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Supplementary information

Title

A spatiotemporal coding mechanism for background-invariant odor recognition

Authors

Debajit Saha, Kevin Leong, Chao Li, Steven Peterson, Gregory Siegel and Baranidharan Raman

Nature Neuroscience: doi:10.1038/nn.3570

Supplementary Figure 1: Mean ORN, PN and KC responses for all odor pairs used in this study

Supplementary Figure 1: PSTHs are shown for: (a) 2oct–hex, (b) chex–2hep, (c) bzald–iaa, (d) hxa–

hex, (e) ger–cit, (f) mint–apple. The 4 s odor stimulation period is shown using a gray bar along the x-

axis. For all odors used, three dynamical states can be clearly identified: an on-transient response

following odor onset, an off-transient response after stimulus termination, and a steady-state between the

two transient activity phases. n represents the number of neurons recorded. (g) The diverse set of

background–foreground odor combinations chosen for the study is listed here. Diversity assessed based

on functional groups, electroantennogram responses, vapor pressures, and complexity of the odorants

(mono-molecular versus complex blend).


Supplementary Figure 2: Electroantennogram (EAG) responses to the chosen odor pairs

Supplementary Figure 2: EAG recordings obtained from four locusts are shown for all six background–

foreground odor pairs. Raw EAG signals (mean ± s.d.) obtained from one locust are shown on the left

panels. Right panels reveal the distribution of peak EAG response amplitude to the background and

foreground odors across different locusts to illustrate response consistency. Three groups of odor pairs

can be easily identified based on their relative EAG response profiles: (a) odor pairs where the foreground

odor has stronger EAG response: 2oct–hex and hxa–hex (b) odor pairs where the background odor has

stronger EAG response: bzald–iaa and ger–cit, and (c) odor pairs with comparable EAG responses: chex–

2hep and mint–apple. (* P < 0.05; paired t-test, n = four trials).


Supplementary Figure 3: Visualization of ensemble PN responses using linear principal component

analysis (PCA)

Supplementary Figure 3: PCA trajectories to all six odor pairs are shown. Same convention as that used

in Fig. 4. The same sets of PNs used in Fig. 4 were used for generating the PCA plots.


Supplementary Figure 4: LLE plots showing PN ensemble response trajectories for 3 additional

overlapping conditions

Supplementary Figure 4: LLE plots showing PN ensemble response trajectories for 3 additional

overlapping conditions. The three new presentation conditions include: background–500 ms latency–

foreground, background–1000 ms latency–foreground, and background offset–500 ms latency–

foreground. n = number of PNs recorded for each odor pair.


Supplementary Figure 5: Significance of PN classification results

Supplementary Figure 5: (a) Percentage of time bins during pre-stimulus periods that were classified is

less than 3% for all odor pairs. (b) Histograms revealing the distribution of angular distances between

individual test patterns and their closest reference templates are shown. Same coloring scheme as used in

panel a. Only vectors exceeding standard deviation test were included in this analysis. Black bars

represent angular distance greater than 85°. The mean angular distance was between 68.65°–71.79°. (c)

Classification of random vectors using reference vector templates obtained for each odor pair. Less than

5% of the random vectors were within the tolerance limit by chance. All other vectors exceeded the

tolerance threshold and were not classified into any odor category.


Supplementary Figure 6: Average KC PSTHs for all odor pairs and for different overlapping conditions

Supplementary Figure 6: Average KC PSTHs for all odor pairs and for different overlapping conditions.

For each condition, firing rates were calculated over 100 ms non-overlapping time bins, averaged over ten

trials and smoothed using a 3-point running average. For each odor pair, the plot follows the stimulus

protocol scheme shown in Fig. 1b. n denotes the total number of KCs recorded for each odor pair. Max

indicates the maximum firing rate observed.


Supplementary Figure 7: Locust retention tests and T-maze assay

Supplementary Figure 7: (a) A schematic of locust palp opening response (POR). Dotted red line

indicates the POR threshold used to determine a response. One or both maxillary palps have to cross this

detection threshold at least once during the odor presentation period to be counted as positive response.

(b) PORs for four consecutive blocks of unrewarded test trials are shown. Each block consisted of two

test trials: presentations of the CST (iaa) and an untrained odor (bzald). Test trials started 10 min after the

last training trial. A 10 min delay was maintained between test trials in a single block, and a 30 min delay

was observed between consecutive blocks of test trials. Conditioned locusts had a significantly higher

POR to the trained odor (iaa) during all four test trials (**P = 1.22×10–4

, 6.10×10–5

, 7.63×10–5

, 3.05×10–5

;

McNemar’s exact test, n = 28 locusts). The frequency of POR observed for trained and untrained odor

remained consistent across the four consecutive test blocks (Cochran’s Q test; for CST: Q = 0.67, df = 3,

P = 0.87; for untrained odor: Q = 2.2, df = 3, P = 0.53). (c) The bar graph summarizes responses of

locusts to a trained odor (CST – cit) and an untrained odor (ger). The POR to citral was low indicating

that effective association between CST and unconditioned stimulus was not achieved (P = 0.50;


McNemar’s exact test, n = 26 locusts). n denotes the number of locusts used in the training set. (d,e) Bar

graphs summarizing conditional POR probability in those locusts that responded only to the CST are

shown (*P = 0.0351; NS indicates not significant, P > 0.05; McNemar’s exact test with Bonferroni

correction for multiple comparisons, n = 25 locusts for 2oct–hex, n = 27 locusts for bzald–iaa). (f) A

schematic of the T-maze assay is shown. Locusts were restrained in a custom-designed holder and

released just before the odor delivery. A test odor and the control (mineral oil) were simultaneously

presented at the two odor delivery ports. An exhaust fan at the center of the maze ensured that there was a

stable airflow inside the maze (flow patterns were confirmed with titanium tetrachloride). Each locust

was given 4 min to make a decision: i.e. select a T-maze arm, reach and touch the sidewall at the end of

the selected arm with its leg or antenna.


Supplementary Figure 8: Qualitatively similar results obtained from analysis of ensemble neural

activity recorded from single locusts

Supplementary Figure 8: Classification analysis using PN responses obtained from a single locust is

shown. Results for all six different odor pairs are arranged as in Fig. 5b–g. n denotes the number of PNs

recorded from both antennal lobes of the locust. Note that for each odor pair a different locust was used.


Supplementary Figure 9: Proposed attractor model of olfactory coding

Supplementary Figure 9: (a) Each odor is encoded by a ‘sub-space’ or an ‘attractor’ in the state-space

that encodes for its identity. Blue and red curves represent response trajectories evoked by pure odor

presentations. The entire neural response dynamics during odor presentation (i.e. on-transient and steady-

state activities) strive to keep the response within the attractor for the particular odor. Note that the

steady-state activities are closer to the baseline response but are still odor-specific and aligned with the

on-transient response. When a foreground odor is presented following a preceding stimulus, a strong

excitatory input is required to overcome the inhibition offered by the on-going activity, and switch the

system’s response to the foreground odor attractor as shown by the dynamic trajectory in black. B denotes

the baseline response where the neural activity eventually returns. (b) Percentage of PNs with excitatory

or inhibitory responses to each background and foreground odor is shown (see Online Methods for

details regarding the criteria used for this categorization). The height of the bar plot above and below the

horizontal axis (‘zero percentage’) indicates the fraction of excitatory and inhibitory PNs respectively. As

can be noted amongst all foreground odors (red bars), citral evoked the least excitatory and most

inhibitory response. n represents the total number of PNs in each set (same as in Fig. 4).


Supplementary Figure 10: Examples of ORN, PN and KC spike-sorting

Supplementary Figure 10: (a) An example of ORN recording and spike-sorting. (Left panel) Raw

extracellular trace showing response of a single ORN. (Middle panel) Individual ORN spike events

(black) and their mean (red). (Right panel) Inter-spike interval distribution for the identified ORN. (b)

An example of PN spike-sorting. (Left panel) Extracellular waveforms from four independent channels

of a tetrode are shown for all spiking events corresponding to two simultaneously recorded PNs.

Individual events (black), mean (red), and s.d. (blue) are shown for both cells. (Right panel – top)

Histograms obtained by projecting high-dimensional PN event representations (180 dimensional vector

obtained by concatenating signals from all electrodes) onto the line connecting their means. To be

considered a well-isolated unit, as in this case, a bimodal distribution with cluster centers separated by at

least five times the noise s.d. is expected for every pair of simultaneously recorded cells. (Right panel –

bottom) Distributions of inter-spike intervals are shown for these two PNs. (c) Similar plot showing an

example for KC spike-sorting.


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