Decoding locomotion from population neural activity in moving C. elegans
- Department of Physics, Princeton University, United States
- Princeton Neuroscience Institute, Princeton University, United States
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, United States
Figures
Figure 1 with 3 supplements
Population calcium activity and tuning of select neurons during spontaneous animal movement.
Recording AML310_A. (a) Calcium activity of 134 neurons is simultaneously recorded during locomotion. Activity is displayed as motion-corrected fluorescent intensity . Neurons are numbered …
Figure 1—figure supplement 1
Additional details and examples of velocity tuning.
Additional examples of velocity tuning curves for (top) and (bottom) from recording AML310_A are shown. The correlation coefficient ρ captures the relation between each neuron’s activity and …
Figure 1—figure supplement 2
Additional details and examples of curvature tuning.
Additional examples of curvature tuning for (top) and (bottom) from recording AML310_A are shown. The correlation coefficient ρ captures the relation between each neuron’s activity and …
Figure 1—figure supplement 3
Number of significantly tuned neurons across recordings.
Pearson’s correlation coefficient ρ was calculated for each neuron in 11 GCaMP recordings and 11 GFP control recordings that lacked a calcium indicator. Neurons were counted as significantly tuned …
Figure 2 with 1 supplement
Neuron pair AVA is active during backward locomotion and exhibits expected tuning during moving population recordings.
(a) AVAR and AVAL are labeled by BFP under a rig-3 promoter in strain AML310. Two optical planes are shown from a single volume recorded during movement. Planes are near the top and bottom of the …
Figure 2—figure supplement 1
Sum of AVAL and AVAR activity.
Activity of AVAL and AVAR from AML310_A in Figure 2b are shown summed together. This permits comparison to recordings that do not resolve the two neurons separately.
Figure 3 with 5 supplements
Population neural activity decodes locomotion.
(a–d) Performance of the best single neuron (BSN) is compared to a linear population model in decoding velocity and body curvature for the exemplar recording AML310_A shown in Figure 1. (a) …
Figure 3—figure supplement 1
Performance correlates with maximal GCaMP Fano Factor, a metric of signal.
Decoding performance is plotted against maximal GCaMP Fano Factor for each recording for velocity and curvature. Maximal GCaMP Fano Factor is the Fano Factor of the raw GCaMP activity for the neuron …
Figure 3—figure supplement 2
Neural activity and behavior for all moving calcium imaging recordings (AML310 and AML32).
Figure 3—figure supplement 3
Neural activity and behavior for all moving GFP control recordings (AML18).
Neural activity and behavior for all moving GFP control recordings (AML18).
Figure 3—figure supplement 4
Alternative population models.
Performance of alternative population models for decoding velocity. Traces are shown for exemplar recording AML310_A. Mean across all moving GCaMP recordings is also listed. Gray shading shows …
Figure 3—figure supplement 5
Nonlinear fits using best single neuron.
Performance of polynomial regression models for decoding velocity on 11 GCaMP recordings using the best single neuron. The best single neuron is defined as the one with the best decoding performance …
Figure 4
Example where population decoded a fuller range of animal behavior.
(a) The decoding from the best single neuron and the population model are compared to the measured velocity for example recording AML32_A. (b) Predictions from the best single neuron saturate at a …
Figure 5 with 3 supplements
Weights assigned to neurons by the population model in the exemplar recording, and their respective tuning.
(a) The weight assigned to each neuron’s activity () or its temporal derivative () by the velocity population decoder is plotted against its Pearson’s Correlation coefficient ρ which …
Figure 5—figure supplement 1
Comparison of weights assigned to a neuron’s activity versus its temporal derivative.
Comparison of weights assigned to a neuron’s activity versus its temporal derivative for velocity (left) or curvature (right) decoders. Comparison of weights assigned to a neuron’s activity by …
Figure 5—figure supplement 2
Comparison of weights assigned for decoding velocity vs decoding curvature.
Comparison of weights assigned for decoding velocity vs decoding curvature. (a) The magnitude of the weight assigned to each neuron in recording AML310_A for velocity is compared to the magnitude …
Figure 5—figure supplement 3
Example traces of highly weighted neurons used to decode curvature in AML32_A.
Traces of top five highest weighted neurons used to decode curvature in AML32_A. Same recording as in Figure 4. Arrows indicate activity peaks corresponding to ventral (blue shades, top) or dorsal …
Figure 6 with 1 supplement
Number of neurons needed by the model to decode velocity and curvature.
(a) The minimum number of neurons needed for a restricted model to first achieve a given performance is plotted from recording AML310_A in Figure 1. Performance, is reported separately for …
Figure 6—video 1
Animation showing partial model performance as neurons are added, corresponding to Figure 6a.
Top panel shows performance evaluated on both test and training set. Bottom left shows measured velocity (black) and decoded velocity (blue). Gray shading indicates test set. Bottom right shows …
Figure 7 with 2 supplements
Immobilization alters the correlation structure of neural activity.
(a) Calcium activity is recorded from an animal as it moves and then is immobilized with a paralytic drug, recording AML310_E. (b) Activity of AVAL and AVAR from (a). (c) Population activity (or its …
Figure 7—figure supplement 1
Example from additional moving-to-immobile recording.
Calcium activity is recorded from an animal as it moves and then is immobilized with a paralytic drug, recording AML32_H. Activity and behavior. (b) Population activity (or its derivative) from (a) …
Figure 7—figure supplement 2
Immobile-only recording.
Calcium activity is recorded from an animal immobilized with nano-beads, recording AML310_G. (a) Calcium activity. (b) Activity of neurons AVAL and AVAR. (c) Population activity (or its temporal …
Figure 8
Correlations with respect to AVAL and AVAR during movement and immobilization.
(a) The Pearson’s correlation of each neuron’s activity to AVAR and AVAL is shown during movement and immobilization. Selected neurons are numbered as in Figure 7 (same recording, AML310_E). Neurons …
Tables
Table 1
Number of neurons needed to achieve 90% of full model performance, N90, reported as (median ± standard deviation), across all 11 recordings.
| Velocity N90 | Curvature N90 | Intersection N90 | Total recorded |
|---|---|---|---|
| 27 ± 16 | 31 ± 18 | 7 ± 5 | 121 ± 12 |
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Strain, strain background (C. elegans) | AML310 | this work | Details in Table 2 | |
| Strain, strain background (C. elegans) | AML32 | Nguyen et al., 2017 | RRID:WBI-STRAIN:WBStrain00000192 | |
| Strain, strain background (C. elegans) | AML18 | Nguyen et al., 2016 | RRID:WBI-STRAIN:WBStrain00000191 |
Table 2
Strains used.
Associated Research Resource Identifiers are listed in Key Resources.
| Strain | Genotype | Expression | Role | Reference |
|---|---|---|---|---|
| AML310 | wtfIs5[Prab-3::NLS::GCaMP6s; Prab-3::NLS::tagRFP]; wtfEx258 [Prig-3::tagBFP::unc-54] | tag-RFP and GCaMP6s in neuronal nuclei; BFP in cytoplasm of AVA and some pharyngeal neurons (likely I1, I4, M4 and NSM) | Calcium imaging with AVA label | This Study |
| AML32 | wtfIs5[Prab-3::NLS::GCaMP6s; Prab-3::NLS::tagRFP] | tag-RFP and GCaMP6s in neuronal nuclei | Calcium imaging | Nguyen et al., 2017 |
| AML18 | wtfIs3[Prab-3::NLS::GFP, Prab-3::NLS::tagRFP] | tag-RFP and GFP in neuronal nuclei | Control | Nguyen et al., 2016 |
Table 3
Recordings used in this study.
| Unique ID | Strain | Duration (mins) | Notes |
|---|---|---|---|
| AML310_A | AML310 | 4 | Ca2+ imaging w/ AVA label, moving |
| AML310_B | 4 | ||
| AML310_C | 4 | ||
| AML310_D | 4 | ||
| AML310_E | AML310 | 8 | Ca2+ imaging w/ AVA label, moving-to-immobile |
| AML310_F | 8 | ||
| AML310_G | AML310 | 15 | Ca2+ imaging w/ AVA label, immobile |
| AML32_A | AML32 | 11 | Ca2+ imaging, moving |
| AML32_B | 11 | ||
| AML32_C | 10 | ||
| AML32_D | 11 | ||
| AML32_E | 4 | ||
| AML32_F | 5 | ||
| AML32_G | 4 | ||
| AML32_H | AML32 | 13 | Ca2+ imaging, moving-to-immobile |
| AML18_A | AML18 | 10 | GFP control, moving |
| AML18_B | 10 | ||
| AML18_C | 7 | ||
| AML18_D | 5 | ||
| AML18_E | 5 | ||
| AML18_F | 6 | ||
| AML18_G | 9 | ||
| AML18_H | 6 | ||
| AML18_I | 7 | ||
| AML18_J | 6 | ||
| AML18_K | 6 |
Table 4
List of recordings included in each figure.
| Figure | Recordings |
|---|---|
| Figure 1; Figure 1—figure supplement 1; Figure 1—figure supplement 2; | AML310_A |
| Figure 1—figure supplement 3 | AML310_A-D, AML32_A-G, AML18_A-K |
| Figure 2a,b; Figure 2—figure supplement 1 | AML310_A |
| Figure 2c | AML310_A-D |
| Figure 3a–d | AML310_A |
| Figure 3e,f | AML310_A-D, AML32_A-G, AML18_A-K |
| Figure 3—figure supplement 1; Figure 3—figure supplement 2; Figure 3—figure supplement 4; Figure 3—figure supplement 5 | AML310_A-D, AML32_A-G |
| Figure 3—figure supplement 3 | AML18_A-K |
| Figure 4 | AML32_A |
| Figure 5; Figure 5—figure supplement 1; Figure 5—figure supplement 2 | AML310_A |
| Figure 5—figure supplement 3 | AML32_A |
| Figure 6a,b; Figure 6—video 1 | AML310_A |
| Figure 6c | AML310_A-D, AML32_A-G |
| Figure 7a–f | AML310_E |
| Figure 7g | AML310_A-F, AML32_A-H |
| Figure 7—figure supplement 1 | AML32_H |
| Figure 7—figure supplement 2 | AML310_G |
| Figure 8 | AML310_E |
Table 5
Alternative models explored.
Most are linear models, using either the Ridge or ElasticNet regularization. In some cases, we add an additional term to the cost function which penalizes errors in the temporal derivative of model …
| Model | Penalty | Features | Number of parameters |
|---|---|---|---|
| Linear | Ridge | and | |
| Linear | Ridge | ||
| Linear | Ridge + Acceleration Penalty | and | |
| Linear | Ridge + Acceleration Penalty | ||
| Linear | ElasticNet | and | |
| Linear | ElasticNet | ||
| MARS (nonlinear) | MARS | and | variable |
| Linear with Decision Tree (nonlinear) | Ridge | and |
