Journal: eLife

Article Title: Noisy neuronal populations effectively encode sound localization in the dorsal inferior colliculus of awake mice

doi: 10.7554/eLife.97598

Figure Lengend Snippet: ( A ) Schematic representation of the decoding strategy using multi-class classification on the recorded simultaneous population responses. ( B ) Cumulative distribution plots of the absolute cross-validated single-trial prediction errors obtained using naive Bayes classification (blue) and chance level distribution associated with our stimulation paradigm obtained by considering all possible prediction errors for the 13 azimuths tested (gray). K.S.: Kolmogorov-Smirnov test, n.s.: (p>0.05). ( C ) Schematic representation of the decoding strategy using the first PCs of the recorded population responses. Inset: % of explained variance obtained using PCA for dimensionality reduction on the complete population responses. Median (blue line) and median absolute deviation (shaded blue area) are plotted for (n=12 mice/imaging sessions). ( D ) Same as ( B ) but for decoding different numbers of first PCs from the recorded complete population responses. ( E ) Significance of classification performance with respect to chance level for different numbers of first PCs, determined via Kolmogorov-Smirnov tests with Sidak correction for multiple comparisons. Arrowhead indicates model loss of performance associated with fitting more parameters for a larger feature space (# PCs) with the same dataset size (# trials collected).

Article Snippet: IC population response classification (decoding) was performed using the naive Bayes classifier algorithm implemented in MATLAB, using the fitcnb function from the statistics and machine learning toolbox.

Techniques: Imaging

Journal: eLife

Article Title: Noisy neuronal populations effectively encode sound localization in the dorsal inferior colliculus of awake mice

doi: 10.7554/eLife.97598

Figure Lengend Snippet: ( A ) Histogram of the nS/N ratios from the recorded units across mice during sound stimulation or during the inter trial periods without sound stimulation (on going). ( B ) Representative stimulus azimuth tuning curves from units with significant median response tuning detected using non-parametric one-way ANOVA (Kruskal-Wallis test). Median and absolute median deviation are plotted. The imaging depth from the corresponding units is displayed in gray. Azimuth selectivity is color-coded based on . ( C ) Percentage of the simultaneously recorded units across mice that showed significant median response tuning, compared to false positive detection rate ( α =0.05, chance level). ( D ) Response dependency to stimulus azimuth, determined via χ 2 tests (see Materials and methods), for simultaneously recorded units ranked in descending order of significance. Left inset: Representative responses from the top ranked 7 units with significant response dependency to stimulus azimuth. Response amplitudes are displayed with a continuous trace for visualization purposes, the displayed response order was sorted as a function of stimulus azimuth and does not represent the experimental stimulus delivery order (random). Right inset: Same as ( A ) but for the subset of units displaying response dependency to stimulus azimuth. ( E ) Percentage of the simultaneously recorded units across mice that showed significant response dependency to stimulus azimuth, compared to false positive detection rate ( α =0.05, chance level). ( F ) Schematic representation of the decoding strategy using the top ranked units from the recorded population responses. ( G ) Top: Cumulative distribution plot of the absolute cross-validated single-trial prediction errors obtained with a Bayes classifier (N. Bayes, naive approximation for computation efficiency). The number of top ranked units considered for decoding their simultaneously recorded single-trial population response patterns is color coded from cyan (4 top ranked units) to purple (10 top ranked units) and the chance level distribution associated to our stimulation paradigm, obtained by considering all possible prediction errors for the 13 azimuths tested, is displayed in gray. Bottom: Significance of classification performance with respect to chance level for 4–30 decoded top ranked units, determined via Kolmogorov-Smirnov tests with Sidak correction for multiple comparisons. Arrowhead indicates model loss of performance associated with fitting more parameters for a larger feature space (# units) with the same dataset size (# trials collected).

Article Snippet: IC population response classification (decoding) was performed using the naive Bayes classifier algorithm implemented in MATLAB, using the fitcnb function from the statistics and machine learning toolbox.

Techniques: Imaging

Journal: eLife

Article Title: Noisy neuronal populations effectively encode sound localization in the dorsal inferior colliculus of awake mice

doi: 10.7554/eLife.97598

Figure Lengend Snippet: ( A ) Simplified schematic representation of the possible effects from (positive) noise correlations on the response separability of a theoretical population consisting of 2 units, and within class randomization strategy to model decorrelated datasets lacking noise correlations. ( B, C ) Left top: Representative correlation matrices of pairwise correlations between the responses of top ranked units detected in simultaneous recordings during sound stimuli for representative azimuths. The simultaneously imaged units are sorted in the correlation matrices based on cross validated hierarchical clustering (see Materials and methods). Left bottom: Distribution histograms for the pairwise correlation coefficients (Kendall tau) from pairs of simultaneously recorded top ranked units across mice (blue) compared to the chance level distribution obtained through randomization of the temporal structure of each unit’s activity to break correlations (purple). Vertical dashed lines show the medians of these distributions. * : p<0.05, *** : p<0.0001, Kolmogorov-Smirnov with Sidak. Right: Cumulative distribution plots of the absolute cross-validated single-trial prediction errors obtained using a Bayes classifier (naive approximation for computation efficiency) to decode the single-trial response patterns from the 6 (neuropixels) or 7 (sTeFo 2 P imaging) top ranked units in the simultaneously acquired datasets across mice (cyan), modeled decorrelated datasets (orange) and the chance level distribution associated with our stimulation paradigm (gray).

Article Snippet: IC population response classification (decoding) was performed using the naive Bayes classifier algorithm implemented in MATLAB, using the fitcnb function from the statistics and machine learning toolbox.

Techniques: Activity Assay, Imaging

Journal: Scientific Reports

Article Title: An intelligent learning system based on electronic health records for unbiased stroke prediction

doi: 10.1038/s41598-024-73570-x

Figure Lengend Snippet: Performance comparison with state-of-the-art (SOTA).

Article Snippet: Sailasya et al. , No , No , Low , Kaggle , Naive Bayes , 82.00%.

Techniques: Comparison, Plasmid Preparation