Enhanced tactile coding in rat neocortex under darkness
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Japan
- Institute for AI and Beyond, The University of Tokyo, Japan
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Japan
Figures
Figure 1
Behavioral paradigm and limb movement assessment with concurrent local field potential (LFP) recordings.
(A) A diagram of the disk-shaped treadmill used in the experiment. One half of the disk is covered with #80 sandpaper, and the other half with #2000 sandpaper. (B) A frame from the video capturing a walking rat from a left-side perspective. (C) The motivation scheme. The rats were water-deprived prior to the experiments. A water port was coupled with the movement of the treadmill so that when the rat walked on the treadmill, the water would come out. This way, the rats were always motivated to walk during the whole session. (D) The experimental protocol, where each rat walked for 10 min in light (50 lx) and then for 10 min in darkness (0 lx). (E) An example trajectory of the elbow and wrist joints from one session, plotted with the shoulder joint fixed in the coordinate space (shoulder: yellow, elbow: green, wrist: cyan). (F) A schematic illustrating the forelimb and hindlimb subregions of primary somatosensory cortex (S1). (G) A custom 32-channel electrode array used to record LFPs from these subregions.
Figure 2 with 1 supplement
Comparison of gait parameters across textures and environmental conditions.
(A) Swing phase vs. stance phase, illustrated with video frames (left: swing, right: stance). (B) Normalized swing duration measured for each rat under different textures (smooth vs. rough) and environmental condition (light vs. dark). Light orange and dark orange correspond to the light conditions (smooth, rough), while light blue and dark blue correspond to the dark conditions (smooth, rough). There were no significant differences among trial conditions. (C–E) Stance duration, stride length, and footstrike speed, respectively, under the same conditions as in B. None of these parameters differed significantly across texture types or lighting conditions.
Figure 2—figure supplement 1
Locomotor-speed traces during each trial.
(A) Locomotor-speed traces aligned with corresponding local field potentials (LFPs). (B) Locomotor-speed traces across trials for two rats. Top left: Locomotor speed trace for Rat 10, with the solid line representing the mean and the shaded area indicating the standard deviation (orange: light-trial, navy: dark-trial). Top right: Cumulative distance traveled during trials for Rat 10, with the solid line representing the mean and the shaded area indicating the standard deviation (orange: light-trial, navy: dark-trial). Bottom: Same as the top row but for Rat 11.
Figure 3
LFP recordings in rat S1 during walking.
(A) A single representative LFP trace aligned to a forelimb contact. (B) An example of an average LFP trace from one session, aligned to forelimb contact. (C) The electrode montage and averaged LFP at each electrode. Left: The electrode montage showing all 32 recording sites. Right: Averaged LFP signals aligned to forelimb contacts with the floor, shown for each electrode depicted in the left panel. (D) Comparison of amplitudes between hindlimb and forelimb subregions, aggregated across all 11 rats. p=1.89 × 10–3, t10=–2.2, paired t-test, n=11 rats. (E) Comparison of averaged amplitudes within the same trial for different floor textures and environmental conditions. p>0.05, one-way analysis of variance (ANOVA) followed by Tukey‒Kramer post hoc test, n=149, 149, 107, and 107 trials for smooth-light (light-orange), rough-light (dark-orange), smooth-dark (light-blue), and rough-dark (dark-blue), respectively. (F) Cumulative probability distributions of mean amplitude from each session, compared across different textures. p=4.84×10–1, 8.35×10–1, D=1.03×10–1, and 7.54×10–2 for light and dark environments, respectively, two-sample Kolmogorov‒Smirnov test, n=149 and 107 trials from 11 rats for light and dark, respectively. (G) Cumulative probability distributions of mean amplitude from each session, compared across light and dark environments. p=1.05×10–1, 1.83×10–1, D=0.14 and 0.149 for smooth and rough textures, respectively, two-sample Kolmogorov‒Smirnov test, n=149 and 107 trials from 11 rats for light and dark, respectively. Abbreviations: ERP, event-related potential; LFP, local field potential; S1, primary somatosensory cortex.
Figure 4
Local field potential (LFP) characteristics across textures and lighting.
(A) Example averaged LFP traces from one session, aligned to forelimb contact: light-smooth, light-rough, dark-smooth, dark-rough (left to right). (B) Channel correlation difference map (light-dark) computed from average LFP waveforms (Pearson’s r). Higher values indicate stronger channel-wise correlations in light relative to dark. (C) Correlation matrices are shown separately for each texture and lighting condition. Matrices exhibit similar within-S1-subregion structure, with no clear texture- or light-dependent differences. (D) Example time–frequency (wavelet) spectrogram of LFP power from one session, aligned to forelimb contact. Dotted line indicates the cone of influence. Color scales represent wavelet coefficient magnitude (C) and corresponding power (D). S1, primary somatosensory cortex.
Figure 5
Prediction of texture and environmental conditions from local field potential (LFP) using principal component analysis (PCA) and support vector machine (SVM).
(A) LFP traces aligned to forelimb onset, mapped to two-dimensional space using PCA. Light orange and dark orange represent the light conditions (smooth, rough), while light blue and dark blue represent the dark conditions (smooth, rough). (B) Classification accuracy for two categories (light vs. dark and smooth vs. rough) using an SVM classifier. The red dotted line indicates the chance level.
Figure 6
Deep-learning-based prediction of texture and environmental conditions from LFP.
(A) The deep-learning model architecture. The LFP input is processed through two parallel pathways for macro- and micro-scale feature extraction, followed by residual blocks that feed into two output heads for floor texture (smooth vs. rough) and the environmental condition (light vs. dark). (B) Training performance for a single representative rat. The left graph shows accuracy curves for texture (blue) and lighting (yellow), and the right graph shows the loss curves. (C) Testing performance for the same rat. The model exhibits good generalization, as accuracy increases and loss decreases on held-out data. (D) Confusion matrix for texture classification for all rats. Values above chance on the diagonal indicate successful texture prediction. Note that all values in the same row add up to 1. (E) Same as D, but for environmental conditions. (F) Combined confusion matrix for texture and trial predictions. The model performs well on both tasks across all rats. Abbreviations: avgpool, average pooling layer; conv, convolutional layer; maxpool, max pooling layer; LFP, local field potential.
Figure 7 with 3 supplements
Neural representations are more distinct in dark environments than in light environments.
(A) A 912-dimensional feature vector is extracted from the layer preceding the final output. (B) A scatter plot of these features from one rat shows individual local field potential (LFP) segments (aligned to forelimb contact). Light orange and dark orange correspond to the light conditions (smooth, rough), while light blue and dark blue correspond to the dark conditions (smooth, rough). (C) Silhouette scores across all nine rats, showing that the dark condition yields higher scores and thus more distinct neural representations. p=2.31×10–2, t8=–2.8, paired t-test, n=9 rats. (D) A pseudo-colormap based on occlusion analysis, illustrating the contribution of each electrode in the forelimb and hindlimb subregions. Hotter regions indicate higher importance for the model’s predictions. (E) Forelimb channels exhibit higher occlusion sensitivity than hindlimb channels, highlighting the forelimb’s dominant role when the foot contacts the floor. p=4.53×10–6, t16=–5.57, Student’s t-test, n=9 rats. (F) Class activation maps generated via integrated gradients highlight key input features responsible for accurate model predictions of texture and environmental conditions. Attribution scores show each feature’s impact on the model’s output relative to a reference baseline: high positive scores denote features that strongly affect the predicted class. The onset of forelimb contact is aligned to time zero. (G) Attribution scores averaged over forelimb electrodes for floor texture (left) and the environmental conditions (right). A temporal lag in the dark condition suggests an extended processing window for floor texture when visual cues are absent.
Figure 7—figure supplement 1
Separability of texture representations in inverted session.
Figure 7—figure supplement 2
Silhouette score for each individual rat.
Figure 7—figure supplement 3
Spectral analysis of learned features by convolutional neural network (CNN) model.
Additional files
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Supplementary file 1
Silhouette score in light and dark trials for each rat.
- https://cdn.elifesciences.org/articles/106554/elife-106554-supp1-v1.xlsx
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Supplementary file 2
Silhouette score in light and dark trials for each rat in inverted session.
- https://cdn.elifesciences.org/articles/106554/elife-106554-supp2-v1.xlsx
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MDAR checklist
- https://cdn.elifesciences.org/articles/106554/elife-106554-mdarchecklist1-v1.docx
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Enhanced tactile coding in rat neocortex under darkness
eLife 14:RP106554.
https://doi.org/10.7554/eLife.106554.3
