A topographical organization in the primary olfactory cortex

Domain: biology
Published: 2026-03-15
Editorial outcome: major_revision

Abstract
A key principle of most sensory cortices is their topographic organization, in which neurons in proximity exhibit similar tuning properties, typically varying along a specific stimulus feature. However, such an organization has not yet been identified in the olfactory system. Here, we developed a method that reveals the set of glomerular inputs to multiple Piriform Cortex neurons. We found that, on average, each Piriform neuron receives input from ~60 glomeruli. Putative interneurons tend to receive input from more glomeruli. Most neurons were activated when multiple distinct subsets of 2-4 glomeruli were active. Notably, the input glomeruli of nearby Piriform neurons were positioned close to each other, sharing a few glomeruli. The similarity of glomerular input maps decreased with increased distance between Piriform neurons. Furthermore, this similarity principle aligned with an increase in odor-tuning similarity of nearby neurons in both awake and anesthetized mice. These results indicate that the primary olfactory cortex adheres to an organizational principle based on how it processes glomerular inputs.

Introduction
Topographical mapping is a fundamental organizational principle of the mammalian sensory cortices. For example, in vision, adjacent locations in space are mapped to adjacent locations in the primary visual cortex in a retinotopic fashion. In contrast, olfaction may involve a weak chemotopic map in the olfactory bulb 1–3, but there is no evidence for retaining such mapping in downstream olfactory regions. In fact, a preponderance of anatomical and physiological evidence argues against a spatial organization in the olfactory cortex4–13 . However, organization in the olfactory cortex may be based on a principle that cannot be captured with current anatomical tools or odor stimulations. Several recent studies reported organization in the projections to the Piriform cortex (PC) 14–18, strongly suggesting that location within PC matters and that there is some functional organization within PC. This functional organizational principle has not yet been found.

Here, we revisited the question of structural organization in the olfactory cortex circuit. We hypothesized that the organizational principle is not based on physicochemical properties but on how PC neurons integrate inputs from the olfactory bulb (OB). Using optogenetics, we examined the computations of PC neurons in response to their OB inputs. We developed a technique and methodology to uncover the receptive fields (RFs) of multiple recorded olfactory cortical neurons by light-stimulating multiple glomeruli in mice expressing Channelrhodopsin in their Mitral/Tufted (MT) cells 14. The RF of a sensory neuron is the region of the sensory space (e.g., the body surface or the retina) in which a stimulus alters the neuron’s activity. We extended the concept of RF to functional connectivity between neurons: if many neurons form synapses with a single downstream cell, they collectively form that cell's RF. Once the RF of a neuron is revealed, it can be used to estimate the strength and sign of the neuron's functional connections with each of its inputs and to predict the neuron's response to novel stimuli 15. Applying this method to anterior PC (aPC) neurons, here we found that aPC neurons receive input from several dozen glomeruli and respond when several distinct subsets of these glomeruli are active. Narrow-fast-spiking neurons receive input from a larger number of glomeruli than non-narrow-fast-spiking neurons. Strikingly, we found that aPC is topographically organized: Spatially close aPC neurons tend to have more similar RFs and to share more glomeruli than expected by chance. We further show that this increased similarity in RF maps is consistent with the increased similarity of odor tuning curves of nearby aPC neurons in both awake and anesthetized mice.

Results
A method for revealing the receptive field of olfactory cortex neurons
Under the assumptions of the linear-non-linear Poisson model, the RF of a neuron can be estimated using the Spike-Triggered-Average (STA) technique 16. In this method, a large number of stimuli that span the stimulus space is applied. Computing the weighted sum of stimuli provides a characterization of the RF of all the simultaneously recorded neurons (Fig. 1a and Methods). The outcome of this process is a 2-dimensional map in which the value of each pixel is proportional to the synaptic strength between the neurons below that pixel and the recorded target neuron. Since odor stimuli cannot be delivered systematically to cover the odor space or in their thousands, we used optogenetics. Optogenetics converts the OB into a retina-like system, thereby circumventing this obstacle. We delivered thousands of random light patterns while recording single and multi-neuron spiking activity in anesthetized mice (Methods). This technique differs from previously used sequential or laser-scanning techniques 14,17–19 because it reveals all possible patterns that activate the neuron; it provides a model that can predict the neuron's response to novel stimuli and allows comparison of the RFs of all simultaneously recorded neurons.

To tune the technique parameters, we first applied it to MT neurons in anesthetized mice, where we expected to find one excitatory glomerulus in the vicinity of the electrode and several inhibitory glomeruli 20. We used square light patches of 100, 120, 140, or 160 µm2. The light patches could occur anywhere on the OB dorsal surface, but did not overlap. Previous studies used a sequential scan of the dorsal OB surface, dividing it into n by m non-overlapping patches, typically of the size of a glomerulus 14,18,21–23. Our projector system has a resolution of 20 µm/pixel, and since a glomerulus is estimated to be ~100 µm in diameter, our stimulation protocol could hit a glomerulus at ~25 locations (Fig. 1a). This high-resolution map nicely revealed the glomerular ellipsoid structure (Supplementary Fig. 1a, left panels).

To estimate the location and number of glomeruli in this relatively high-resolution RF map, we converted the RF map values to z-values. We then filtered out isolated pixels, assuming that a suspected glomerulus must contain at least several pixels, and set values below a certain threshold to zero (Methods). We termed this RF map the ‘z-scored’ map (Supplementary Fig. 1a, middle panels). To estimate glomerular locations from the z-scored map, we developed an iterative algorithm that fits 2D ellipsoid Gaussians to each local maximum and minimum (Methods). The Gaussian fit was performed on 80% of the data. We evaluated fit quality by assessing how well the reconstructed map predicted the firing rates elicited by the remaining 20% of light patterns. We termed this map the ‘glomeruli-fitted’ map.

As expected, most recorded MT cells had an RF-map composed of one excitatory glomerulus (mean ± std = 1.1 ± 0.3, median = 1, N = 15 MT cells from five mice; Supplementary Fig. 1b). The number of inhibitory glomeruli was low (0.5 ± 0.8, median = 0, Supplementary Fig. 1b), which is consistent with recent papers showing activity-dependent weak lateral inhibition between MT cells 19,20,23–25. The inhibitory effect could alter the temporal dynamics of the MT cell response – delaying the MT response or shortening the response time without affecting the peak response (Supplementary Figs. 1c-d). Interestingly, two MTs had two adjacent excitatory glomeruli (Supplementary Fig. 1e). Finding more than one glomerulus in a small number of OB neurons could be the result of an artifact caused by the stimulation method (light hitting the superficial dendrites of the recorded MT in its vicinity or reflected from the electrode placed in the OB), clustering two MT cells from two different glomeruli as one, or the sensitivity of our fitting algorithm that might consider an elongated glomerulus as two glomeruli. The median evoked firing rate elicited by light stimulations hitting the estimated location of the glomerulus above the recorded MT was 15.3 spikes/sec (mean 17.8 spikes/sec), which is within the range of previously reported odor-evoked responses in anesthetized mice 19 (Supplementary Fig. 1f).

PC neurons receive excitatory and inhibitory inputs
Next, we applied the STA method to aPC neurons. We recorded 34 aPC neurons using a tungsten electrode in anesthetized mice and 38 using a custom-made tetrode bundle. Figure 1b shows three examples of PC neurons' RF-, z-scored-, and glomeruli-fitted- maps from three different mice. Each neuron receives input from a different number of glomeruli dispersed over the OB surface. Both excitatory and suppressive glomeruli were detected. Unlike MT neurons, PC neurons require more light-patterning stimuli to reach a stable RF map. We considered a neuron stable-convergent if its RF and z-scored map constructed from the first half of the stimuli resembled the map built from the second half (see Methods). Of the 72 recorded aPC neurons, 50 (69%) met this criterion (Methods). The convergence scores varied considerably (Supplementary Fig. 2). The main reasons for the lack of convergence are the use of too few light patterns or insufficient neuron stability (Supplementary Fig. 2; Methods). Analyzing all converging neurons, we found that each aPC neuron received, on average, excitatory input from 4.9 ± 2.4 glomeruli (mean ± std, median = 4) and inhibitory input from 2.2 ± 1.2 glomeruli (median = 2, Fig. 1c upper panel). The number of inhibitory glomeruli scaled with the number of excitatory glomeruli (r = 0.37, P = 0.008, N = 50, Pearson correlation, Fig. 1c lower panel). There was no correlation between the number of glomeruli found and the light patch sizes (P > 0.3). To examine whether the spatial distribution of glomeruli is randomly scattered on the OB surface or has some organization, we compared the pairwise distances between all pairs of glomeruli within an RF map to the distances expected by assuming a random location. We found that these two distributions did not differ significantly (median P across 30 simulations ~ 0.2; KS test, Supplementary Fig. 1g), suggesting that aPC neurons integrate from random sets of glomeruli. Examining how the response to light stimuli depends on the respiration phase revealed that some neurons responded to light stimuli at all phases, others elicited a stronger response at the neuron’s preferred phase, and some at the neuron’s non-preferred phase (Supplementary Fig. 3a). Overall, the response to light was moderately correlated with the respiratory dynamic (Supplementary Fig. 3b). The convergence scores were similar when we computed them based on light stimulations that occurred in the first and second half of the respiration phase (r = 0.95, P = 4.4e-38, N = 72, Pearson correlation, Supplementary Fig. 3c) suggesting that the exact respiration phase of the light stimulation does not strongly affect the RF map. We conclude that aPC neurons are connected to a relatively small number of glomeruli that are randomly dispersed on the dorsal OB surface, and that these glomeruli can exert either excitatory or inhibitory effects.

PC neurons respond to multiple distinct patterns composed of small subsets of glomeruli
The STA method can reveal how stimulating a single glomerulus or a combination of glomeruli affects an aPC neuronal response. To examine this, we plotted the neuron’s PSTH while restricting our analysis to light patterns containing light patches that hit a particular glomerulus or glomeruli combination. Figure 2a shows the responses of an aPC neuron for which we have identified presumably seven excitatory contributing glomeruli. Plotting the response of this neuron to all light patterns that contained a light patch that hit one of the six selected glomeruli that did not hit the other five, and the response to light patterns that hit all six glomeruli simultaneously shows that this PC neuron did not respond when one glomerulus was stimulated but responded strongly when all six glomeruli were stimulated (Fig. 2a left panel). Considering all possible combinations of the six glomeruli shows that this aPC neuron started to respond when at least three glomeruli were stimulated, and its response increased as more glomeruli were stimulated (Fig. 2a, middle and right panels). Not all triples of glomeruli activated the neuron. The example in Figure 2b shows the response of an aPC neuron that receives input from presumably six excitatory glomeruli. This neuron responded weakly when one of these glomeruli was light-stimulated, and the response increased moderately as two or more glomeruli were stimulated. The response peaked when all glomeruli were photo-stimulated. The example in Figure 2c shows a neuron receiving input from presumably nine excitatory glomeruli. This neuron responded weakly when three or four glomeruli were light-activated. Examining neurons containing inhibitory glomeruli in their RF map suggests that light activation of inhibitory glomeruli can reduce the neuron’s firing rate or change its temporal dynamics (Supplementary Figs. 4a-b). Plotting the normalized response of all aPC neurons as a function of the number of excitatory glomeruli out of six indicates that aPC neuron responses increase moderately with the number of stimulated glomeruli (Figure 2d). The rate of increase may appear to saturate as more glomeruli are activated. However, since the number of trials in which light patterns activate more than six glomeruli decreases exponentially, this could be due to an analysis based on a small number of points.

The response of an aPC neuron is likely to depend more on the total synaptic input it receives than on the number of activated glomeruli. To examine this, we plotted the aPC firing rate of the example shown in Fig. 2a as a function of the sum of the estimated synaptic weights of the light-stimulated glomeruli. This analysis demonstrated that this neuron response is strongly correlated with the sum of the synaptic weights (r = 0.88, P = 3.0e-22, Pearson correlation, Fig. 2e). For example, this neuron response was higher when two strongly connected glomeruli were stimulated and did not respond to the stimulation of three weakly connected glomeruli (compare red and green circles in Fig. 2e). To examine this across all neurons, we compared how the number of glomeruli or the synaptic weights sum explains the neuron firing rate using a linear regression model. We found that a model based on the sum of synaptic weights better explains the firing rate than a model based on the number of glomeruli (Fig. 2f, P = 0.0004, df = 48, 2-sample paired t-test of the adjusted R2 values). Fitting a quadratic model did not improve the prediction power compared to the linear model.

We summarize that an aPC neuron is activated even when a subset of its connected glomeruli is active, if the synaptic strengths of these connections are sufficiently high. Its response scales moderately with the number of stimulated excitatory glomeruli and synaptic strengths. Several distinct sets of glomeruli can activate the same aPC neuron, indicating that aPC neurons are not tuned to detect only one unique glomeruli pattern.

Neighboring aPC Neurons have Overlapping Receptive Fields.
We next examined the relationship between RFs of adjacent and distant aPC neurons. Figure 3a shows the z-scored and glomeruli-fitted maps of two pairs of units recorded on the same electrode. As can be seen, there is considerable similarity between these maps. The input regions in the two maps are in similar OB regions (e.g., the left upper corner in the right example and the right part of the OB in the left example). Computing the similarity between the two z-scored maps by computing the correlation between the two matrices reveals that these maps share a substantial part of their activation regions ( Fig. 2; r = 0.24, P << 0.001, N = 2501 pixels and r = 0.42, P << 0.001, N = 2911 pixels, Pearson correlation - All correlation values above 0.1 or below -0.1 were highly significant, due to the large number of pixels in each map). The spike sorting quality parameters and the mean waveforms of these units are shown in Supplementary Fig. 5a, indicating that these maps are highly likely to represent two different neuron pairs and not the same neuron that has been mistakenly clustered into two different neurons (see also the next section and the Methods section for additional analysis and discussion on this issue). Additional examples of units recorded on the same electrode with varying levels of overlapping regions and adjacent regions are shown in Supplementary Fig. 5b. Interestingly, some neuron pairs had significant negative correlations (Supplementary Fig. 5c), suggesting that close-by neurons can have anti-correlated maps: One neuron receives excitatory input from one OB region, whereas the second neuron receives inhibitory input from the same region. Plotting the cumulative distribution function of the correlation values revealed higher rates of both negative and positive correlation values for neurons recorded on the same electrode (Supplementary Fig. 5d-e). To take this into account, we computed the correlation between the absolute values of the z-scored maps (values shown in parenthesizes in Supplementary Fig. 5c). Comparing the correlations between all pairwise absolute z-scored map values recorded on the same electrode to those recorded on different electrodes or experiments suggests that spatially close neurons are more likely to have overlapping input regions (mean correlation = 0.16 in neurons recorded on the same electrodes/tetrode bundles compared to 0.06 for neurons recorded on different electrodes/sessions, N = 50 neurons, Wilcoxon rank-sum test, Fig. 3b-c). When we compared all maps across all sizes (by clipping the larger map), the P value was 5e-16 (Z = 8.1, r = 0.22, , 2-sided Wilcoxon rank-sum test).

A significantly increased correlation was detected even when we considered the single and tetrode recordings separately (Fig. 3b). Occasional high correlation values were observed between neurons recorded on different sessions in the same mouse in consecutive experiments in which the recording site was near the previous one (Fig. 3b, values outside of the black rectangles and inside the white rectangles). Comparing the correlation between all glomeruli-fitted maps revealed that glomeruli maps of neurons recorded on the same electrode/tetrode bundle are more similar than glomeruli maps recorded on different electrodes/tetrodes (N = 50, Fig. 3d, Wilcoxon rank-sum test). This increased similarity strongly suggests that close-by aPC neurons tend to share glomeruli or have more overlapping glomeruli than distant ones. Computing the percentage of overlap between glomeruli, we found that, on average, 18% of the glomerular area was shared when the units were recorded on the same electrode, compared to 12% when recorded on different electrodes/sessions (N = 50; Fig. 3e; Wilcoxon rank-sum test).

The analyses above quantify similarity between maps based on overlapping regions. However, it cannot capture map similarity when input regions are close but do not overlap. To quantify this, we estimated the distances between glomeruli patterns of all neuron pairs using two different metrics. The first metric was based on computing the Euclidean distance between the two patterns’ centroids. We found that glomeruli pattern centroids tend to be closer in neurons recorded on the same electrode or tetrode bundle than on different electrodes or recording sessions (N = 50, Wilcoxon rank-sum test, Fig. 3f). The centroid-based metric is good if the two patterns are located in similar OB regions, but it can give inaccurate results when the two patterns are spread over different parts of the OB and have similar centers. We therefore used a second metric, in which we computed for each glomerulus and each pattern the shortest Euclidean distance to one of the glomeruli in the other pattern. We defined the distance between two patterns as the average distance across all glomeruli-pair distances for the two patterns. This analysis revealed a significant difference in the glomerular pattern distances between close-by and distant units (Wilcoxon rank-sum test, Fig. 3g). Taken together, these analyses suggest that close-by neurons have more similar glomerular patterns because they share a few glomeruli or because the glomeruli are located in nearby regions. These glomeruli can exhibit the same sign (i.e., excitatory or inhibitory) in both neurons and occasionally opposing signs.

The More Distant aPC Neurons are, the Less Similar their RF Maps
Recording with a single electrode or a tetrode bundle cannot provide insight into how the distance between neurons correlates with their RFs. To examine this, we recorded an additional set of aPC neurons using NeuroNexus probes with 32 contacts, for which the contact spacing is known. We used two different electrode configurations (Supplementary Fig. 6a) and conducted 15 recording sessions in nine anesthetized mice. We obtained 411 neurons, of which 244 met our convergence criterion. Recomputing the number of excitatory and inhibitory glomeruli in this experiment yielded results similar to those obtained in the single electrode/tetrode experiments (Supplementary Fig. 6b and compare with Fig. 1c). Plotting the similarity between the absolute z-scored maps against the neurons’ estimated pairwise distances of one recording session revealed that, consistent with our previous observation, nearby neurons can have similar and less similar maps (Fig. 4b). However, there was a trend towards a negative correlation between neurons estimated pairwise distances and their map similarities (r = -0.33, P = 0.07, N = eight converging neurons out of 20 recorded, Pearson correlation; Fig. 4a) suggesting that neurons in closer proximity are more likely to have similar RF maps. Plotting the similarity between the absolute z-scored maps against the neurons’ estimated pairwise distances of all recorded neurons revealed a significant negative correlation (r = -0.09, P = 5.3e-7, Pearson correlation, Fig. 4c). Applying a statistical test with a mixed-effects model to verify that the significant correlation did not result from animal-to-animal variabilities and within session dependencies confirmed this correlation is significant (P = 6.2e-5, linear mixed-effects model with slope and intersect as random effects, Fig. 4c). This negative correlation was robust as it did not depend on the criterion used to define a map as converging (P ≤ 0.003 for all threshold values ranging from zero to 0.3, linear mixed-effects model, Supplementary Fig. 6d). The negative correlation was not biased by conducting multiple recording sessions in the same mice, as restricting the analysis to recordings that were conducted in different mice gave similar results (48 possible different nine recording sessions out of the 15 sessions; correlation value ranged from -0.07 to -0.23 and P value ranged from 0.0001 to 0.02). Excluding neurons that have a small number of excitatory glomeruli, which can spuriously affect the correlation value, increased the strength and significance of the correlation between distance and RF similarity (r = -0.11 and r = -0.12, when we removed neurons with only one or two excitatory glomeruli, respectively, P < 1e-6 for both, linear mixed-effects model). Examining each of the recording sessions revealed that in 12 out of 15 there was a negative correlation between neuron distances and their map similarity which is unlikely to occur by chance (P = 0.017, binomial test, Fig. 4d). A negative correlation between map similarity and unit pairwise distances was also observed when we split the recording sessions into recordings in which the electrode spanned the aPC in the, medial-lateral (ML) and the dorsal-ventral (DV) axes but not in the anterior-posterior (AP) axis, (r = -0.06, -0.07, -017; P = 0.0004, 0.27, 0.0009, for ML, AP and DV respectively; Supplementary Fig. 6c).

One possible caveat of this analysis is that neurons located close to each other might be more contaminated than distant ones, leading to two neurons that are considered different but are the same, thereby biasing the similarity of nearby neuron RF maps toward high levels. To rule this out, we examined how the correlation between neuron distances and map similarity is affected when we restrict our analysis to neurons at or above a given distance. We found that the correlation was significantly negative, even when we considered neurons that are 100,150, 200, and even 250 μm apart (Supplementary Fig. 6e). Neurons that are more than ~100 μm apart are very unlikely to represent the same neuron, as non-overlapping channels capture them. These analyses strongly indicate that the farther the neurons are, the less similar their RFs are.

Although map similarity reduced with distance, there were notable exceptions. First, many of the nearby neurons could have dissimilar maps (dots around zero in Fig. 4c). Second, several distant neurons exhibit high similarity. For example, Figure 4e shows the z-scored and glomeruli-fitted maps of three neurons estimated to be located 363, 371, and 405 μm apart. These maps are highly similar (r = 0.92, 0.8, and 0.76, respectively, Pearson correlation of absolute RF maps, color arrowheads in Fig. 4c corresponding to color panels in Fig. 4e). The glomeruli-fitted maps suggest that both units share at least one glomerulus and differ in the location of one or more glomeruli. These z-score values are considerably high, ruling out the possibility of falsely detected glomeruli (Fig. 4e, upper panels). These units were detected by different groups of channels that are far apart (Fig. 4e, lower panels) and are thus very unlikely to be contaminated or the same neuron split into two units.

Subsets of aPC neurons receive input from different numbers of glomeruli
Anatomical studies suggested that inhibitory neurons in aPC receive input from a larger set of MT cells compared with excitatory neurons 7. Recordings in PC are likely to capture pyramidal and semilunar cells, and to a lesser extent, inhibitory neurons 26–29. Discriminating between pyramidal and semilunar neurons based on their spike waveforms is not yet considered accurate 26. However, several studies have attempted to classify aPC neurons as putative excitatory or inhibitory based on waveform width and firing rate 26,28,30,31. The exact threshold values for spike width and firing rate used in these studies ranged from 0.2 to 0.5 ms and from 0 to 10 spikes/sec. This is probably because estimating spike widths depends on the recording setup, the recording methods, and the type and range of the applied filters (Supplementary Fig. 7; Methods). To avoid setting arbitrary threshold values and capture other possible groupings, we compared the number of excitatory glomeruli in the RF maps across a range of baseline firing rates and spike-width thresholds (N = 267 neurons recorded with tetrodes or silicon probes; Methods). This analysis revealed two clusters in which the number of excitatory glomeruli in the RF differs significantly (P < 0.05, Wilcoxon rank-sum test; Figure 4f; Methods). The first cluster (marked with *) separates aPC neurons into narrow-spiking, relatively high baseline firing-rate neurons (firing rate ≥ 9 spikes/sec and waveform width ≤ 9/32 = 0.281 ms) versus wide-spiking, low-firing-rate neurons. High-firing and narrow-spiking neurons are commonly identified as interneurons. Only 18/267 (6.7%) of the recorded neurons were classified as putative interneurons, consistent with recent reports suggesting that recordings in aPC yield only a small number of inhibitory neurons 26–28,30. Our analysis thus suggests that narrow-width high-firing neurons receive input from ~27% more glomeruli on average (4.9 versus 3.85; Fig. 5a, purple shading; Fig. 5b, left two box plots). Interestingly, this analysis revealed a second cluster (marked with ** in Fig. 5a) that separates neurons into those with very low firing rates (< 1 spikes/s) and those with firing rates of at least 1 spike/s (‘fr ≥ 1’, N = 175 neurons; ‘fr < 1’, N = 92 neurons). The number of excitatory glomeruli was significantly higher in the low-firing-rate group than in the high-firing-rate group (P = 0.0001, two-sided Wilcoxon rank-sum test, Fig. 5b). One possible explanation for this result is that low-firing-rate neurons converge better and, as a result, reveal more glomeruli. However, convergence level and firing rate were not correlated (P = 0.4, Spearman correlation, N = 267). Furthermore, the number of excitatory glomeruli did not correlate with the neuron firing rates (P = 0.26, Spearman correlation). Together, these findings suggest that low-firing-rate neurons and putative interneurons (high-firing but with narrow spike widths) are two groups of neurons that receive input from a greater number of excitatory glomeruli, despite their very different firing rates. The group of neurons with a baseline firing rate of at least one spike/s and that are not interneurons has the lowest average number of excitatory glomeruli. It is worth noting that a recent study showed that some high-firing, narrow-spiking neurons could be semilunar cells 26. We did not find significant differences in the number of inhibitory glomeruli across baseline firing-rate or waveform-width groups.

Receptive fields in the awake state
The animal state shapes neural dynamics. In the PC of an awake mouse, there is an increase in baseline activity, inhibition load, and recurrent activity, which likely contribute to an improved odor representation 32. To examine the RF of PC neurons in the awake state, we recorded from 181 neurons using silicon probes (three mice, five sessions, 11526 light stimulations on average). We found that the rate of RF convergence was much lower in the awake state (66/181) than in the anesthetized state (244/411; P < 1e-8, proportion test). This lower rate could result from greater recording instability, including frequent changes in firing rate and animal activity, observed in awake mice passively stimulated with light for long periods. Examining how repeated light stimulation affected the neurons’ firing rate suggested that PC neurons in the awake state are less responsive and more unstable to light stimulations (Supplementary Figs. 8a,b; see Methods). However, the neurons were less susceptible to habituation, defined as a reduction in firing rate due to repeated light stimulation (quantified by the negativity of the firing rate slope, Supplementary Figs. 8c,d), likely because they responded less to our light stimulation protocol. The mean number of excitatory glomeruli was significantly lower in the awake state (P = 2e-8, df = 253, Z = 5.8, 2-sample t-test, Supplementary Figs. 9b-c). The mean number of inhibitory glomeruli did not differ significantly between the two animal states (P = 0.099). As in the anesthetized state, the number of inhibitory glomeruli tended to scale with the number of excitatory glomeruli (r = 0.23, P = 0.06, N = 66, Pearson correlation, Supplementary Fig. 9d), and there was no correlation between spike waveform width and the number of connected glomeruli (Supplementary Fig. 7c, P = 0.47, N = 66, Pearson correlation). There was no correlation between neuron-pairwise distances and RF map similarity (r = 0.11, P = 0.9, linear mixed-effects model; Supplementary Fig. 9e). This lack of correlation in the wake state could be due to RF map instability or the use of a small dataset, or the result of comparing maps with a small number of glomeruli, which increases spurious map similarities. Alternatively, it may suggest that, in the awake state, nearby neurons have more dissimilar RF maps than in the anesthetized state due to stronger recurrent activities and a higher inhibitory load.

Receptive Field Similarity Aligns with Weak Odor Response Similarity
Our results suggest that, in an anesthetized state, nearby aPC neurons tend to receive input from more nearby regions and share more glomeruli than more distant neurons. In a few cases, nearby aPC neurons with overlapping input regions can have opposing synaptic valences (e.g., Supplementary Fig. 4c), and distant neurons can have similar RF, possibly through long-range connections (Figs. 4c,e). This similarity structure suggests that the odor-tuning curves (OTCs) of nearby aPC neurons will be dissimilar, as has been reported in several previous studies of aPC 6, 8, 9, 11 and posterior PC 13. However, an exact quantification of OTC similarity as a function of the neuron distances in aPC is still missing. A recent study reported the responses of two sets of aPC neurons to 15 and 13 odors 6. Neurons were recorded in awake mice using four-shank silicon probes in which the distance between shanks is 200 µm. Each shank contained eight contacts spread at a 140-150 µm range. Their analysis suggested that the OTC similarity of neurons recorded on the same shank is similar to that recorded on different shanks (Fig. 7a in Iurilli & Datta., 2017). However, comparison of OTCs as a function of neuron distances was not reported. We re-analyzed these datasets, focusing on how the distance between neurons relates to their OTCs. Analyzing the larger dataset reporting the responses of 199 aPC neurons to 15 odors revealed that OTCs of neurons recorded on the same shank are significantly more similar than those recorded on different shanks (mean correlation = 0.049, median = 0.061, P = 8.6e-14, two-sided Wilcoxon rank-sum test, N = 199 neurons, Fig. 6a). Although this mean correlation value is small, it cannot be explained by neural fluctuations or other external factors, as applying the exact computation on the same time duration before odor onset resulted in a correlation value that is not significant (mean = 0.005, P = 0.44, Z = 7.4, two-sided Wilcoxon rank-sum test). Furthermore, we found that the similarity between neuron OTCs is negatively correlated with the number of shanks separating them, suggesting that OTC similarity decreases with increasing distance between neurons (Fig. 6b; r = -0.99, P = 0.009 when considering the mean correlation values within each shank distance; Pearson correlation). Moreover, OTC mean similarity values were significantly above zero when comparing neurons located on the same shank or one shank from each other (Fig. 6b, P < 0.002 for shank distances 0 and 1, one-sided t-test) and below zero when the distance was four shanks (P = 0.02, two-sided t-test). The results were similar when we restricted the computation to neurons that responded to at least one odor (Supplementary Figs. 10a-b; P = 0.0008 when comparing the correlation on the same and different electrodes; P = 0.78 when applying the exact computation on the same time duration before odor onset). These results suggest that neurons located up to 200 µm apart exhibit odor responses that are more similar than expected by chance, and that this similarity decreases with distance. It is important to note that neurons separated by more than ~100 µm are very unlikely to be recorded on the same contact.

Analyzing the dataset based on 13 natural odors provided some additional support for this conclusion, as the OTCs of neurons recorded on the same shank were significantly higher than those recorded on the different shanks (P = 9.7e-9, Z = 5.7, two-sided Wilcoxon rank-sum test, Supplementary Figs. 10c-f). However, the mean OTC similarity did not correlate with the distance due to an unclear significant increase in OTC similarity between neurons located four shanks apart from each other (Supplementary Figs. 10c-d), and there was a significant difference when applying the exact computation on the same time duration before odor onset (P = 0.03, two-sided Wilcoxon rank-sum test). Similar results were obtained when considering only neurons that responded to at least one odor (P = 8.6e-14 compared to P = 0.59 when applying the exact computation on the same time duration before odor onset; Supplementary Figs. 10e-f).

The two datasets above did not report the exact location of each contact on each shank, limiting the analysis to distances between shanks. We therefore recorded the response to 15-20 odors of an additional 286 aPC neurons in eight experiments in four anesthetized mice and 185 aPC neurons in seven experiments in three awake mice. We found that OTC similarity was significantly correlated with the pairwise distances of the neurons in both the awake and anesthetized datasets (Fig. 6c-d, P < 9e-5 in both, linear mixed-effects model). This result cannot be explained by mistakenly clustering a single neuron into two nearby neurons, as the correlation remained highly significant even when considering only neuron pairs separated by at least 200 μm in both anesthetized and awake mice (Supplementary Fig. 10f). The results were similar when we the analysis was restricted to neurons that responded to at least one odor and when Spearman’s correlation was computed instead of Pearson’s.

Taken together, these results suggest that, on average, close-by neurons tend to have more similar OTCs than distant ones. This is consistent with the finding that adjacent aPC neurons receive odor information from glomerular sets with small overlapping regions, sharing only a small number of glomeruli and sometimes opposing synaptic valence. Figure 5e summarizes our suggested model of how aPC neurons integrate glomeruli inputs.

Neurons that have Similar RF Maps have Similar Odor Tuning Curves
The finding that nearby aPC neurons have more similar OTCs and RF maps suggests that neurons that have similar RF maps should also tend to have more similar OTCs. To test this, we analyzed a subset of three recorded sessions from two anesthetized mice, in which we recorded the neuron’s RF and responses to a set of odors (20 odors in one mouse and 18 in the second mouse). We then compared the neurons’ OTC pairwise similarity with the neurons’ pairwise z-scored map similarity. Strikingly, we found a significant positive correlation between the z-scored maps and OTC similarities (r = 0.07, P = 0.007, linear mixed-effects model, Fig. 6f). We conclude that nearby neurons exhibit greater similarity in their OTCs and RF maps, indicating a topographic organizational principle among aPC neurons.

Methods
Surgical procedure
All surgical and experimental procedures were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and Bar Ilan University guidelines for the use and care of laboratory animals in research, and were approved and supervised by the Institutional Animal Care and Use Committee (IACUC) of Bar-Ilan University. Mice were housed under controlled environmental conditions with a reversed light/dark cycle, at an ambient temperature of 22 °C and relative humidity of approximately 52%. Mice were housed in a group cage and received no experimental treatment other than genotyping. We used 30 Tbet-Cre mice (Strain #:024507, Jackson Labs) crossed with the Ai32 strain (Strain #:024109, Jackson Labs), which expresses Channelrhodopsin-2 in mitral/tufted (MT) cells. Animals were anesthetized with ketamine/medetomidine (60/0.5 mg/kg, intraperitoneal) and placed in a stereotaxic frame. The bone overlying the dorsal olfactory bulbs (OBs) was removed to expose the largest possible dorsal surface. To allow electrode access, the bone overlying the anterior piriform cortex (aPC) was also removed. Supplemental anesthesia was administered as needed (30–50% of the initial dose of ketamine/medetomidine). Body temperature was maintained at 36–37 °C using a homeothermic blanket system (Harvard Apparatus). We used seven WT (C57BL/6JOlaHsd, Harlan Labs, Israel) mice for the odor experiments. We did not limit our experiment to males or females.

In Vivo Extracellular Recordings
Overall, this study is based on 1130 aPC and 15 MT neurons recorded in our lab, and analyzes an additional 477 aPC neurons taken from previously published datasets. The detailed breakdown of the number of neurons recorded in each experimental condition, the number of mice used, and the number of recording sessions per mouse is provided in Supplementary Table 1. Receptive fields were computed for 483 neurons in anesthetized mice and 181 in awake mice. Odor response analysis was applied to 466 neurons recorded in our lab and 477 neurons from Iurilli & Datta, 2017 study. The majority of the RF experiments were conducted under anesthesia to increase recording stability and minimize variability in neural activity caused by the animal’s state (e.g., motivation, stress, motor actions, changes in respiration frequency). However, we also recorded 181 neurons in the awake state (five sessions from three mice).

Electrodes were typically inserted into the anterior piriform cortex (aPC) at ~1.8–2.4 mm anterior to bregma and 1.2–2.1 mm lateral from the midline (depending on the anterior coordinates). Recordings began ~20 minutes after probe insertion to allow stabilization.

In the OB and some aPC experiments, tungsten electrodes (5–10 MΩ) were used. Neural signals were amplified and filtered between 300 and 5,000 Hz (AM-Systems 1800), sampled at 40 kHz (National Instruments, Austin, TX), and stored on a computer. Spikes were sorted offline using MClust (MATLAB, A.D. Redish). In some single-electrode recordings, the same neuron was recorded across multiple sessions and then combined based on waveform similarity. This was done to assess responsiveness and to light and guide electrode relocation if necessary. This procedure biases the estimated rate of converging neurons in single-electrode recordings. Using tungsten electrodes, we recorded 15 mitral/tufted (MT) neurons from five mice and 34 aPC neurons from 17 mice.

In addition, we employed recordings in aPC using custom-made bundles of one to eight tetrodes. Neural signals were amplified and filtered between 300 and 6,000 Hz (Neuralynx), sampled at 32 kHz, and stored. Spikes were sorted offline using SpikeSort3D by visually identifying dissociable clusters for each electrode or tetrode, and validating them with auto- and cross-correlograms across all neurons recorded in the same session. Using custom tetrodes, we recorded 38 aPC neurons from four mice.

In additional RF experiments, we recorded aPC neurons using NeuroNexus 32-channel silicon probes (A1X32-Poly3-5mm-25s-177I-CM32 and Buzsaki32). We recorded 411 aPC neurons across 15 sessions from nine mice. In three recordings, we positioned the four shanks along the anterior–posterior axis (1.22–1.82 mm and 1.6–2.2 mm). In seven experiments, we recorded from the same cortical region after moving the probe, typically ~200-300 µm posterior to the original location. In one recording, we sampled the same region but detected different neurons. The results of the analysis remained consistent when this dataset was excluded and when we analyzed only experiments recorded on different mice (48 possible combinations; see text). For the NeuroNexus array, we spike sorted the data using KiloSort4 and curated it with Phy. Kilosort4 considers the contact arrangement and ensures the integrity of the recorded data by preventing neuron redundancy across multiple channels.

Spike waveform analysis:
We computed spike waveform width as the interval from the peak to the trough. The peak was defined as the largest extremum, and the trough as the subsequent extremum. This approach performs well for regular and inverted spikes, as well as for spikes with a slight rising edge preceding the central peak (Supplementary Fig. 7a). When a spike was detected on multiple channels, we computed the width using the channel with the most prominent peak. The mean ± std waveform width in the Tungsten electrode, tetrodes, and silicon probes was 0.49 ± 0.14, 0.31 ± 0.06, and 0.38 ± 0.16 ms, respectively (mean ± std, Supplementary Fig. 7b). The mean spike widths in the tetrode and Silicon probe recordings were not significantly different, and we therefore combined them when we analyzed spike widths. Several recent studies reported a median spike-wave width ranging from ~0.3 to ~0.75 ms 26,28,31,68,69. The spike waveform width depends on several parameters, including the recording method, recording device, electrode type, sampling rate, and filter types. Indeed, the mean waveform width in the tetrode and silicon probe recordings was narrower than in the Tungsten electrode (P = 6e-5, 2-sample t-test, df = 483), suggesting that different filtering methods (analog versus digital) can affect spike widths. Our multi-channel recording system is based on Digital Lynx Sx (Neuralynx), in which we applied an online 256-tap FIR 300-6000 Hz filter, which can result in a narrower waveform width due to the application of DC filter combined with a high-pass filter. To verify this in an independent recording, we computed the waveform width of 28 neurons recorded in Dan Rokni’s lab from mice’s aPC using tetrodes and applying a 256-tap FIR filter between 300 and 5000 Hz. We found that the mean waveform width in this dataset was shorter than the range we obtained in the silicon probe recordings. A narrower average waveform width was also reported in a recording done with the Neuralynx system in rats aPC while applying a 60-6000 Hz filter 69.

To find possible differences in the number of connected glomeruli between different groups of neurons, we examined all possible combinations of firing rate (≥ 0 to ≥20 spikes/s) and waveform width (≤5/32 to ≤32/32 ms) values. This procedure revealed two clusters of parameter combinations that crossed the P < 0.05 (shown in Figure 4f). The lowest P value in the group marked with ** was 6.61e-6, which is significant even after Bonferroni correction for multiple comparisons (P = 0.0012). It also survives a shuffling analysis (P = 1/300). The lowest P value in the group marked by * is 0.028, which does not exceed the multiple-comparison Bonferroni correction or a shuffling analysis. To ensure that baseline activity is not affected by the light stimulation, we computed it during the 10 seconds starting 10 seconds after the last light stimulation.

STA model: We assumed a linear-nonlinear-Poisson (LNP) model 16, wherein on each time step, the components of the stimulus vector are linearly combined using a weight vector, k. The response of this linear filter is then passed through a nonlinear function f, whose output determines the instantaneous firing rate of the Poisson spike generator. Under these assumptions, the STA method enables us to estimate k and f.

Optical stimulation of the olfactory bulb
Optical stimulation of the OB was performed as previously described (Haddad et al., 2013). Briefly, spatially precise light patterns were projected onto the dorsal surface of the OB using a digital micro-mirror device (OPTOMA X600 DLP projector) coupled to an optical imaging system. A blue filter was placed above the collimating lens (f = 75 mm, achromatic doublet, Thorlabs), positioned 20 cm from the projector. Under this configuration, each projected pixel corresponded to an area of ~20 µm², with an estimated light intensity of ~1 mW/mm². Stimulation timing was controlled using the MATLAB Psychophysics toolbox. A photodiode (FDS1010, 400-ns rise time, Thorlabs) was used to record timestamps for each light stimulus.

The craniotomy size determined the total scanned area for each experiment. Light stimulation lasted 80–100 ms, followed by an inter-stimulus interval of ≥200 ms, which is sufficient for MT cell recovery 14. In most single- and tetrode-based recordings, we applied 8–12 light patches (140 × 140 or 160 × 160 µm²), with the number and size of patches fixed within each experiment. In two recordings, we used four larger patches (200 × 200 or 240 × 240 µm²), and in two others, 25 smaller patches (120 × 120 µm²). The number of light stimuli ranged from 3,290 to 18,481 (median = 9,983; mean = 10,155; mode = 15,000), with variability reflecting adjustments made to achieve convergence. Some experiments were terminated prematurely due to animal conditions or technical issues.

In silicon probe experiments, we applied 10–12 patches (120 × 120, 140 × 140, or 160 × 160 µm²) and typically delivered 10,000–15,000 trials (median = 10,000; mean = 10,770; maximum = 15,000). All neurons crossing the convergence score threshold were included in the analysis, irrespective of patch size, number of patches, or total stimuli (see definition of convergence below). Because stimulation patterns were randomized, the same MT cell was unlikely to be repeatedly activated.

Following the presentation of N random light patterns, we computed neuronal RFs using the STA method, which calculates the weighted average of all stimuli 16,70:

∑N S r
i=1 i i
S = 
∑Nr
i i

Where Sᵢ denotes the two-dimensional light pattern i and rᵢ is the firing rate of the recorded neuron in response to stimulus Sᵢ. Thus, S is the weighted average stimulus. Notably, the STA technique can detect both supra- and sub-threshold connections. This arises because some light stimulations simultaneously activate supra- and sub-threshold inputs, producing firing rates distinct from those elicited by stimulations that drive only supra-threshold connections. For the same reason, the STA method can also reveal inhibitory connections, even in neurons with low spontaneous activity, provided that a substantial number of excitatory glomeruli are present.

STA Significance: The significance of the STA vector was assessed using a bootstrap procedure, as described by71. Specifically, we randomly shuffled the light patterns and recomputed the STA. This process was repeated three times to generate a bootstrap distribution. For each pixel, we then estimated the probability of observing an STA magnitude greater than the 98th percentile or less than the 2nd percentile of the bootstrap distribution (corresponding to a z-value of ~2.1). Pixels that did not meet this threshold were set to zero. To further reduce noise, the resulting map was convolved with a two-dimensional linear filter, which reduced isolated or small patches of significant pixels. We refer to this final map as the z-scored RF map.

STA Map Convergence: To evaluate how well the RF maps represent the “true” receptive field, we defined a convergence score (also referred to as a stability score) as the correlation between maps constructed from the first and second halves of the stimuli. We computed this value for both the raw and z-scored RF maps. Because this measure is based on only half the trials, it underestimates the convergence value obtained using all trials. To establish a reasonable threshold for convergence, we computed convergence scores for all neurons after shuffling the light events. We then defined our threshold as values that exceed the 95th percentile (or fall below the 5th percentile) of the shuffled distribution. This procedure yielded a threshold of 0.07. For simplicity and to further exclude noisy maps, we set the threshold to 0.1. This cutoff effectively removed the left tail of the convergence score distributions (Supplementary Fig. 2b). The remaining neurons exhibited relatively high prediction values on the test set (Supplementary Fig. 2c). Excluding neurons below threshold did not alter the number of mice reported in each dataset. Importantly, our results were robust to the choice of threshold, as similar outcomes were obtained across thresholds of 0, 0.05, 0.1, 0.15, 0.2, 0.25, and 0.3 (Supplementary Fig. 4b).

The critical factor determining whether a neuron reached convergence was whether enough light patterns elicited substantial changes in its firing rate. When this number was low, the change in firing rate was small, or the neuron altered its response properties during the recording due to changes in animal state, the neuron typically exhibited a low convergence value. The correlation between convergence values and the number of light patterns applied was 0.31 (P = 0.1, N = 72, Pearson correlation; Supplementary Fig. 2d).

Several neurons responded to many light patterns with inhibition. Neurons displaying inhibitory responses required more light stimuli to converge, since inhibitory effects are usually smaller than excitatory responses and more challenging to detect when baseline activity is low (Supplementary Fig. 2e). Conversely, increasing the number of light patterns also raised the likelihood of losing neurons due to changes in their response properties over time.

To reduce the potential habituation of aPC neurons from multiple consecutive light stimulations, we paused the light stimulations for 2–3 seconds after every ~20–30 trials. Supplementary Fig. 8 shows the effect of multiple consecutive light stimulations on the neurons’ firing rate in two anesthetized and two awake experiments for both convergent and non-convergent RF maps. As shown, some neurons exhibit a substantial reduction in firing rate upon repeated stimulation. However, even neurons that undergo a significant decrease in firing rate reached a relatively stable RF map (convergence score > 0.1). The pauses in light stimulation were also used to safeguard against missing or phantom light events. When a light event was missing or extra, we removed the block (or blocks) of light events whose number of events did not match the expected value, rather than excluding the experiment.

Non-convergent neurons may be connected to glomeruli that are not located in the dorsal OB or have synaptic weights too low to drive substantial responses with the current protocol. Some neurons may not have converged because their response properties do not conform to the assumptions of the STA framework (e.g., the linear–nonlinear Poisson model).

Estimating glomeruli number and their locations:
We developed an iterative algorithm to fit two-dimensional Gaussian ellipsoids to local maxima and minima in the z-scored response map. Fitting was performed using 80% of the data, while the remaining 20% was used to evaluate prediction quality. Only glomeruli that improved the predictive power (i.e., > 0.0001 from current prediction) of the reconstructed map on the test data were retained. This procedure was repeated 30 times, and a glomerulus was considered valid if it was detected in at least 50% of the iterations and its center was located within half a glomerular diameter (~60 µm). This tolerance allowed for the detection of partially overlapping glomeruli. The final RF map prediction value was defined as the mean prediction accuracy across the 30 iterations.

The algorithm for a given neuron RF proceeded as follows:
1. Randomly split the dataset into training (80%) and test (20%) trials.
2. Construct the z-scored map from the training group.
3. Identify all local maxima and minima in the map.
4. Fit a Gaussian to each local maximum and update the glomerular map.
5. Evaluate the prediction of test responses using the updated map.
6. If prediction accuracy improved, keep the Gaussian; otherwise, discard it and proceed to the next local maximum.
7. Repeat the procedure for local minima.
8. Iterate this process 30 times to generate 30 candidate glomerular maps.
9. Cluster all candidate glomeruli across iterations; define a glomerulus as valid if detected in ≥50% of iterations, with centers allowed to vary within three pixels.

Because single-spot stimulation can elicit responses that broaden the estimated receptive field, the STA procedure for OB neurons revealed larger glomerular sizes. To account for this, we used a slightly larger Gaussian sigma (6 vs. 5) and applied two convolution steps to the raw maps.

Comparing RF maps
To estimate map similarity, we converted the two maps into vectors and computed their Pearson correlation. In a subset of experiments, we performed repeated recordings from the same region by moving the electrode or tetrode bundle by ~200 μm in the dorsal-ventral or the anterior-posterior axes. This procedure could artificially increase mean similarity across sessions. Nevertheless, despite this potential bias, the similarity between neurons recorded on the same electrodes remained significantly higher. We computed the correlation between map similarity and pairwise neuronal distance using Pearson's correlation and obtained results comparable to those obtained with Spearman's correlation. To account for dependencies in pairwise distance comparisons, we verified significance using the Mantel test (P < 1/1000 across 1000 iterations for the RF and OTC similarity analyses). Additionally, to account for dependencies among neurons recorded within the same session, we applied a linear mixed-effects model treating slope and intercept as random effects. All P values remained highly significant.

Spike clusters quality analysis
We assessed spike sorting quality using multiple criteria, including visual inspection of cluster separation in feature space, differences in spike waveforms, violations of interspike interval (ISI) constraints, cross- and autocorrelations, and temporal stability. In the silicon-probe experiments, the mean ISI violation rate (2 ms) was 0.19%, (median = 0.09%, maximum = 1.6%). In the single- and tetrode recordings, the mean ISI violation rate (2 ms) was 0.8% (median = 0.5%, maximum = 3.8%). In these recordings, the median signal-to-noise ratio (SNR), as defined by Suner et al. (2005), was 5.6. In the odor experiments, the mean ISI violation rate was 0.21% (median = 0.09%, maximum = 2.2%). Electrode positioning was validated both by detecting clear respiration-locked firing during recordings and by post hoc histological verification.

The increased similarity between RFs of neurons recorded on the same Tungsten electrode or on nearby tetrodes could occur when one neuron is mistakenly clustered into two or more units. While this is unlikely, given the apparent difference in neuron clusters and waveforms, we also conducted several additional analyses. First, in the single- and tetrode recordings, we compared the clustering quality (L-ratio and Isolation distance) of neurons recorded on the same electrode with that of neurons recorded on different electrodes. We observed no significant correlation between map similarity and clustering quality. Second, the L-ratio and Isolation distance values did not significantly differ in units recorded on the same electrode, and these recorded on different electrodes (Supplementary Fig. 5f). Third, given that anterior piriform cortex (aPC) neurons are phase-locked to specific respiration phases, especially in the anesthetized state 6,9,72, a neuron erroneously clustered as two distinct units should exhibit similar preferred phases. Consequently, we examined whether neurons recorded from the same electrode demonstrated more similar preferred phases than those recorded from different electrodes. We found no significant difference between the phase differences (Supplementary Fig. 5g). Thus, improperly clustering the same neuron into two units is unlikely to explain our results. We note that it is generally accepted that neurons recorded in the aPC with contacts > 100 µm are likely different. This is clearly demonstrated in the Buzsaki probe recordings.

Odors Application
Odors were delivered using a custom-built olfactometer. Odorants were diluted 1:100 in mineral oil and stored in sealed plastic tubes; this concentration was selected to reliably elicit detectable responses. Fresh odor solutions (5–10 ml) were prepared before each experiment. Airflow was regulated with a mass flow controller (Agilent, Alimc-2LSPM) and set to 0.5 l/min. Between stimulations, air circulated freely to minimize residual odor contamination, and a vent positioned behind the mouse removed remaining odors. All odorants were obtained from Sigma-Aldrich at the highest available purity. Stimulation times and sequences were controlled by custom LabVIEW scripts. Each odor stimulus lasted 1.5 s, with an inter-trial interval of 10 s. The number of trials ranged from 10 to 15, and the number of odorants per experiment ranged from 15 to 20. A blank stimulus was included to verify that responses were not due to mechanical stimulation. Odor-evoked responses were quantified across the stimulation window beginning at odor onset.

Analysis of odor-tuning curves
We first analyzed the datasets published by Iurilli and Datta (2017). One dataset contained 199 aPC neurons responding to 15 odors, and the second contained 278 aPC neurons responding to 13 odors. Each odor was presented ten times. To quantify similarity between OTCs, we calculated the mean firing rate across the ten trials over a one-second window, subtracting baseline spike counts from the same time interval before odor onset. The pairwise signal correlation, reflecting the similarity of neuronal tuning profiles, was computed as Pearson’s correlation coefficient between the two baseline-subtracted response vectors, as in the original study. Excluding neurons that did not respond to any odors yielded similar results (P < 0.05, two-sided two-sample t-test). We applied the same analysis to data we recorded in four anesthetized and three awake mice (eight and seven recording sessions, respectively). Because OTCs are typically not normally distributed, we used Spearman correlation as the primary measure; repeating the analysis with Pearson correlation produced similar results. In these odor experiments, the silicon probes spanned the ML axis (1.5–2.1 mm).

Statistical analysis
All statistical tests and analyses were performed using built-in functions or custom code in MATLAB R2024b. Details for each test, including the P value and the number of data points, are provided in the Results section, figure legends, or directly within the figures. In general, we used two-sided t-tests or Pearson correlations when the data could be assumed to follow a normal distribution, and Wilcoxon rank-sum tests or Spearman correlations otherwise. Additional statistical tests were applied as needed and are described in the text.

Box plots are based on MATLAB convention (box boundaries show the 25% and 75% percentiles (Q1 and Q3), red line denotes the median, lower and upper whiskers extend from the box edges to the most extreme data point within 1.5 × (Q3-Q1) of the box, ‘+’ represent outliers)

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study employs optogenetics combined with in vivo electrophysiological recordings to explore the topographical organization of information input in the piriform cortex. The authors developed a method they believe can reveal the receptive field (RF) of anterior piriform cortex (aPC) neurons to uncover the topographical organization within the aPC. Their findings indicate that spatially adjacent aPC neurons tend to exhibit similar receptive field properties. This discovery offers meaningful insights into how olfactory information is represented within the central nervous system. However, the study has several limitations in terms of experimental conditions and recording techniques, which may impact the generalizability and reliability of the conclusions. Additionally, the study lacks further exploration into the underlying mechanisms or biological significance. Below are my major concerns:\n1.All experiments were performed on anesthetized animals, which represents a distinct physiological state that can significantly affect neuronal activity in the piriform cortex (Bolding et al., 2020; Murakami et al., 2005). Therefore, the results may not fully reflect the true activity of aPC neurons in awake states, limiting the applicability of the study’s conclusions.\n2.Although the researchers employed various electrodes, including tungsten electrodes, tetrodes, and silicon probes, the recording range remained relatively limited. Given that the piriform cortex is a large region, these techniques may restrict the depth and breadth of the topographical investigation. Additionally, the sample size was relatively small, with only 50 neurons recorded in Figures 1 and 3, and 26 neurons in Figure 4. As the study aims to explore the spatial topographical structure of the PC, such a small dataset is more susceptible to random errors and biases, potentially compromising the stability and reliability of the conclusions. Thus, further studies with larger datasets are necessary to validate the findings.\n3.Although the authors developed a new technique capable of delivering thousands of different stimulus patterns, the reproducibility of responses under each pattern remains uncertain. There was considerable variability in the spiking activity of aPC neurons in response to identical stimuli, making it challenging to generalize the results from single stimulations. Therefore, the stability of the RFs obtained through this method requires further validation.\n4.It is difficult to confirm whether the authors selectively activated a specific glomerulus in the olfactory bulb. While the authors addressed this issue to some extent, they did not provide direct evidence. Even though the illuminated area on the olfactory bulb's surface was small, light penetration into deeper layers could lead to a larger effective area, raising concerns about the reliability of this method.\n5.The study would benefit from additional mechanistic exploration at the circuit level to further elucidate the findings. Moreover, incorporating behavioral experiments related to the observed phenomena could address some of the study’s limitations and enhance its quality.", "reviewer_2": "In this study by Baragin, Ochan et al., the authors developed an innovative and high-throughput screening method to characterize the receptive field of anterior piriform cortex neurons in ketamine/xylazine-anaesthetized mice. Using DLP-based pattern light stimulation of the dorsal olfactory bulb of mice expressing ChR2 in mitral/tufted cells, they show that recorded aPC neurons require the activation of multiple glomeruli to respond. The study provides precise descriptive data about the number & the spatial location of glomeruli as well as how glomeruli interact to drive the aPC neuron. Although the design of such innovative and unbiased optical stimulation method as well as the analysis of the data are quite elegant and convincing, the final observations are rather incremental, relative to previous papers (Apicella et al., 2010; Davison & Ehlers, 2011) and don’t bring new significant biological conclusions.\nBelow is a list of points that could help strengthening the manuscript and increase the interest of the neuroscience community :\n1) The identity of the recorded aPC neurons is not documented. The aPC contains at least two distinct populations of principal neurons —pyramidal cells and semilunar cells— and it would be important for the field to describe whether pyramidal versus semilunar neurons have distinct RFs. Does their data set also contain local interneurons? Optogenetic-based tagging and identification of the different types of neurons of the aPC could be used to classify the recorded neurons.\n2) The recordings are generated in anaesthetized animals, which makes difficult to generalize their observations to the awake state. Awake recording is the standard in the field and the authors should demonstrate that their conclusions stay valid in the awake state. This comparison is even more necessary given that, in the last part of the manuscript, they compare their anaesthetized data with awake data from Iurilli and Datta, 2017.\n3) The aPC responses are not analyzed relative to respiration/sniffing. Are the response of an individual aPC neuron to the activation of multiple glomeruli changes over respiratory cycle? Does the receptive field vary over respiratory cycle? Changes in excitability and/or recurrent feedback along the respiratory cycle may significantly change their spiking readout.\n4) How to exclude the contribution of indirect recurrent inputs to the recorded aPC responses? The authors don’t provide any clear time constraints to select monosynaptic responses versus polysynaptic responses from the aPC recurrent network. Their RF measurement may be potentially contaminated/overestimated by the contribution of aPC recurrent inputs.\n5) Is the measured RF (and the associated connectivity) stable over time or can this RF evolve with repeated stimulation (ie adaptation/habituation), experience (passive exposure) or associative learning? The authors should include soma data to tackle this questions of stability/plasticity, which would strongly benefit from their innovative stimulation methods.\n6) The authors should include stimulation with odor stimuli and test 1) how this odor-evoked stimulation can affect the measured RF, 2) whether two aPC neurons with close RF have similar odor-receptive field.", "reviewer_3": "This manuscript presents novel data on the spatial organization of the functional connectivity between the sheet of mitral cells in the olfactory bulb and their projection target cortical neurons in the anterior piriform. The nature of this connectivity has been the subject of much debate in the field and these data provide a welcome contribution to the discussion. The authors employ a reverse correlation approach in which they record from single neurons in cortex while using rapidly changing light patterns to excite channelrhodopsin-expressing mitral cells in the olfactory bulb. By presenting many spatial patterns and using statistical modeling to infer back to the underlying glomerular structure of the stimulation, the authors are able to make claims about the set of olfactory bulb glomeruli whose mitral cells project to particular neurons in the cortex and to compare the distribution of these sets across cortical neurons. The data appear to be valid, albeit requiring a complex and potentially fiddly analysis, and reveal some interesting principles of functional connectivity between the bulb and anterior piriform. However, the limitations of the stimulation technique constrain the conclusions that can reasonably be drawn from these experiments.\nMajor points\n1. The general idea of using patterned light stimulation to assess the spatial relationships between bulbar glomeruli and cortical neurons is sound, but to be able to present the thousands of light stimulations required in a single mouse the authors had to largely ignore the temporal structure of olfactory stimulation. Endogenous activity of mitral cell and cortical neurons is diverse, even in anesthetized mice, including spontaneous and odor-evoked activity occurring at different phases of the respiratory cycle, responses to onsets and offsets of odor stimulation, etc. The author’s stimulations were not synchronized to the respiratory cycle (and in fact happened at a rate of multiple spatially different stimulations per inhalation-exhalation cycle), ensuring that the stimulation patterns had a chaotic temporal relationship to the population-level activity in both the target and recorded neurons. Similarly, the patterns presented were treated as independent of each other, while data from other labs (e.g. Chong et al. 2020) demonstrate that the sequential pattern of activity across glomeruli are a major driver of perceived odor quality and thus presumably determine patterns of activity in the piriform cortex. This limitation may contribute to their finding of fewer inhibitory connections than expected (e.g. median 0 in their mitral cell control experiments). While it may not have been practical for the authors to vary the relative timing of their olfactory bulb stimulation, it does unfortunately constrain the interpretation of the data. The authors seem to have performed some analyses of their respiration data to retroactively compare respiratory phase relationships among neurons (see methods line 891). A more full analysis of this temporal information and related questions (e.g. does input from an inhibitory glomerulus take more or less time to affect a piriform neuron than input from an excitatory neuron) could partially alleviate this concern and inform their discussion of indirect connections.\n2. The authors leverage their ability to selectively address glomerular inputs to answer some very fundamental questions about input summation within a given neuron. This is an underplayed strength of the manuscript that helps to place earlier data from the authors and others in an important global context.\n3. The manuscript is unclear about the number of different animals their data is drawn from, but it may be from very few individual mice. If I correctly matched the numbers in the results to those in the methods, the primary dataset included 75 cells, composed of 40 cells derived from a total of three mice recorded with tetrodes and 35 cells derived from (possibly) 14 mice recorded with single tungsten electrodes, which was then reduced to a total pool of only 50 cells after excluding the cells that didn’t yield a good fit to their statistical model of the glomerular map. The authors do not explain how many individual mice and which techniques were represented in that reduced total. In “a few” experiments recordings made from the same animal at different depths were treated as independent, without further explanation. An important follow-up experiment exploring the impact of distance between recording sites the data come from three individual mice, each reported separately. The inability for the reader to follow the data flow through the analysis certainly needs to be corrected, perhaps with a table or Sankey diagram. However, this is more than just a communication problem because the paper is trying to reach conclusions about the organization of a cortex, not just a cell. We encourage the authors to seriously consider the number of independent cortices (i.e. individual mice, not cells) needed to support the generality of their conclusions, whether their dataset still meets that need after applying their various cellular inclusion criteria, whether their statistical inferences correctly treat the independence or potential covariation of neurons from the same animal in computing p-values, whether their figures adequately communicate any animal-by-animal structure in their data, etc.\n4. The authors use the term “receptive field” to characterize the set of olfactory bulb glomeruli that drive responses in target neurons in the piriform cortex, but this is a conceptually confusing terminology. A central theme of sensory neuroscience is that the visual system represents inferences about the world and not just the structure of the retina, the auditory system infers sound sources not cochlea-space, etc. Yet by using the language of receptive fields, the authors (knowingly) write as if the piriform cortex represents the olfactory bulb glomeruli rather than the external olfactory world. Moreover, the authors are using a highly artificial stimulation method, with highly artificial timing, opening the possibility that reverse correlated mapping they observe is not what the “normal” glomerulus-to-cortex signaling of these cells would be with a realistic stimulus. I encourage the authors to use terms like “functional connectivity” rather than “receptive field” to avoid confusing the presentation of their data with this interpretational question.\nMinor points\n1. The authors should provide a more detailed description of the recording locations (e.g. depth of recording) and stimulation locations (e.g. what specific part of the bulb is exposed).\n2. The authors do not address the median of zero inhibitory connections from glomeruli onto mitral cells in the discussion.\n3. Fig. 1C could be improved by more clearly registering the datapoints for which cells have which excitatory and inhibitory connection numbers.\n4. The authors make reference in the methods to a surprising range of stimulation parameters, including 80-100 msec duration and substantial differences in the number and size of the light patches across individual animals. It was unclear from the text whether this reflected a planned active parameter search conducted live during each experiment or whether it reflected the inclusion of pilot animals collected prior to finalizing the design of the main experiment. If the latter, the authors need to fully justify their inclusion of disparate stimulation paradigms in a dataset with a modest number of subjects to begin with.\n5. The term “convergence” is a bit confusing because it is used to refer to goodness of fit but implicitly also includes a criterion of statistical stationarity across the recording period. Perhaps a more generic term like “reliably mapped” might be better?\n6. In line 851, please explain the selection of a 60 micron gap.", "reviewer_4": "Review of NCOMMS-24-66177-T, “A Topographical Map in The Primary Olfactory Cortex.” This study by Taragin and colleagues uses targeted optogenetic stimulation of olfactory bulb projection neurons to probe chemotopy in the piriform cortex. The study's main finding is that pyramidal neurons that reside in proximal space are more likely to have similar receptive fields than those distributed further in space. The study is noted for its technically demanding approach, a large number of trials per recording site, and use of naturalistic odorants to support the findings of the optogenetic experiments. However, there are several factors that limit my overall enthusiasm.\n1) From the methods, it is not clear how spikes from pyramidal neurons were isolated from local inhibitory neurons.\n2) There are concerns about anesthesia and recurrent inhibition in the piriform cortex. While it is understandable that to collect such a large number of trials per animal and for stimulation stability, anesthesia may be necessary. However, other recent studies have shown that anesthesia profoundly alters PC odorant responses (Boyd 2016, Bolding 2020). In the anesthetized state, PC neurons are generally less active. Therefore, suppressed responses may be difficult or impossible to observe. This likely biases the RF mapping towards excitatory glomeruli.\n3) The low number of inhibitory glomeruli is surprising (and perhaps related to anesthesia). Simulation with odorants often yields a large fraction of suppressed responses (Berners-Lee, 2023). It is not clear why optogenetic stimulation does not reveal these responses as well.\n4) The legends for the left panels in Figure 2A-C are not consistent. The black line corresponds to none of the glomeruli and blue to all. How can the greatest response arise from “none” being stimulated? Several display issues throughout the manuscript make the data difficult to interpret.\n5) Supplementary 1D, the filtering used for the firing rates clearly gives edge effects, which lowers confidence in the displayed data.\n6) Figure 3A, the bottom middle right colormap does not correspond to the given scale bar.\n7) Figure 3, why are the z-scored maps discretized? Only ~4 values are represented. The top right plot also seems to be misrepresented (too dim) compared to the scale bar.\n8) Figure 4, attempting to fit a linear regression to discretized values, is not an appropriate test of a relationship between variables.\n9) Figure 5, It is not reasonable to show the data that includes neurons that did not respond to any of the odorants. This induces a false correlation with neurons having flat curves. The stronger analysis is in part C-D, and this somewhat coincides with the author’s message, but the effect is less pronounced. Here, some p-values are listed as N.S., and all above 0.05 should be listed the same. Ideally, the authors will provide exact p values."}, "author_response": "To all reviewers:\nWe thank the four reviewers for taking the time to read this manuscript carefully and for\nproviding very insightful and helpful comments. We revised the manuscript according to your\nsuggestions and added several new analyses and experiments, including some additional\ninteresting findings. Below we summarize the main changes and new findings, followed by a\npoint-by-point response to each reviewer’s comments.\nSummary of main changes and additions:\n1. We conducted several additional experiments investigating the relationship between\nneurons’ location and their receptive fields (RFs) using silicon probes. This increased the\nnumber of neurons recorded with silicon probes from 116 in the original manuscript to\n413 in this revision. Overall, the revised manuscript is now based on the analysis of 951\nneurons.\n2. We conducted additional recordings of neurons spanning the anterior–posterior, lateral–\nmedial, and dorsal–lateral axes of the aPC.\n3. Recordings in the awake state are difficult because the STA method requires stable\nneurons for long durations. We made a substantial effort to collect data in awake mice,\nbut most experiments failed. Nevertheless, we successfully recorded 179 neurons (five\nsessions from three mice). A significantly smaller fraction of neurons converged to a\nstable map compared to anesthetized mice (35% versus 60%), and the maps contained far\nfewer excitatory and inhibitory glomeruli (~50% less). We suspect these results more\nlikely reflect the difficulty of applying the STA method in awake mice rather than a true\nbiological difference. We therefore decided not to report these results until a more\ncomprehensive study is conducted that overcomes these stability issues, allowing us to\nconfidently estimate the RFs of aPC neurons in awake mice.\n4. We conducted new experiments in which we recorded the aPC response to a large set of\nodors in awake and anesthetized mice and then compared the odor tuning curves with\nthe neuronal distance. We show that, as we reported in the original manuscript, in which\nwe analyzed the Iurlli & Data datasets in awake state with 13-15 odors, there is a\nsignificant correlation between neuron pair distances and their odor running curves in\nboth anesthetized and awake states (Figures 5c-d). This result substantially strengthens\nour main findings as we now show this in data we collected, and data collected in a\ndifferent lab and for both awake and anesthetized state.\n5. In three new experiments, we recorded odor responses and determined the RFs of the\nsame neurons. We then compared RF similarity with odor tuning similarity and found\nthat neurons with more similar RF maps also had more similar odor responses (Figure\n5f).\n6. We conducted an additional analysis examining the relationship between light stimulation\ntiming relative to respiration (Supplementary Figure 1.3). We found that different aPC\nneurons respond more strongly at different phases of respiration.\n7. We uncovered a potentially interesting mechanism that increases dissimilarity: some\ndistant neurons with similar RFs appear to be interconnected (Figure 4e).\n8. We improved data reporting throughout the manuscript in line with the reviewers’\nsuggestions and added analyses to strengthen both the clarity and robustness of the study.\nWe report the actual P value in the figures. We added a table that summarize the number\nof neurons recorded in each mouse in each of the different experiments (Supplementary\nTable 1).\n9. We repeated all analysis steps and carefully examined all neurons to exclude possible\nerrors. Because map estimation includes a stochastic phase, the maps are not always\nidentical but are consistently very similar to those reported in the original manuscript.\n10. We repeated all analyses using a lower threshold (0.1 instead of 0.137). This threshold\nwas chosen because it increases the number of neurons analyzed, remains stable when\nnew data are added (unlike 0.137, which was defined as ±2.1 SD of the mean), and\nsimplifies reporting. We show that the analysis is not sensitive to the threshold value (we\ntested across 0, 0.05, 0.1, 0.15, 0.2, 0.25, and 0.3; Supplementary Figure 4b).\n11. One reviewer raised a valid concern regarding the accuracy of estimating P-values when\naggregating data across mice. To address this, we now report P-values based on linear\nmixed-effects model and further verified their correctness using a Mantel test to account\nfor dependencies when comparing similarity matrices.\nThe first part of the manuscript (Figures 1–3) remained largely similar to the original\nsubmission, whereas the second part (Figures 4–5) and the Discussion have undergone\nsubstantial modifications and restructuring. We also revised the style of the manuscript to adhere\nto Nature Communications guidelines (e.g., shortening the abstract and adjusting style and\nreporting standards).\nBelow are point by point response to each of the reviewer’s comments. Our response is in cyan;\ncitations from the manuscript are in italic.\n[...]"}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have significantly improved their manuscript and strengthened their initial findings. Notably, the new data on odor responses are quite interesting (Figure 5 is great!). The number of added recorded neurons is very impressive.\nI have some suggestions in line with my initial comments :\n1) Recorded cell identity.\nThe authors have performed some additional analysis to characterise the proportion of presumed inhibitory interneurons in their data set. This information is available only in the discussion where they state (without showing any data) that “Examining the small subset of neurons that fit multiple combinations of these criteria did not reveal a significant differences between inhibitory and excitatory neurons in terms of the number of connected glomeruli.”\nCould the authors rather introduce this quantification in the beginning of the Result section to give the reader an overview of the identity of recorded neurons before we jump into the light stimulation protocol? Could they also present their data on the number of connected glomeruli in presumed interneurons versus principal cells?\nThe authors also skipped my initial question about Semilunar versus Pyramidal neurons: could they add a discussion point about the RFs of these two cell types, in light with published data (see Nagappan & Franks 2021 Elife)?\n2) RFs in awake\nThe awake data with odors are quite convincing and strengthen the conclusions of the manuscript.\nI would invite the authors to reconsider their RFs mapping data in awake. The observed decrease in convergence in the awake state is an important observation which could be related to higher M/Ts cell baseline activity (and lower evoked responses) and to the higher inhibitory tone in the aPC network. Whatever the origin of this difference, these results in the awake state don’t rule out the precise anatomical logic of OB-to-PC inputs the authors mapped in the anaesthetized state, but rather illustrate to what extent aPC neurons evoked activity is state-dependant. This result would be very informative for the reader.\n3) RFs and respiratory cycle\nThe authors provide new descriptive data about evoked responses relative to sniff cycle in SFig 1.3.\nThis descriptive analysis is interesting but don’t fully answer my initial question. If one’s takes the PSTH example in the top-right of panel a of SFig 1.3, the authors observed an increase of spike rate at the preferred phase as well at anti-phase. If you analysed separately the glomerular maps during the first or second half of the respiration cycle, are these maps similar? Could the authors just split their RF data into two data sets according to the phase of stimulation (first versus second half of the respiration cycle) and compare the obtained RFs?\nSFig1.3, Panel b : could the author extend the X axis from -1 to +1?\n4) Direct versus recurrent inputs and temporal correlation\nThe authors mentioned some cross-correlation analysis (CCF) on some pairs of neurons for Figure 4e, but the data are not shown. It would be informative to run this pairwise analysis between all pairs of neurons and evaluate to what extend the strength of cross-correlogram correlate with RF similarity. In other words, is there a relation between temporal correlated activity and receptive field/signal correlation? Miura et al. (2013 Neuron) showed that correlations of pairs of aPC neurons were independent of their odor response correlations and distance (but observed higher signal correlation in neurons from the same electrodes). The authors could now evaluate whether this hold true for neurons with similar OB-to-PC connectivity.\n5) Stability/plasticity\nThe author mentioned in the rebuttal letter an example of habituation after repeated stimulation, but this observation is not detailed in the manuscript (the word “habituation” is not mentioned in Legends, Results and Discussion). Could the author add some elements to describe this habituation process during light stimulation?\nSome typos :\nLine 129 : “1.3bWe”\nLine 390 : “connecte.”\nLine 595 : “assesing connectivity by examining”\nLine 606 : “signficant\"", "reviewer_3": "This substantially revised manuscript reports new data on the functional connectivity between olfactory bulb mitral cells and their individual target neurons in the piriform cortex. The authors use an innovative mapping technique in which a very large number of highly variable artificial optical stimulations are delivered to the olfactory bulb while recording from individual neurons in the cortex. Reverse correlation analyses are then used to capture the key spatial relationships between stimulation foci and neuronal responses. The authors use this dataset and complementary data from experiments using real odor stimuli to reach new conclusions about the organization of the bulb to piriform projection.\nThe original four reviews were notably consistent in raising concerns about the validity and interpretation of the results. Several major flaws have been corrected in this revision: the reliability of the results has now been confirmed in a much larger sample of neurons, the incorrect statistical analyses have been replaced with valid ones, the data are now presented more fully and without labeling errors, the text has been modified in many places to better communicate methods and interpretations, and several irresolvable issues (e.g. uncontrolled effects of stimulation timing) have now been explicitly analyzed to better inform the interpretation. The authors have also added a substantial new dataset using odor stimulation in awake animals to confirm that at least part of the functional organization they report using artificial stimuli obtains during more naturalistic testing, though that data is not especially convincing. The authors have thus resolved all of my concerns about the validity of the results.\nHowever, the primary limitations on the interpretations of the results cannot really be improved: the reverse correlation method can only be practically applied in anesthetized animals (despite the authors’ valiant but unsuccessful efforts in awake mice for this revision), the authors cannot adequately explore the timing of the stimulation (i.e. changing stimulus sequence or latency relative to respiration), and the authors could not provide much further illustration of the mechanisms (e.g. different cell types) or broader functions (e.g. roles in learning or neuromodulation) of the circuit organization they report. The manuscript remains of interest despite these limitations, and it reports on a unique and intriguing dataset that was doubtless very difficult to obtain.\nThe new data added by the authors purports to demonstrate that odor tuning functions are more similar for piriform cortical neurons that are close together than for those further apart. However, the actual correlations for the close together neurons are in fact quite small – less than 0.1 in Fig. 5 – and thus explain virtually none of the variance.\nThere are still concerns with the reporting of statistics, such as line 342 where a correlation of 0.33 is reported as a “substantial” correlation despite being non-significant and a few lines later where a correlation of -0.09 is described correctly but perhaps misleadingly as “highly significant” despite being very small. The authors should adopt a more consistent and matter of fact set of descriptions and make sure that every statistic has an effect size and degrees of freedom specified (when appropriate).\nThe authors have effectively addressed the gaps in their methods section and communicated the N’s for each analysis.\nThe authors do not seem to have addressed sex as a biological variable, which I believe is required by Nature Communications.\nThere are a surprising number of spelling errors throughout.", "reviewer_4": "The authors have done a commendable job addressing an extensive list of reviewer comments. Each of my previous concerns has been addressed in adequate detail. I support the publication of this technically and analytically demanding work."}, "author_response": "To all reviewers:\nWe thank the reviewers again for taking the time to offer multiple suggestions to\nimprove the manuscript further. Below, we address all the suggestions made.\nSummary of main changes:\n1) We added the awake-state recordings and compared them with the anesthetized\ndata (supplementary Figure 9).\n2) We added a new waveform analysis, which revealed interesting dynamics\n(added to Figure 4g and supplementary Figure 7).\n3) We expanded on the analysis of the relation between light stimulation and the\nrespiration phase (supplementary Figure 3).\n4) We expanded on the light-driven habituation we sometimes observed\n(supplementary Figure 8).\nWe have included a detailed response-to-reviewers document providing a point-by-point\nreply.\nThe changes in the manuscript are marked using track changes.\nOur responses are blue."}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have successfully answered to my points, congratulations for all this highly demanding work. The new data on awake state and aPC neuron subtypes are quite interesting.\nThe authors should consider updating the abstract according to these new data, notably the fact that the increased odor-tuning similarity in nearby aPC neurons tends to disappear in the awake state.\nA few typos :\n- line 710-711\n- Supp Fig 9 : \"the a Wake State\"", "reviewer_3": "The authors have responded effectively to my concerns through a combination of new analyses and textual changes. I applaud their perseverance through this process."}, "author_response": ""}]

Decision letter