PMC

A. Pairwise signal correlation (similarity in odor tuning between pairs of neurons, Pearson’s r) and noise correlations between neurons recorded from the same and different shank of the silicon probe (shanks separated by 200, 400 and 600 μm, plCoA = red, PCx = blue) for the indicated odor sets. B. Observed fraction of responsive neurons on each shank preferring one of the three odors across 5 concentrations. The across shanks slopes (see inset in C) are not statistically different from zero for each odor at all concentrations (permutation test). C. Average slope of the line fit to the distribution of responsive neurons preferring the indicated odor across shanks (see inset) for natural odor mixtures and monomolecular odorants (p>0.05, ANOVA).

Three models for the generation of innate behaviors by the plCoA in response to odors. Neurons have the tuning properties indicated by the legend on the right. The assigned behavioral meaning of neurons is indicated with external circles (appetitive = green, aversive = red). Model A and model B represent two extremes: in A, odor identity is decoded through precise and developmentally-specified hardwiring; in B, odor identity is decoded using rare labeled lines that are embedded within a broader population code for odor identity. In model C (left), the behavioral consequence of plCoA activation depends upon the balance between neurons mediating attraction and avoidace. After odor learning (C, right), however, the relative strength of the PCx to pCoA afferents are altered, causing changes in the tuning properties of the plCoA neurons. For example, now odor B elicits activity in more approach neurons than avoidance neurons, thereby changing the effective valence of odor B. This model provides an explanation for both how hardwiring from the bulb can elicit an innate behavior from what appears to be a population code, and how the plCoA could take advantage of its access to the PCx and downstream decoders to act as a switchboard, re-routing information about odors to appropriate behavioral centers in an adaptive fashion.

A. Histogram depicting the probability of an excitatory response out of ten trials (see STAR Methods). Open bars (red = plCoA, blue = PCx) depict the fraction of cell-odor pairs with a given excitatory response probability; filled bars represent only those cell-odor pairs whose responses are considered significant by auROC analysis. Black dotted lines: distribution of false-positive responses in absence of odor presentation; note that this rate reflects the level of spontaneous activity in each brain area. B. Response probabilities for cell-odor pairs with a significant excitatory response for five neutral, aversive and appetitive odors; no significant differences were observed (three factors: valence, odor identity and area, three-way ANOVA). C. (Left) Grand averages of peri-stimulus time histograms of excitatory responses to isoamyl acetate/neutral (green), TMT/aversive (red) and 2-phenylethanol/appetitive (blue). (Right) Same as (Left) with all odors considered, grouped by valence: neutral (green), aversive (red) and appetitive (blue). Odor period (black) is demarked. D. Spike count change during presentation of five neutral, aversive and appetitive odorants for those neurons that had an excitatory response. Firing rate was not significantly modulated by the innate valence of odors (three factors: valence, odor identity and area, p<0.05 only for difference between areas, three-way ANOVA).

A. Proportion of neurons activated by increasing concentrations of three odors (mean and SEM indicated, no significant changes as assessed by χ² test). B. Single neuron responses to different concentrations of three odors in plCoA and PCx; the auROC of each cell-odor pair response is depicted (color bar). C. Fraction of plCoA (red) and PCx (blue) neurons that significantly responding to distinct concentrations of the same odor. Of those neurons that are concentration variant, the fraction of plCoA and PCx neurons whose responses change monotonically or non-monotonically to increasing concentrations of an odor is also depicted. See STAR Methods for definitions of concentration variance and monotonicity. D. Fraction of responsive neurons distinguishing odor identity at each concentration in plCoA (red) and PCx (blue). E.–F. Distribution of mutual information about odor (F), concentration (G) and odor and concentration (E) in individual neurons across 3 odors and 5 concentrations in plCoA and PCx (see STAR Methods, no significant differences, Wilcoxon rank sum test). H.–J. Accuracy of linear classifiers using the indicated numbers of neurons at discriminating odor identity (H), odor concentration (I) or both (J) in plCoA (red) or PCx (blue). Dotted line indicates chance performance exhibited after odor label shuffling. K. Principal component plot of ensemble responses (limited to the first three principal components) to the three indicated odors across five concentrations (5 pseudotrials/odor, see STAR Methods).

A. auROCs of responses to 13 natural odor mixtures in plCoA (red) and PCx (blue). Responses were not distinguishable between these areas (two-way ANOVA). B. Number of natural odor mixtures that significantly activate (left) or inhibit (right) a given neuron. The excitatory and inhibitory tuning breadth of neurons were not statistically different between areas (Kolmogorov-Smirnov test). C. Probability density function of signal (left) and noise (right) correlations between individual neurons in plCoA (red) and PCx (blue) in response to natural mixtures. Correlation distribution observed after shuffling odor labels is indicated with the dashed lines. D, left. Accuracy of linear decoders trained to discriminate 13 different natural odor mixtures, with classification accuracy after odor label shuffling indicated in dashed lines. D, right. Confusion matrices of the classifier shown on the left. E. Classifier accuracy at discriminating either valence (left) or ethological class (right) of natural mixtures and controls, computed as in 4H and 4K (see STAR Methods for assignment of individual mixtures to valences or ethological classes). Shaded circles indicate the mean accuracies obtained after randomly grouping the odors in arbitrary classes; this represents chance performance in this experiment. The classification of the ethological class of an odor for population sizes of 20 – 140 neurons is just above the 97.5^th percentile of the control distribution. F. Correlation matrices of ensemble odor representations for natural odor mixtures in plCoA and PCx in response to natural odor mixtures; five pseudo-trials (average of two consecutive trials) of each odor (whose identity is indicated by a letter code, and which are in the same order as the odors in panel A) are independently depicted here to reveal cross-trial variability as well as across-odor correlations.

A. Example raster plots of odor responses (y axis = 10 trials) in plCoA and PCx. Odors were presented for 2 seconds (bar). B. Spontaneous firing rate distributions in plCoA (535 neurons, red) and PCx (339 neurons, blue; median plCoA: 0.52 spikes/sec, median PCx: 1.32 spikes/sec, p<0.001, Wilcoxon rank sum test). C. Odor response magnitude histogram (firing rate change during the first second of odor presentation) in plCoA (red) and PCx (blue); median response amplitude plCoA: 3.9 spikes/sec, median PC: 5.7 spikes/sec; p < 0.001, Wilcoxon rank sum test. Inset: Average baseline firing rates (± SEM) for neurons exhibiting significant excitatory (E) and inhibitory (I) responses in plCoA (red) and PCx (blue). D. Average response fraction of neurons in plCoA (red) or PCx (blue) exhibiting excitatory (E) or inhibitory (I) odor responses (excitatory responses: 6% in plCoA vs 11% in PCx; inhibitory responses: 3% in plCoA vs 8% in PCx; p<0.001, χ² test). E. Mean (±SEM) baseline and odor-evoked firing in plCoA (red) and PCx (blue) during the inspiratory (i) or expiratory (e) phases of the sniff cycle. F. Phase-intensity plots illustrating the distribution of the phase and firing rate of neuronal activity relative to the onset of the last respiration cycle before odor onset (baseline) and of the first cycle after onset (response). The plot angle indicates peak phase; radius indicates peak firing rate (spikes/sec, maximum is 15 Hz); color map indicates the proportion of cell-odor pairs exhibiting any given phase and rate. Insets: the mean phase of the population (arrow angle) and the concentration of the data around the mean (arrow length, where perfect concentration = 1). The mean phase in both brain areas is not uniformly distributed across the respiration cycle during odor responses (p<0.01, Raleigh’s uniformity test).

A. Mean accuracy of linear decoders trained to discriminate 15 different monomolecular odors (see STAR Methods). Dashed curves indicate performance after shuffling odor labels for all trials. B. Confusion matrices based upon the linear classifier in A reveal no systematic confusions between odors. C. Distribution of mutual information about odor identity in individual neurons for 15 monomolecular odors in plCoA and PCx (see STAR Methods, no significant differences observed using a Wilcoxon rank sum test). The number indicated on top is the information content of the population; square box indicates the mean. D. Distribution of the pairwise correlation coefficients (Pearson’s r) between ensemble neural representations (using the average response for each neuron) for 15 odors in plCoA (red) and PCx (blue). Control distributions (dashed lines) were obtained by reshuffling odor labels 500 times for each neuron. E. Classification performances obtained after sorting neurons based on their informativeness about odor identity (highest to lowest, as in C.). F. Classification performances in which populations of 90 plCoA and PCx neurons were systematically depleted of neurons in order of their informativeness; dashed lines indicate the performance of the most informative single neurons in plCoA (red) and PCx (blue). Note the sharp initial drop in accuracy in PCx was caused by removing the two highly informative neurons depicted in , followed by the equalization of the slopes of the decrementing curves. G. Classification performances obtained after sorting neurons based on their lifetime sparseness (highest to lowest). Note that the number of neurons added at each step was equal for both plCoA and PCx (total number of at each lifetime sparseness indicated within parentheses). H. Linear discriminator accuracies (as in A) of the chemical class of an odor, plotted as a function of the size of plCoA (red) and PCx (blue) populations. 15 odors were grouped in five classes based on their main chemical moiety (e.g., alcohols, aldehydes, amines, phenols, and thiazoles). Circles indicate means. Shaded circles indicate the mean accuracies obtained after randomly grouping the 15 odors in 5 arbitrary classes; this represents chance performance in this experiment. The performances for populations of 70, 80 and 90 plCoA neurons are just above the 97.5^th percentile of the controls. I. Distribution of chemical class information in individual neurons across 15 odors in plCoA and PCx, with total population information indicated at the top, and with the box indicating the mean (no significant differences, Wilcoxon rank sum test). J. Pairwise correlation between population vectors representing two odors belonging to the same chemical class or to two different chemical classes. Medians are reported and whiskers represent the interquartile range (p<0.05, t-test). K.– M. Like H. – J. but with respect to odor valence.

A. Histograms of the discriminability of olfactory responses in plCoA (red) and PCx (blue) as assessed by response auROC (in which an auROC of 1 indicates a perfectly discriminable excitatory response, and 0 indicates a perfectly discriminable inhibitory response, see STAR Methods). Filled bars: significant responses. Odor response discriminability in plCoA and PCx were similar (p>0.05, permutation test). B. auROCs of significant responses (mean ± SEM) to five neutral, aversive and appetitive odors in plCoA (red) and PCx (blue). Response discriminability was not modulated by the innate valence of odors (three factors: valence, odor identity and area, three-way ANOVA). C, left. Number of monomolecular odorants that significantly activate a given neuron (29 percent of plCoA neurons and 32 percent of PCx neurons activated by at least one odor; 14 percent of plCoA neurons and 11 percent of PCx neurons activated by only one odor out of 15). C, right. Same as C, left. but for odor suppression (16 percent of plCoA neurons and 29 percent of PCx neurons inhibited by at least one odor; 9 percent of plCoA neurons and 13 percent of PCx neurons inhibited by only one odor). The excitatory and inhibitory tuning breadth of neurons was not statistically different between areas (Kolmogorov-Smirnov test). D. Lifetime sparseness (1 = more odor selective) distributions of plCoA (red) and PCx (blue) neurons. E. Fraction of neurons activated by two or more odors of the same chemical or valence class, compared to the fraction activated by two or more odors of different classes in plCoA (red) and PCx (blue). F, left. Fraction of neurons that respond to the indicated number of chemical classes. Black dotted line: null distribution obtained by reshuffling odor labels across classes. F, right. Distributions of the number of odors to which each neuron responded in associated panel on the left. Note that most of the neurons that respond to one class of odorant respond to a single odor, suggesting they may not be “class” specific as they do not generalize across odors within a class. G. Similar to F. but with respect to odor valence (appetitive or aversive). H. Odor valence discriminability of plCoA (red) and PCx (blue) neurons; dark colored dots represent discriminability greater than expected by chance (permutation test). No differences in the significant auROCs between the plCoA and PCx were observed (permutation test). I. Probability density function of signal (left) and noise (right) correlations between neurons that responded to at least one odor in plCoA (red) and PCx (blue); signal and noise correlations observed after shuffling odor labels indicated with the dashed lines. Signal correlations were computed between all pairs of neurons from different experiments using the same odor panel of 15 monomolecular odorants. Noise correlations were computed only between neurons recorded in the same experiment. Observed distributions were not significantly different (t-test, mean signal correlation plCoA = 0.005 ± 0.003, PCx = 0.00025 ± 0.004, noise correlation plCoA = 0.02 ± 0.003, PCx = 0.04 ± 0.007). J. Correlation matrices (Pearson’s r) of plCoA (left panels) and PCx (right panels) of the tuning curves for individual neurons. Neurons are ordered via hierarchical clustering (metric: Pearson’s r, see STAR Methods) as shown by the dendrogram to the right. Bottom panels are for all neurons; top panels include only odor-responsive neurons.