimuli are perceived robustly. A psychometric function (Prins and Kingdom, 2018; Watson, 1979; Wichmann and Hill, 2001) mathematically describes this property of perception. Only within a narrow range around the perceptual threshold do stimuli lead to significant trial-to-trial perceptual variance. While many studies present stimuli at threshold (Herman et al., 2019; Levitt, 1971; Pins and Ffytche, 2003; Ress and Heeger, 2003), few have probed the laminar cortical microcircuit mechanisms that underlie successful or unsuccessful perception under these conditions (McCormick et al., 2020; van Vugt et al., 2018).

Prior studies have characterized how perceived stimuli trigger stronger information propagation from earlier visual areas to higher-order visual and frontal regions (Herman et al., 2019; van Vugt et al., 2018). This information propagation and sensory processing are strongly influenced by brain states such as arousal and attention (McCormick et al., 2020; Harris and Thiele, 2011). Arousal has long been recognized for its role in modulating cortical activity (Livingstone and Hubel, 1981; McCormick and Bal, 1997; Vinck et al., 2015) and affecting performance in various sensory tasks (Aston-Jones and Cohen, 2005; McGinley et al., 2015; Yerkes and Dodson, 1908). In visual area V4, a key intermediate region in the ventral visual processing stream (Goodale and Milner, 1992; Mountcastle, 1997; Roe et al., 2012), attention strongly modulates neural activity (Desimone and Duncan, 1995; McAdams and Maunsell, 1999; Moran and Desimone, 1985; Reynolds et al., 2000). Attention increases the firing rates of V4 neurons, enhances the reliability of individual neuron firing, and reduces correlated fluctuations among pairs of neurons (McAdams and Maunsell, 1999; Cohen and Maunsell, 2009; Mitchell et al., 2007; Mitchell et al., 2009). Brain state dynamics impact both cortical and subcortical structures, contributing to behavior (Ghosh and Maunsell, 2021; Zénon and Krauzlis, 2012). Fluctuations in attention are reflected in the on- and off-state dynamics of V4 ensembles, which have been shown to correlate with behavioral performance (Engel et al., 2016; van Kempen et al., 2021).

The visual cortex has a columnar architecture in which multiple cell classes (Connors and Gutnick, 1990; Markram et al., 2004; Migliore and Shepherd, 2005; Wonders and Anderson, 2006; Zeng and Sanes, 2017) across the cortical layers (Mountcastle, 1997; Douglas and Martin, 2004) form distinct sub-populations. These sub-populations have unique and stereotyped patterns of connectivity, thus forming a canonical microcircuit that orchestrates the encoding and flow of information (Douglas and Martin, 2007; Hirsch and Martinez, 2006). Moreover, these sub-populations contribute uniquely to sensory processing and are differentially modulated by brain states (Mitchell et al., 2007; Nandy et al., 2017; McCormick et al., 1985; Pettine et al., 2019). While it has been shown that attentional modulation varies across cortical layers (Nandy et al., 2017; Pettine et al., 2019; Mehta et al., 2000a; Mehta et al., 2000b; Buffalo et al., 2011; Ferro et al., 2021; Westerberg et al., 2021; Westerberg et al., 2022), the role of these sub-populations in attentive perception at threshold remains poorly understood. Moreover, the influence of physiological states, which may be responsible for different outcomes at threshold, on these sub-populations has not been studied in detail.

Here, we examine the neural mechanisms that regulate perception at threshold. We specifically focus on the columnar microcircuit mechanisms within area V4. We hypothesized that minor fluctuations in behavioral state, such as arousal and visual sensitivity, and in the activity of neural sub-populations across the layers of the visual cortex, result in different perceptual outcomes at threshold. Specifically, we hypothesized that output layer (II–III, V–VI) sub-populations, ones projecting to higher cortical areas and subcortical structures, would show evidence of improved capacity for stimulus representation during successful perception. We also hypothesized that such successful events would be accompanied by improved information propagation throughout a cortical column. We find that differences in behavioral states and lamina-specific neural states characterize correct and incorrect trials at threshold and explain perceptual variability.

To study the neural dynamics responsible for determining whether a stimulus presented at perceptual threshold is perceived, we analyzed behavioral and cortical layer-specific neural data from area V4, collected while monkeys performed a cued attention task (Nandy et al., 2017). Monkeys were trained to detect an orientation change in one of two Gabor stimuli that were presented concurrently at two spatial locations, and to report having seen the change by making an eye movement to the changed stimulus. Prior to a block of trials, monkeys were cued as to which of the two spatial locations was likely to undergo the orientation change (95% valid cue; presented at the start of each block). During a trial, ‘non-target’ stimuli at a fixed reference orientation were repeatedly presented. Non-targets were turned on for 200 ms at the two spatial locations, and then turned off for a variable interval (200–400 ms). At a random time (1–5 s, mean 3 s) a ‘target’ stimulus, differing in orientation from the non-targets, was presented at one of the locations. If the monkey reported having detected the orientation change by making an eye movement to the location of the target stimulus, it received a juice reward (Figure 1A, ‘hit’ trial). If the monkey failed to detect the orientation change and instead continued to maintain fixation on the center of the monitor, it was not rewarded (Figure 1A, ‘miss’ trial). In this study, we focused exclusively on trials in which the target stimulus was presented at the cued location (95% of trials). All figures relate exclusively to trials in which the change occurred at the cued location.

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On each trial, the magnitude of the orientation change was drawn from a distribution that spanned multiple levels of difficulty. We fit the behavioral data with a logistic function (Prins and Kingdom, 2018) and defined the threshold condition as the orientation change that was closest to the 50% threshold of the fitted psychometric function for that session (Figure 1B, Materials and methods; see Figure 1—figure supplement 2 for additional examples and Figure 1—figure supplement 3A–G for logistic fit parameters). We selected this subset of trials for further analysis, since the constant target stimuli in these trials were equally likely to be perceived or not perceived. Target presentation times were not different between hit and miss trials (Figure 1—figure supplement 3H; p=0.15, Wilcoxon rank sum test). There was a slight difference in threshold trial performance based on time in the session (Figure 1—figure supplement 3I, p<0.01, permutation test). Performance in the middle of the recording session (second and third quartiles) was higher than in the beginning and end of the session (first and fourth quartiles). Monkeys initiated a median of 905 trials per session (range: 651–1086).

While monkeys performed this task, single- and multi-unit activity and local field potentials (LFPs) were recorded in area V4 using 16-channel linear array electrodes (Plexon Inc, Figure 1—figure supplement 1A–E). The array was inserted perpendicular to the cortical surface and spanned the cortical layers. We used current source density (CSD) analysis (Mitzdorf, 1985) to estimate the boundaries between the superficial (I–III), input (IV), and deep (V–VI) cortical layers (Figure 1—figure supplement 1E and F), and assign individual neurons their layer identity (Nandy et al., 2017). Single units were classified as either broad-spiking (putative excitatory neurons) or narrow-spiking (putative inhibitory neurons) on the basis of their waveform width using previously published techniques (peak-to-trough duration; Figure 1—figure supplement 1D; see Materials and methods; Connors and Gutnick, 1990; Nandy et al., 2017; McCormick et al., 1985; Kawaguchi, 1993; Nowak et al., 2003). Eye position and pupil diameter were also recorded (ISCAN ETL-200). When analyzing pupil diameter and eye position data, we considered all trials in the threshold condition in which the change occurred at the cued location, regardless of whether the cued location was within the receptive field (RF) of the recorded neurons. For all electrophysiological analyses, we only considered trials in which the cued stimulus was within the RF of the recorded neurons, and the stimulus change occurred at the cued location.

To assess the behavioral impact of variations in arousal and retinal image stability across trials at the threshold condition, we compared pupil diameter and microsaccade incidence across trial outcomes. Larger pupil diameter is thought to be a proxy for elevated alertness and arousal (McCormick et al., 2020; Aston-Jones and Cohen, 2005; McGinley et al., 2015; Beatty and Lucero-Wagoner, 2000; Hess and Polt, 1964; Reimer et al., 2014; Tang and Higley, 2020). We found that hit trials were associated with larger pupil diameters compared to miss trials, both before and during non-target and target stimulus presentations (Figure 2A). We quantified this difference in the estimation statistics framework (Calin-Jageman and Cumming, 2019; Ho et al., 2019) by comparing effect sizes and using bootstrapping to estimate uncertainty in the differences. We found that the mean of the distribution of pupil diameters associated with hit trials is greater than that associated with misses (Figure 2B; complementary null hypothesis testing results in Supplementary file 1a). Prior work has shown that the optimal state for sensory performance occurs at intermediate levels of arousal, with states of low and hyper arousal associated with decreased performance (Aston-Jones and Cohen, 2005; McGinley et al., 2015; Yerkes and Dodson, 1908; Cools and D’Esposito, 2011; Murphy et al., 2011; Rajagovindan and Ding, 2011; Vijayraghavan et al., 2007). In both hit and miss trials, the mean pupil diameter was close to the optimal arousal state for perceptual performance (Figure 2C; McGinley et al., 2015). The average differences in pupil diameter across hit and miss trials reflect differences within the optimal state of intermediate arousal. All results were held for individual animals (Figure 2—figure supplement 2A–C). Our results thus suggest that hits are more likely to occur during periods of greater arousal.

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Microsaccades, small fixational eye movements of <1° in amplitude that occur during normal fixation, are associated with periods of decreased visual sensitivity due to unstable retinal images (Dicke et al., 2008; Zuber and Stark, 1966). Microsaccades have been linked to suppressed neural responses in visual areas during perceptual tasks, impairing fine visual discrimination and behavioral performance (Beeler, 1967; Hafed and Krauzlis, 2010). We grouped trials in the threshold condition based on whether a microsaccade occurred in a 400 ms window preceding the onset of the target stimulus. Most trials with a pre-target microsaccade were misses, whereas the majority of trials without a microsaccade in this window were hits (Figure 2D; see Figure 2—figure supplement 2D for individual animal plots). There is a strong link between microsaccade direction and attention deployment (Lowet et al., 2018; Engbert and Kliegl, 2003; Hafed and Clark, 2002; Gowen et al., 2007; Galfano et al., 2004; Pastukhov and Braun, 2010; Yu et al., 2022). Consistent with previous reports we also find that microsaccades toward the attended stimulus were overrepresented in correct trials (Figure 2—figure supplement 1A, upper left). Conversely, microsaccades toward the attended stimulus were underrepresented in incorrect trials (Figure 2—figure supplement 1A, lower left). There was a very low but statistically significant negative correlation between pupil diameter and microsaccade rate (Figure 2—figure supplement 1B, r² = 0.006, p < 0.001). Microsaccade rates and inter-microsaccade times are reported in Figure 2—figure supplement 1C and D. Overall, these results suggest that successful trials at threshold are significantly more likely to occur during a state of greater arousal and improved visual sensitivity.

Having established that hit trials are more likely to occur in states of elevated arousal and visual stability, we investigated whether hits are characterized by differential information processing in V4. We first examined the ability to discriminate target stimuli from non-target stimuli using the firing rates of single- and multi-unit V4 neurons in each of the three identified cortical layers (superficial, input, and deep). A linear decoder could better discriminate targets from non-targets in hits compared to misses (Figure 3A; see Figure 3—figure supplement 1 for individual animal plots), suggesting differences in firing rates across these trial types. This improved stimulus discriminability was consistent across all three layers (Figure 3A).

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Elevated stimulus-evoked firing rates would indicate a stronger representation of the stimulus that could cause this improved discriminability in hits. We compared the firing rates of all neurons (single- and multi-units) recorded in each cortical layer across hit and miss trials. For non-target stimuli, firing rates were equivalent for hits and misses in both the pre-stimulus (0–200 ms before stimulus onset) and stimulus-evoked (60–260 ms following stimulus onset) periods (Figure 3B; see Figure 3—figure supplement 2 for individual animal plots). For the target stimulus, firing rates were once again equivalent in the pre-stimulus period, but hit trials were characterized by elevated firing across cortical layers in the stimulus-evoked period (Figure 3C and D). Broad- and narrow-spiking neurons in both the input and deep layers respond more to target stimuli in hit trials, and trend toward elevated firing rates in the superficial layers during hits (Figure 3E). The average firing rate in response to target stimuli for each neuron is shown in Figure 3—figure supplement 3A for both hit and miss trials. It is important to note that the stimuli presented to the animals were identical for both hits and misses. Moreover, the responses to the target stimuli occur early, and elevated firing in hits emerges at the time of expected V4 response latencies (70–100 ms; Figure 3—figure supplement 3C), and thus cannot be attributed to elevated levels of firing due to subsequent saccade planning in these trials (Figure 3—figure supplement 3B; expected >200 ms; Steinmetz and Moore, 2014).

Variability in response reflects how reliably information is encoded by a neural population. Lower baseline variability can enhance the ability of neurons to encode stimulus differences. We calculated the Fano factor, a mean-normalized measure of trial-to-trial variability in firing, for single units in our population (Figure 4A; see Figure 4—figure supplement 1 for individual animal plots). We find that broad-spiking units in the superficial layer exhibited lower Fano factor during the pre-stimulus period in hit trials (0–60 ms before non-target stimulus onset, Figure 4B), indicating this population of neurons fired more reliably when the animal correctly detected the orientation change. This was not the case for broad-spiking neurons in other layers (Figure 4B) or narrow-spiking neurons (Figure 4—figure supplement 2).

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We next wanted to test how the relationship between spiking activity and LFPs may differ across hits and misses. Spike-LFP synchrony can reflect cortical processing and both within- and inter-areal coordination (Fries, 2009; Fries et al., 2008; Siapas et al., 2005). We calculated the PPC (Vinck et al., 2010), a frequency-resolved measure of spike-LFP phase-locking, for single and multi-units relative to their local LFP signal during the pre-stimulus period (0–200 ms before non-target stimulus onset, Figure 5A; see Figure 5—figure supplement 2 for individual animal plots). We averaged PPC values at low (3–12 Hz), medium (15–25 Hz), and high (30–80 Hz) frequency bands (superficial and input: Figure 5—figure supplement 1A and B; deep: Figure 5B, Figure 5—figure supplement 1). Deep-layer neurons exhibit reduced low-frequency phase-locking in hit trials than in misses (Figure 5B). This desynchronization during hits is similar to prior reports of desynchronization due to the deployment of attention (Mitchell et al., 2009; Nandy et al., 2017).

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Our results at the individual neuron or neural-sub-population levels suggest enhanced processing of perceived stimuli. However, it is the concerted activity among neural sub-populations that ultimately determine information flow through the laminar cortical circuit. We turned to canonical correlation analysis (CCA) to investigate the strength of feed-forward communication across layers (Mitra, 2007). CCA has previously been used to describe interactions among multiple cortical areas (Semedo et al., 2022). We performed CCA on each pair of layers: input to superficial, input to deep, and superficial to deep, where the two elements in each pair correspond to the upstream and downstream layers respectively (Figure 6A). We refer to the results of CCA as population correlations. Interlaminar feed-forward population correlations were higher in hits than in misses in both the pre-stimulus and stimulus-evoked periods (Figure 6B and C). This suggests that feed-forward information flow through the column is more effective in hits than in misses.

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To further investigate interlaminar communication, we analyzed interlaminar synchrony as signatures of differential information flow between hit and miss trials. Spike-spike coherence (SSC) is a frequency-resolved measure of the degree to which two spike trains fluctuate together (Mitchell et al., 2009; Mitra and Pesaran, 1999). We measured interlaminar SSC for spike trains from pairs of cortical layers, each spike train being comprised of all recorded action potentials in a given layer (see Materials and methods). We computed interlaminar SSC separately for hit and mis trials in both the pre-stimulus (0–200 ms before non-target stimulus onset, Figure 7A) and non-target stimulus-evoked (60–260 ms after non-target stimulus onset, Figure 7C) periods, matching the firing rates across hit and miss trials separately for the pre-stimulus and non-target stimulus-evoked conditions (see Figure 7—figure supplement 1 for individual animal plots). We averaged SSC for each pair of layers across three frequency bands, 3–12 Hz, 15–25 Hz, and 30–80 Hz (Figure 7B and D).

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Overall, hit trials have greater interlaminar SSC compared to misses at almost all frequencies (Figure 7B and D). In the pre-stimulus period, the strongest SSC difference between hits and misses was observed between the superficial and deep layers across all frequencies (Figure 7B, middle panel). This implies greater synchrony of the output layers of the cortex during hit trials. In contrast, this pattern was directionally the same during the non-target stimulus-evoked period, but stronger in the other layer pairs, with greater SSC differences being found in pairs that involve the input layer (Figure 7D, top and bottom). This may reflect a higher degree of stimulus-driven feed-forward information propagation during hit trials. When comparing across time (pre-stimulus vs non-target stimulus-evoked), layers, and frequency band, there was a significant interaction effect of layer pair and time window (three-way ANOVA, p = 0.0075).

Finally, we sought to compare the predictive power of our results on the monkey’s perceptual performance. We created a generalized linear model (GLM) to regress behavioral outcome from the pupil diameter, number of microsaccades in the pre-target window, and average target-evoked multi-unit firing rate in each of the three layers (see Materials and methods; Davis et al., 2020). Other reported measures (Fano factor, PPC, interlaminar population correlations, and SSC) that we could not estimate reliably on a single-trial basis were not considered in the GLM analysis. Pre-target microsaccades were by far the strongest predictor of performance (${\displaystyle weight=-1.3116;p=6.0757e-08}$). Input layer firing rate also significantly predicted perception (${\displaystyle weight=0.3276;p=0.020068}$). Superficial firing rate, deep firing rate, and pupil diameter were not significant predictors (Supplementary file 1b, all p > 0.5). This indicates that, among the variables that we could estimate reliably on a single-trial basis, stable retinal images in the pre-target window are critical for behavioral performance, and elevated firing in the input layer is the most reliable physiological signature of a perceived stimulus. GLM fit parameters can be found in Supplementary file 1c.

We investigated the physiological processes responsible for variable behavioral outcomes at perceptual threshold. Controlling for both the attentive instruction (thus minimizing large-scale attentional effects) and the stimulus condition that elicited performance at a threshold level allowed us to examine the physiological and neural correlates that underlie correct vs incorrect behavioral outcomes. While this study cannot disentangle the independent roles of behavioral state fluctuations and neural fluctuations in determining behavioral outcomes, evidence suggests that differences in both are associated with hits. We found multiple lines of evidence which suggest that a state of higher arousal and eye position stability and the accompanying enhanced processing of visual stimuli contributes to accurate perception in hit trials (Figure 8).

Figure 8

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Pupil diameter is elevated in hit trials (Figure 2A–C; Figure 8A), and prior studies have shown that pupil diameter is strongly linked to arousal and alertness (Beatty and Lucero-Wagoner, 2000; Hess and Polt, 1964; Tang and Higley, 2020). This provides evidence that a state of higher arousal may contribute to improved sensory processing. The much lower hit rate in trials with a microsaccade preceding the target (Figure 2D; Figure 8A) and our GLM analysis show that stability of retinal images is critical for accurate discrimination at threshold. It is unlikely that these two measures are reflecting the same phenomenon, as there is a very weak correlation between them over the course of a trial (Figure 2—figure supplement 1B).

There is a strong link between oculomotor control and attentional deployment (Moore and Zirnsak, 2017; Schafer and Moore, 2011; Moore and Fallah, 2001). In this study, hits and misses differ in their behavioral responses, with hit trials being characterized by a saccade to the target stimulus. Almost all of our neural results reflect differences around the time of non-target stimulus presentations during which the monkeys maintained fixation at the center of the screen and, therefore, were hundreds of milliseconds prior to saccade planning and execution in the case of hit trials. Trials in which saccades were made to non-target stimuli were excluded from analysis, as were trials in which the monkey made a saccade to the target too soon after its presentation to have been a behavioral response to stimulus perception (see Materials and methods). The analysis of microsaccade occurrence focused on the window just before target stimulus presentation and before monkeys could begin oculomotor planning. Only the analysis of neural responses to target stimuli appears in conjunction with divergent oculomotor behavior between the hit (saccade) and miss (no saccade) trials. However, here too firing rates diverge much earlier, particularly in the input layer, than would be consistent with the effects of saccade planning (Figure 3—figure supplement 1B; Steinmetz and Moore, 2014).

Non-target stimulus contrasts were slightly different between hits and misses (mean: 33.1% in hits, 34.0% in misses, permutation test, ${\displaystyle p=0.02}$), but the contrast of the target was higher in hits compared to misses (mean: 38.7% in hits, 27.7% in misses, permutation test, ${\displaystyle p=1.6e-31}$). To control for potential effects of stimulus contrast, firing rates were first normalized by contrast before performing the analyses reported in Figure 3. For all other results, we considered only non-target stimuli, which had very minor differences in contrast (<1%) across hits and misses. In fact, this minor difference was in the opposite direction of our results with mean contrast being slightly higher for misses. While we cannot completely rule out any other effects of stimulus contrast, the normalization in Figure 3 and minor differences for non-target stimuli should minimize them.

A body of evidence (see Martinez-Conde et al., 2013, for review) suggests that microsaccades directed toward a target stimulus reflect attention-related processing and performance (Lowet et al., 2018; Engbert and Kliegl, 2003; Hafed and Clark, 2002; Gowen et al., 2007; Galfano et al., 2004; Pastukhov and Braun, 2010; Yu et al., 2022). In our dataset, during the pre-target period, microsaccades toward the attended stimulus were overrepresented in correct trials (Figure 2—figure supplement 1A, upper left). Conversely, microsaccades toward the attended stimulus were underrepresented in incorrect trials (Figure 2—figure supplement 1A, lower left). Microsaccades directed toward the location of the eventual target may reflect elevated attentional deployment that can compensate for reduced sensitivity due to a higher incidence of microsaccades.

Our electrophysiological findings and their laminar patterns associated with hit trials within a cued attention state mirror several previous findings that are associated with the deployment of covert spatial attention. Attention has long been known to increase firing rates in V4 (McAdams and Maunsell, 1999; Mitchell et al., 2007; Spitzer et al., 1988), and there is evidence that this increase occurs in all cortical layers in V4 (Nandy et al., 2017). We find improved target vs non-target discriminability in hits (Figure 3A) across all cortical layers. Additionally, elevated target-evoked firing rates in hits occur across all layers in conjunction with elevated arousal (Figure 3B–D; Figure 8C). Attention reduces the variability in the firing of V4 neurons, and this reduction is thought to contribute to the improved information coding capacity of a population of neurons (Cohen and Maunsell, 2009; Mitchell et al., 2007; Mitchell et al., 2009; Nandy et al., 2017; Moreno-Bote et al., 2014). The reduction in Fano factor among broad-spiking superficial-layer neurons in hit trials mirrors the effects of attention (Figure 4). Multiple lines of evidence suggest broad- and narrow-spiking correspond to putative excitatory and inhibitory neurons respectively. Narrow-spiking neurons exhibit higher firing rates, which corresponds well with inhibitory interneuron (Connors and Gutnick, 1990; Nandy et al., 2017; McCormick et al., 1985; Contreras and Palmer, 2003; Foehring et al., 1991; Povysheva et al., 2006). Repolarization times in broad-spiking neurons are also longer, as they are in excitatory pyramidal neurons (McCormick et al., 1985; Nowak et al., 2003; Hasenstaub et al., 2005). Since these neurons are putative projection neurons to downstream cortical areas, this reduction in Fano factor may indicate increased reliability in stimulus encoding that could contribute to hits. Our finding is also in agreement with previous reports of higher variability in representations of unperceived stimuli in humans (Schurger et al., 2010). Synchronous neural activity appears to modulate perceptual and cognitive ability in a variety of contexts (Abbas et al., 2018; Fries et al., 2001; Rohenkohl et al., 2018; Worden et al., 2000). We found that deep-layer neurons exhibit less low-frequency phase-locking in hit trials (Figure 5). This is consistent with prior studies that find an attention-mediated reduction in the power spectrum of the spike-triggered-averaged LFP (Fries et al., 2001).

In examining interlaminar population synchrony, we found that hit trials were characterized by stronger feed-forward interactions across the cortical column (Figure 6). This state of improved interlaminar information flow could be a result of neuromodulatory or top-down processes that maintain the cortex in a state of sustained depolarization corresponding to a state of higher arousal during hits (McCormick et al., 2020; McGinley et al., 2015). Our examination of interlaminar synchrony revealed two interesting and complementary patterns: hits were associated with greater coherence between the superficial and deep layers during spontaneous activity in the pre-stimulus period (Figure 7A and B; Figure 8B); in contrast, we found enhanced coherence between the input layer and both the output layers (superficial and deep) in the stimulus-evoked period during hits (Figure 7C and D; Figure 8C). Increased superficial-deep coherence in the pre-stimulus period could be the result of the same neuromodulatory or top-down processes. Increased synchrony between the input layer and the output layers during the stimulus-evoked period provides further evidence of stronger information propagation through the cortical circuit, and hence with improved stimulus detection (Marshel et al., 2019). In contrast to broad global synchrony or local correlated fluctuations, which may signal a default state of minimal processing or decreased information coding capacity (Mitchell et al., 2009; Steriade et al., 1993; Krosigk von et al., 1993; Zohary et al., 1994), these patterns of interlaminar coherence that we found suggest that successful perception at threshold is mediated by pathway-specific modulation of information flow through the laminar cortical circuit.

Prior studies showing decreased correlations under attention typically do not contain laminar information (Cohen and Maunsell, 2009; Mitchell et al., 2009) or only consider decreased correlations within a layer (Nandy et al., 2017). In contrast, the correlation and synchrony analyses presented here are interlaminar, which we expect could reflect improved information processing in a column, similar to principles of communication across areas (Semedo et al., 2022).

Taken together, our results provide insight about how information about a threshold stimulus may successfully propagate through a cortical column and influence sensory perception. Lower baseline variability among broad-spiking superficial layer neurons and decreased low-frequency synchronous activity in the deep layers could be indicative of improved capacity to encode sensory information. Higher target-evoked firing rates and elevated interlaminar synchrony could enhance the propagation of this encoded signal. These results associate pre-stimulus baseline state differences with enhanced cortical processing in the stimulus-evoked period.

Several studies have examined how information flow differs for perceived and unperceived stimuli at a more macroscopic scale (Herman et al., 2019; van Vugt et al., 2018). van Vugt et al., 2018, recorded from three brain regions, V1, V4, and dorsolateral prefrontal cortex, while a monkey performed a stimulus detection task at threshold. Their work supports the model that feed-forward propagation of sensory information from the visual cortex to the PFC causes a non-linear ‘ignition’ of association areas resulting in conscious perception (Dehaene and Changeux, 2011). Herman et al., 2019, found that conscious human perception triggers a wave of activity propagation from occipital to frontal cortex while switching off default mode and other networks. Our study provides insight into the functions of the cortical microcircuit at the columnar level that could reflect these large-scale sweeping activity changes in perception.

Overall, we identified substantial layer-specific differences in cortical activity between hits and misses at perceptual threshold, leading to the following conceptual model (Figure 8). During spontaneous activity, the state of elevated arousal and eye position stability during hits (Figure 8A) is manifested by increased interlaminar synchrony between the superficial and deep layers (Figure 8B, thicker orange arrows), which we propose is due to top-down influences. We predict that decreased firing variability in broad-spiking neurons in the superficial layer is caused by a lower variability in membrane potential closer to the action potential threshold among these neurons (Figure 8B, inset). Elevated feed-forward propagation in the stimulus-evoked period (Figure 8C) and a membrane potential closer to action potential threshold could both contribute to higher firing rates in the output layers of the cortex (Figure 8C, inset), and are indicative of greater fidelity of stimulus processing in hits. These physiological differences in the laminar microcircuit likely contribute to successful perceptual discrimination at threshold.

This study used data from a previously published study: Nandy et al., 2017. Data used in this study has been deposited in Zenodo: https://zenodo.org/records/13968327.

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Author details

1. Mitchell P Morton

   1. Department of Neuroscience, Yale University, New Haven, United States
   2. Interdepartmental Neuroscience Program, Yale University, New Haven, United States

   Contribution

   Formal analysis, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9185-1633

2. Sachira Denagamage

   1. Department of Neuroscience, Yale University, New Haven, United States
   2. Interdepartmental Neuroscience Program, Yale University, New Haven, United States

   Contribution

   Software

   Competing interests

   No competing interests declared

3. Isabel J Blume

   Department of Neuroscience, Yale University, New Haven, United States

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

4. John H Reynolds

   Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6988-4607

5. Monika P Jadi

   1. Department of Neuroscience, Yale University, New Haven, United States
   2. Interdepartmental Neuroscience Program, Yale University, New Haven, United States
   3. Department of Psychiatry, Yale University, New Haven, United States
   4. Wu Tsai Institute, Yale University, New Haven, United States

   Contribution

   Conceptualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1092-5026

6. Anirvan S Nandy

   1. Department of Neuroscience, Yale University, New Haven, United States
   2. Interdepartmental Neuroscience Program, Yale University, New Haven, United States
   3. Wu Tsai Institute, Yale University, New Haven, United States
   4. Department of Psychology, Yale University, New Haven, United States
   5. Kavli Institute for Neuroscience, Yale University, New Haven, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing - review and editing

   For correspondence

   anirvan.nandy@yale.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4225-5349

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