Article Figures & Data
Figures
- Figure 1.
Mouse odor-navigation task. A, Flow chamber used to conduct behavioral assay. Chamber is flanked by two honeycombs and on the inlet side, a turbulence grid 10 cm in front of the honeycomb. Three odor ports and lick spouts are spaced along inlet side and vacuum is used to establish air flow (5 cm/s). B, Mouse is rewarded for navigating to the port releasing odor (port two) and trial is terminated early if animal navigates to incorrect port (left). Trial structure includes a 30-s period to establish plume before animal enters chamber and given 45 s to navigate (right). C, miniPID readings of odor concentration from odor ports 1 and 2 (time averaged and normalized to maximum reading which occurs at the odor source). D, Performance (% successful trials in a given session) of mice over testing days. Performance is broken up into an early phase (first 7 d) and a late phase (last 7 d). Plot shows mean performance ± SEM, n = 4 mice. E, Percentage of time spent hugging the chamber wall, defined as within 5 cm of behavioral arena wall, over testing days. Plot shows mean % time spent wall hugging ± SEM, n = 4 mice. See also Extended Data Figure 1-1. *p < 0.05.
- Figure 2.
Mice change navigation behavior with increased experience and odor environment complexity. A, Performance (average % successful trials over sessions) across testing phases. Mice are tested on a no-odor condition in addition to the phases with a honeycomb and condition without a honeycomb. Chance level performance is 25% as animals have three ports as options and are not required to choose an odor port on trials. B, Pathlength to target odor port on successful trials. C, Time to target odor port on successful trials. D, Time to target on successful trials over testing days. E, Example traces of successful navigation from the late phase and no honeycomb phase. Traces are color scaled based on velocity. F, Total angle sum of trajectories of late phase and no honeycomb condition. Total angle sum is calculated by using the total sum of angles on turns from frame-to-frame. G, Velocity on successful trials of late phase and honeycomb condition (left). Velocity over the course of successful trajectories resampled to 675 frames (right). H, Change in nose angle per frame (15 Hz) over the course of successful trajectories resampled to 675 frames (left). Change in nose angle on successful trials of late phase and no honeycomb condition (right). I, Ratio of path distance based on nose to path distance based on center of body (left). Example trajectories with ratios of 1.35 (top) and 1.08 (bottom). All plots show mean ± SEM, n = 4 mice. See also Extended Data Figure 2-1. *p < 0.05, **p < 0.01.
- Figure 3.
In silico models show decreased performance with increased odor environment complexity. A, Model virtual chassis moves through space with a heading, θ. Two sensors are separated at a distance ℓs and an angle γ (left). If the center of the model reaches dthreshold = 10 cm of the wall, the model will take corrective measures (right; described in Materials and Methods). B, Model is tested at angles ranging from 90° to 270° with a start position in the center of the arena. Model is tested on two plumes, one originating from a center port and one from a corner port. C, Sample frame depicting instantaneous concentration of the dynamic plume normalized to odor source (left), and an image of the stationary concentration gradient in static plume normalized to odor source (right). D, Performance (average % success of all start angles ± SEM) across code and sensor distance for center target port (left) and corner target port (right); n = 20 simulations. E, Linearity score (calculated as the ratio of the Euclidean distance between start point and end point of trajectory and the actual pathlength) across code and sensor distance for center target port (left) and corner target port (right). Plot shows mean linearity score ± SEM, n = 20 simulations. See also Extended Data Figures 3-1, 3-2, 3-3, 3-4, 3-5, and 3-6. *p < 0.05, **p < 0.01, ***p < 0.001.
- Figure 4.
Arduino-based robot navigation varies based on start position and odor environment complexity. A, Robot odor navigation flow chamber, modifications to the SOL. Solid arrows represent five starting angles. Odor ports were coupled to LED lights detected by sensors on the robot (indicated by dotted red arrows). B, Performance (average % successful trials over 8 and 16 cm and 0° and 45° gas sensor distance and angles, respectively) across codes (left). Performance based on gas sensor distance and angle for the honeycomb condition (right). C, Example trajectories from 180° (magenta) starting position in A for honeycomb and no honeycomb condition. D, Performance (average % successful trials over 8 and 16 cm and 0° and 45° gas sensor distance and angles, respectively) with the honeycomb based on starting angle and rewarded port for Code A (left) and Code B (right). Bars are color coded and labeled according to the starting angles in A. E, Robot overall linearity score with honeycomb and without honeycomb using Code B. Plot shows data combined over sensor angle and sensor distance for each odor environment condition (left). Linearity score across starting angles and target ports with and without the honeycomb. All plots show mean ± SEM, n = 4 sessions. See also Extended Data Figure 4-1. *p < 0.05, **p < 0.01.
- Figure 5.
Mouse, robot, and in silico navigation trajectories. A, Mouse trajectories show consistency with increased odor environment complexity. B, Robot trajectories show decreased success on trials for the same testing conditions with increased odor plume complexity, Code B, sensor distance: 8 cm, sensor angle: 0°. C, In silico trajectories (50 trials with start angles ranging from 90° to 270°) show increased unsuccessful trials for the same testing conditions with increased complexity, Code B, sensor distance: 8 cm. See also Extended Data Figure 5-1.
Tables
Location Data structure Statistical test 95% confidence Intervals a Paired % time spent wall-hugging (late phase vs early phase), n = 4 mice Paired one-tailed t test –35.91 to –18.15 b Paired % success (late phase vs early phase), n = 4 mice Paired one-tailed t test –1.79 to –21.51 c Paired % success (no honeycomb condition vs late phase), n = 4 mice Paired two-tailed t test –10.64 to 6.81 d % success for honeycomb and no honeycomb conditions per odor port Two-way ANOVA on % success (factors: port #, plume complexity) Bonferroni correction:
–3.8 to 56.2e % success for honeycomb and no honeycomb conditions per odor port Two-way ANOVA on % success (factors: port #, plume complexity) Bonferroni correction:
–1.65 to 58.35f % success for honeycomb and no honeycomb conditions per odor port Two-way ANOVA on % success (factors: port #, plume complexity) Bonferroni correction:
–27.85 to 32.15g Paired % success (no odor vs late phase), n = 4 mice Paired one-tailed t test –51.18 to –11.46 h Paired % success (no odor vs no honeycomb condition), n = 4 mice Paired one-tailed t test –46.02 to –12.78 i Paired distance to odor source on successful trials (late phase vs early phase) Paired two-tailed t test –114.2 to –7.34 j Paired time to odor source on successful trials (late phase vs early phase) Paired two-tailed t test –6.92 to –2.28 k Paired distance to odor source on successful trials (no honeycomb vs late phase) Paired two-tailed t test –25.94 to 18.91 l Paired time to odor source on successful trials (no honeycomb vs late phase) Paired two-tailed t test –25.94 to 18.91 m Paired average velocity during trial (no honeycomb vs late phase) Paired two-tailed t test 0.49 to 15.59 n Paired average angle sum during trial (no honeycomb vs late phase) Paired two-tailed t test –69.8 to 15.41 o Paired average Δ nose angle (no honeycomb vs late phase) Paired two-tailed t test 0.008 to 0.12 p Average nose/body distance ratio (late phase) One-sample two-tailed t test 1.13 to 1.15 q Average nose/ body distance ratio (no honeycomb) One-sample two-tailed t test 1.14 to 1.26 r % success for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on % success (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
5.18 to 11.56s % success for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on % success (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
1.47 to 6.36t Linearity for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on linearity (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
0.044 to 0.086u Linearity for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on linearity (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
0.013 to 0.033v % success for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on % success (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
16.92 to 23.3w % success for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on % success (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
0.51 to 6.88x % success for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on % success (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
3.1 to 7.99y Linearity for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on linearity (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
0.13 to 0.17z Linearity for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on linearity (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
0.01 to 0.05aa Linearity for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on linearity (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
0.03 to 0.05bb % success for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on % success (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
–16.23 to –9.86cc % success for static and dynamic across Code A and Code B, sensor distance 8 and 16 cm Three-way ANOVA on % success (factors: plume complexity code, and sensor separation distance) Bonferroni correction:
–4.49 to 1.88dd % success for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on % success (factors: plume complexity and modality) Bonferroni correction:
–46.6 to –10.68ee % success for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on % success (factors: plume complexity and modality) Bonferroni correction:
–46.07 to –10.15ff % success for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on % success (factors: plume complexity and modality) Bonferroni correction:
–42.8 to –6.87gg % success for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on % success (factors: plume complexity and modality) Bonferroni correction:
–37.19 to –1.24hh Time to target for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on time to target (factors: plume complexity and modality) Bonferroni correction:
–44.17 to –23.34ii Time to target for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on time to target (factors: plume complexity and modality) Bonferroni correction:
–47.01 to –26.18jj Time to target for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on time to target (factors: plume complexity and modality) Bonferroni correction:
–45.67 to –24.84kk Time to target for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on time to target (factors: plume complexity and modality) Bonferroni correction:
–49.43 to –28.18ll Paired % success (no honeycomb condition vs honeycomb Code A), n = 4 sessions Paired two-tailed t test –97.78 to –27.22 mm Paired % success (no honeycomb condition vs honeycomb Code B), n = 4 sessions Paired two-tailed t test –27.38 to –11.91 nn Paired % success (no honeycomb condition vs honeycomb Code B), n = 4 sessions Paired two-tailed t test –67.52 to –27.48 oo % success for honeycomb condition per start angle One-way ANOVA (factor: start angle) Bonferroni correction:
24.45 to 125.5pp % success for honeycomb condition per start angle One-way ANOVA (factor: start angle) Bonferroni correction:
–6.11 to 116.1qq % success for honeycomb condition per start angle One-way ANOVA (factor: start angle) Bonferroni correction:
11.79 to 133.2rr % success for honeycomb condition per start angle One-way ANOVA (factor: start angle) Bonferroni correction:
–19.37 to 114.4ss Linearity for honeycomb and no honeycomb using Code B across start angle Two-way ANOVA (factors: plume complexity start angle) Bonferroni correction:
0.051 to 0.29tt Linearity for honeycomb and no honeycomb using Code B across start angle Two-way ANOVA (factors: plume complexity start angle) Bonferroni correction:
0.047 to 0.32uu Linearity score for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on linearity score (factors: plume complexity and modality) Bonferroni correction:
0.014 to 0.42vv Linearity score for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on linearity score (factors: plume complexity and modality) Bonferroni correction:
0.046 to 0.45ww % success for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on % success (factors: plume complexity and modality) Bonferroni correction:
–36.2 to 14.06xx % success for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on % success (factors: plume complexity and modality) Bonferroni correction:
–48.87 to 1.39yy Time to target for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on time to target (factors: plume complexity and modality) Bonferroni correction:
–46.91 to –26.07zz Time to target for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on time to target (factors: plume complexity and modality) Bonferroni correction:
–51.97 to –31.13aaa Velocity for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on time to target (factors: plume complexity and modality) Bonferroni correction:
16.77 to 25.09bbb Velocity for low complexity and high complexity across modalities (mouse, model Code A, model Code B, and robot Code B) Two-way ANOVA on time to target (factors: plume complexity and modality) Bonferroni correction:
24.9 to 33.22
Movies
- Movie 1.
In silico dynamic plume released from corner port. Video played at 10 Hz (first 10 s shown).
- Movie 2.
In silico dynamic plume released from center port. Video played at 10 Hz (first 10 s shown).
- Movie 3.
Mouse navigation to airborne odor source. In first trial animal, odor port 3 is releasing odor. In second trial, odor port 2 is releasing odor. Video recorded and played back at 15 Hz.
- Movie 4.
In silico model navigation of static odor plume released from corner odor port using Code A. Video recorded at 10 Hz and played back at 60 Hz.
- Movie 5.
In silico model navigation of static odor plume released from center odor port using Code A. Video recorded at 10 Hz and played back at 60 Hz.
- Movie 6.
In silico model navigation of dynamic odor plume released from corner odor port using Code A. Video recorded at 10 Hz and played back at 60 Hz.
- Movie 7.
In silico model navigation of dynamic odor plume released from center odor port using Code A. Video recorded at 10 Hz and played back at 60 Hz.
- Movie 8.
In silico model navigation of static odor plume released from corner odor port using code B. Video recorded at 10 Hz and played back at 60 Hz.
- Movie 9.
In silico model navigation of static odor plume released from center odor port using code B. Video recorded at 10 Hz and played back at 60 Hz.
- Movie 10.
In silico model navigation of dynamic odor plume released from corner odor port using code B. Video recorded at 10 Hz and played back at 60 Hz.
- Movie 11.
In silico model navigation of dynamic odor plume released from center odor port using code B. Video recorded at 10 Hz and played back at 60 Hz.
- Movie 12.
Arduino robot navigation to airborne odor source with honeycomb using Code A with sensors at angle 0° and distance 8 cm. Odor source is middle port (port 2) and start angle is indicated in lower left corner (135°, 190°, and 225°). Video recorded at 30 Hz and played back at 90 Hz.
- Movie 13.
Arduino robot navigation to airborne odor source with honeycomb using Code A with sensors at angle 45° and distance 8 cm. Odor source is middle port (port 2) and start angle is indicated in lower left corner (135°, 190°, and 225°). Video recorded at 30 Hz and played back at 90 Hz.
- Movie 14.
Arduino robot navigation to airborne odor source with honeycomb using Code A with sensors at angle 0° and distance 16 cm. Odor source is middle port (port 2) and start angle is indicated in lower left corner (135°, 190°, and 225°). Video recorded at 30 Hz and played back at 90 Hz.
- Movie 15.
Arduino robot navigation to airborne odor source with honeycomb using Code A with sensors at angle 45° and distance 16 cm. Odor source is middle port (port 2) and start angle is indicated in lower left corner (135°, 190°, and 225°). Video recorded at 30 Hz and played back at 90 Hz.
- Movie 16.
Arduino robot navigation to airborne odor source using code B with sensors at angle 0° and distance 8 cm. Odor source is middle port (port 2), start angle is indicated in lower left corner (135°, 190°, and 225°), condition indicated in lower left corner (honeycomb and no honeycomb). Video recorded at 30 Hz and played back at 90 Hz.
- Movie 17.
Arduino robot navigation to airborne odor source using code B with sensors at angle 45° and distance 8 cm. Odor source is middle port (port 2), start angle is indicated in lower left corner (135°, 190°, and 225°), condition indicated in lower left corner (honeycomb and no honeycomb). Video recorded at 30 Hz and played back at 90 Hz.
- Movie 18.
Arduino robot navigation to airborne odor source using code B with sensors at angle 0° and distance 16 cm. Odor source is middle port (port 2), start angle is indicated in lower left corner (135°, 190°, and 225°), condition indicated in lower left corner (honeycomb and no honeycomb). Video recorded at 30 Hz and played back at 90 Hz.
- Movie 19.
Arduino robot navigation to airborne odor source using code B with sensors at angle 45° and distance 16 cm. Odor source is middle port (port 2), start angle is indicated in lower left corner (135°, 190°, and 225°), condition indicated in lower left corner (honeycomb and no honeycomb). Video recorded at 30 Hz and played back at 90 Hz.
Extended Data Figure 1-1
Odor plume within the SOL with and without honeycomb. A, Odor plume properties within the SOL with and without the inlet air laminarization honeycomb at 10, 30, 50, and 60 cm downstream from odor tube. The average miniPID reading at 50 cm from the odor tube is greater without the honeycomb when compared to with the honeycomb (one-tailed t test with correction for multiple comparisons, average with honeycomb 0.16 ± 0.04, average no honeycomb: 0.36 ± 0.06, p = 0.040). The SD of the PID reading at all distances from the outlet is greater without the honeycomb than with the honeycomb (one-tailed t test with correction for multiple comparisons, 60-cm std with honeycomb: 0.09 ± 0.02, 60-cm std no honeycomb: 0.18 ± 0.02, p = 0.014; 50-cm std with honeycomb: 0.10 ± 0.02, 50-cm std no honeycomb: 0.18 ± 0.01, p = 0.004; 30-cm std with honeycomb: 0.11 ± 0.01, 30-cm no honeycomb: 0.25 ± 0.02, p < 0.0001; 10-cm with honeycomb: 0.08 ± 0.03, 10-cm no honeycomb: 0.32 ± 0.01, p < 0.0001). The std/average is greater without the honeycomb than with the honeycomb at 30 and 10 cm from the odor tube (one-tailed t test with correction for multiple comparisons, 30-cm std/average with honeycomb: 0.29 ± 0.04, 30-cm std/average no honeycomb: 0.57 ± 0.09, p = 0.033; 10-cm std/average with honeycomb: 0.08 ± 0.02, 10-cm std/average no honeycomb: 0.32 ± 0.04, p < 0.0001). B, Example PID readings for honeycomb and no honeycomb conditions from 2-min sample at 60 cm from the source. C, Example PID readings for honeycomb and no honeycomb conditions from 2-min sample at 30 cm from the source. Download Figure 1-1, TIF file.
Extended Data 1
In silico MATLAB and Arduino codes. Included are MATLAB codes to generate the center and corner odor plumes (file names: odorFun_plume_center.m, odorFun_plume_corner.m), test the in silico-simulated robot using Code A and Code B (filenames: SimRobot_test_A.m, SimRobot_test_B.m), and to test the in silico model with replicates (filenames: run_model_A_replicates.m, run_model_B_replicates.m). Additionally, the two Arduino codes for robot navigation (file names: Robot_CodeA.ino, Robot_CodeB.ino). Download Extended Data 1, ZIP file.
Extended Data Figure 2-1
Mice show consistent performance and turning behavior across both low-complexity and high-complexity odor environments. A, % success of mouse navigation at each target odor port in the late phase and no honeycomb conditions. B, Same as A, for total angle sum. C, Same as A, for linearity score. All plots show mean ± SEM, n = 4 mice. Download Figure 2-1, TIF file.
Extended Data Figure 3-1
Instantaneous concentration for in silico algorithm Code A at center start position over trajectories resampled to 755 frames. Each trajectory was resampled to 755 frames (the maximum amount of time the model was allotted) and averaged across starting angle (y-axis). Twenty simulations per starting angle were tested. Concentration shown with color scale. For first ∼275 samples, the model is stationary due to collecting baseline data, thus the odor concentration does shows little variation during this sampling period. Data are grouped by left and right sensor reading, tested odor plume (static or dynamic), and sensor separation distance (8 and 16 cm). For each condition, the average concentration at each starting angle is plotted as well as the SD of concentration on these trajectories. Download Figure 3-1, TIF file.
Extended Data Figure 3-2
Instantaneous concentration for in silico algorithm Code A at corner start position over trajectories resampled to 755 frames. Each trajectory was resampled to 755 frames (the maximum amount of time the model was allotted) and averaged across starting angle (y-axis). Twenty simulations per starting angle were tested. Concentration shown with color scale. For first ∼275 samples, the model is stationary due to collecting baseline data, thus the odor concentration does shows little variation during this sampling period. Data are grouped by left and right sensor reading, tested odor plume (static or dynamic), and sensor separation distance (8 and 16 cm). For each condition, the average concentration at each starting angle is plotted as well as the SD of concentration on these trajectories. Download Figure 3-2, TIF file.
Extended Data Figure 3-3
Instantaneous concentration for in silico algorithm Code B at center start position over trajectories resampled to 755 frames. Each trajectory was resampled to 755 frames (the maximum amount of time the model was allotted) and averaged across starting angle (y-axis). Twenty simulations per starting angle were tested. Concentration shown with color scale. For first ∼275 samples, the model is stationary due to collecting baseline data, thus the odor concentration does shows little variation during this sampling period. Data are grouped by left and right sensor reading, tested odor plume (static or dynamic), and sensor separation distance (8 and 16 cm). For each condition, the average concentration at each starting angle is plotted as well as the SD of concentration on these trajectories. Download Figure 3-3, TIF file.
Extended Data Figure 3-4
Instantaneous concentration for in silico algorithm Code B at corner start position over trajectories resampled to 755 frames. Each trajectory was resampled to 755 frames (the maximum amount of time the model was allotted) and averaged across starting angle (y-axis). Twenty simulations per starting angle were tested. Concentration shown with color scale. For first ∼275 samples, the model is stationary due to collecting baseline data, thus the odor concentration does shows little variation during this sampling period. Data are grouped by left and right sensor reading, tested odor plume (static or dynamic), and sensor separation distance (8 and 16 cm). For each condition, the average concentration at each starting angle is plotted as well as the SD of concentration on these trajectories. Download Figure 3-4, TIF file.
Extended Data Figure 3-5
Schematics of model trajectories through odor plume. A, Example model trajectory through center odor plume. Striated patterning seen in Extended Data Figures 3-1, Figures 3-4 is due to robot rotating, causing sensors to rotate in and out of the odor plume. Striated patterning is more obvious at 16-cm sensor separation distance due to sensors being wider apart and therefore detecting odor environments with greater concentration differences. Additionally, striated patterning is less obvious in the dynamic plume because the plume is dynamic and the paths are not deterministic, so averages across trials will show a smoother gradient of concentration over trial time. B, Example model trajectory through corner odor plume. Model begins out of the odor plume, and therefore, the first several frames in Extended Data Figure 3-3, Figure 3-4 show a very low concentration. Again, striated patterning is more obvious at 16-cm sensor separation distance and less obvious in the dynamic plume condition. Download Figure 3-5, TIF file.
Extended Data Figure 3-6
Navigation performance and trajectory linearity across start angles. A, % success (mean performance of one simulation with all start angles tested) and linearity score with static and dynamic plume using binaral model (Code A) and temporal-based binaral model (Code B) across starting angles with a sensor separation distance of 8 cm. Graphs are grouped target port location (either center port or corner port). Plots show mean % success ± SEM or mean linearity score ± SEM; n = 20 simulations, Code A shown in red, Code B shown in blue. B, Same as A, for a sensor separation distance of 16 cm. Download Figure 3-6, TIF file.
Extended Data Figure 4-1
Increased odor plume complexity impairs Arduino-based robot navigation from alternate starting position. A, Top and side view of robot with three proximity, two VOC gas sensors with fans, and an LED sensor. B, C, Normalized odor concentration reading after brief ethanol exposure over time with an original sensor powered at 5 V (1.25 W per sensor), a modified sensor with fan at 6.5 V (2 W) without driving the fan, and a modified sensor with fan at 6.5 V and driving the fan using 3 V (0.15 W). t50 on P: rise time from t50 (time at 50% of peak amplitude) to tp (peak amplitude). t50 off: decay time from to tp to t50. t25 on P: rise time from t25 (25% of peak amplitude) to tp (peak amplitude). t25 off: decay time from to tp to t25. t75 on O: rise time from response onset (2% of peak amplitude) to t75 (75% of peak amplitude). t100 on O: rise time from response onset (2% of peak amplitude) to t100 (peak amplitude). D, Robot odor navigation flow chamber. Red arrow labeled “start” indicates the alternate starting position and the red asterisk indicates the active odor port. E, Performance (average % successful trials over 8 and 16 cm and 0° and 45° gas sensor distance and angles, respectively) across codes with and without honeycomb. Plot shows mean % success ± SEM, n = 4 sessions (left). Performance based on gas sensor distance (8 and 16 cm) and angle (0° and 45°) for the honeycomb and no honeycomb conditions (right). Download Figure 4-1, TIF file.
Extended Data Figure 5-1
Comparison of navigation parameters across modalities. A, Performance (calculated as % success during a session) in mouse, robot using Code B, model using Code A, and model using Code B in low-complexity and high-complexity SOL (left). Performance of the robot and the model using Code B, both including only start angles tested on robot [90° and 135° for port 1 (corner port); 135°, 180°, and 225° for port 2 (center port)]. Each data point in this plot represents trials per combination of sensor distance (8 and 16 cm) and target odor port (port 1 and port 2 for robot, corner and center for model, right). B, Same as A using time to target on successful trials. C, Same as A using velocity. D, Same as A using linearity score. All plots show mean ± SEM, n = 4 mice, n = 4 sessions for robot (one session per combination of sensor distance and sensor angle), n = 4 sessions for each model condition (one session for per combination of sensor distance and target odor port). Download Figure 5-1, TIF file.
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