#correlogram
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neuronasko.bsky.social
And speaking of intrinsic cell behavior here are some papers with better methods of auto-correlation that are hopefully applicable to the auto-correlogram evolution of engram cells.

"Atypical intrinsic neural timescale in autism"
elifesciences.org/articles/42256

And ...
October 18, 2026 at 7:44 PM
random-repec-paper.bsky.social
On the Pitman nonadmissibility of correlogram-based time series methods by Marc Hallin (1994)
IDEAS/RePEc link
to RePEc:ulb:ulbeco:2013/2049
ideas.repec.org
October 6, 2026 at 5:51 PM
netscience.bsky.social
Inferring Temporal Dependencies from Social Time Series with the Cross-Correlogram arxiv.org/abs/2609.16633
Inferring Temporal Dependencies from Social Time Series with the Cross-Correlogram
Characterizing temporal interactions in social systems is challenging because social behavior can be bursty and non-stationary, violating the stationarity assumptions of many methods used to measure t...
arxiv.org
September 17, 2026 at 8:14 AM
arxiv-soc-ph.bsky.social
Inferring Temporal Dependencies from Social Time Series with the Cross-Correlogram
https://arxiv.org/abs/2609.16633
September 16, 2026 at 3:46 PM
arxiv-cs-si.bsky.social
Inferring Temporal Dependencies from Social Time Series with the Cross-Correlogram
https://arxiv.org/abs/2609.16633
September 16, 2026 at 3:34 PM
cssi-bot.bsky.social
Bridget Smart, Renaud Lambiotte, Takaaki Aoki, Ryota Kobayashi: Inferring Temporal Dependencies from Social Time Series with the Cross-Correlogram https://arxiv.org/abs/2609.16633 https://arxiv.org/pdf/2609.16633 https://arxiv.org/html/2609.16633
September 16, 2026 at 6:44 AM
soc-net-bot.bsky.social
Inferring Temporal Dependencies from Social Time Series with the Cross-Correlogram
https://arxiv.org/abs/2609.16633
Characterizing temporal interactions in social systems is challenging because social behavior can be bursty and non-stationary, violating the stationarity assumptions of many me...📈🤖
September 16, 2026 at 6:13 AM
drorlawilliams.bsky.social
🧪Friday post - I am trying to think about how best to address reviewers comments on a paper. I'm looking at the visulisation of data comments, and this led me to this fantastic website which contains a plotting bible! how have I not come accross this before!

www.data-to-viz.com#correlogram
From data to Viz | Find the graphic you need
A classification of chart types based on their input data format.
www.data-to-viz.com
November 14, 2025 at 4:39 PM
ethanofthegulls.bsky.social
However, as you might expect, these variables are highly correlated with each other, both for the varied role pop size has on genomic diversity and analytical reasons. We attempted to disentangle them with LASSO regression, and inferred unique signals of derived TEs, ROH, and recent pop. size.
Correlogram showing the high correlation between most variables in this study, as described in the other figure. Below the diagonal is full Solomons dataset, above is without the smallest island in the dataset (Ghizo), with reduced correlations between runs of homozygosity and variables other than island size and recent MSMC population size.
March 5, 2025 at 5:17 PM
itfeature.bsky.social
Why Does Stationarity Matter"? Whether you are studying econometrics, diving into data science, or building predictive models, time series analysis is a fundamental skill
#timeseriesanalysisimeseriesanalysis" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link">#timeseriesanalysis #stationarity #timeseriesanalysis #
Why Does Stationarity Matter?
In this post, I will discuss "Why Does Stationarity Matter"? Whether you are studying econometrics, diving into data science, or building predictive models, time series analysis is a fundamental skill. At the heart of this analysis lies a critical concept: stationarity. Why Does Stationarity Matter? Stationary Time Series Major Analytical Problem When Working with Non-Stationary Time Series How to Detect Stationarity Graphically in EViews Time Series Line Plot Correlogram (ACF and PACF) Non-Stationary Series Issue Identifying Stationarity vs Non-Stationarity Here is a breakdown of why stationarity is the bedrock of reliable predictive modeling, along with a step-by-step guide to visually detecting stationarity in EViews.
itfeature.com
August 18, 2026 at 6:48 AM
itfeature.bsky.social
A correlogram in Time Series, also known as an Autocorrelation Function (ACF) plot, is a visual tool used in time series analysis to display the correlation of a dataset with itself at different time lags
#timeseriesanalysis #autocorrelation #correlogram #Dataanalysis #ACF #PACF #BasicStatistics
What is a Correlogram in Time Series?
A correlogram in Time Series, also known as an Autocorrelation Function (ACF) plot, is a visual tool used in time series analysis to display the correlation of a dataset with itself at different time lags. It is the primary diagnostic tool for determining if a series is stationary or if it contains specific patterns like trends and seasonality. Correlogram in Time Series Short-term Correlogram in Time Series Alternative Time Series Sample Correlogram (Eviews output) Real Life Examples of Correlogram The Weekend Effect in Retail Correlogram of Atmospheric CO2 Levels Correlogram of Heart Rate Variability (HRV)
itfeature.com
May 2, 2026 at 11:58 AM
castltrastondrs.medsky.network
Understanding the pathology in the 🫁 of patients with #COVID19 might provide clues as to the susceptibility of patients and how the #SARSCoV2 virus can be fatal

www.cell.com/iscience/ful...
High IL-6 levels induce CD95 expression and IL-1β production in macrophages (A) Inflammatory mediators were determined in tracheal aspirate samples obtained from intubated patients with COVID-19, who were sorted in pools of low and high IL-6-producers. (B and C) Concentration of (B) HMGB1 and (C) ATP were measured in the cell-free supernatants derived from tracheal aspirate samples. (D) Correlogram of inflammatory mediators and clinical outcomes in high IL-6-producers. (E) THP-1-derived macrophages (TDM, 5 × 105/mL) were cultured for 24 h with low and high IL-6 cell-free supernatants. (F–I) The median fluorescence intensity (MFI) of CD95 was assessed in (F and G) dying TDM (FVS+) cultured with tracheal aspirate supernatants and in (H and I) total TDM cultured with low (200 pg/mL) and high (2000 pg/mL) concentrations of recombinant human IL-6. (J) IL-1β detection in the supernatants and (K and M) percentage of HLA-DR and (L and M) CD163 evaluated in total live TDM (FVS−) cultured with
December 18, 2023 at 4:59 PM
To correlogram, try ggstatsplot package for #rstats. Great customizable options and returns a #ggplot object.
February 5, 2024 at 9:47 PM
sensation-percept.bsky.social
Audition: Auditory demonstrations resource
ccrma.stanford.edu/~malcolm/cor...
The Correlogram Museum
ccrma.stanford.edu
July 1, 2025 at 4:30 PM
asiermoneva.com
Interestingly, the feedback was highly nuanced. On average, our peers are not simply 'for' or 'against' open science as a whole; instead, they support different combinations of these practices.
A correlogram of Spearman’s rank correlations between the nine attitudes towards open science. Only a subset of tiles with significant correlations are colored.
June 4, 2026 at 8:05 AM
kevinbolding.bsky.social
Anyone know where to find an example of an inhibitory cross-correlogram produced by a somatostatin interneuron? #neuroskyence
July 15, 2025 at 1:26 AM
art.feediverse.org
Randomdot Correlogram - The Golden Fleece, 1977 #dali #opart
November 12, 2023 at 9:00 AM
cianodonnell.bsky.social
anyone come across this statistical issue before?

I have simulated some time series data and am computing crosscorrelograms between two variables. "one year" of smith data recovers ground truth crosscorrelogram (right) but "one month" of sim data (left) gives artifactual negative correlations 🤷‍♂️
two cross-correlogram plots, left shows curves with positive peak at zero time lag, but then negative correlation side lobes. Right plot is similar, but for 'long time series' data, only shows non-negative correlations across the range of time lags.
September 8, 2025 at 12:29 PM
drandygreen.bsky.social
Our new paper on mtDNA structure for fairy shrimps dispersed by waterbirds in Canadian lakes @ebdonana.bsky.social @freshwaterscience.bsky.social If you don't have full access to the paper, see comments below www.tandfonline.com/doi/full/10....
Branchinecta and Artemia franciscana from the Saskatchewan region genetic distances, measured as Kimura 2-parameter (K2P) distances in relation to spatial distance, based on the mitochondrial CO1 gene fragment. There was a significant distance decay in the assembled dataset (Mantel test; nperm = 999; Branchinecta: r = 0.485, P < 0.001; A. franciscana: r = 0.149, P < 0.001); (b) Mantel correlogram for genetic distance (Hellinger-transformed) and geographic distance + 0.1 (log-transformed) in Branchinecta and A. franciscana from the study lakes in Saskatchewan, Canada.
May 21, 2025 at 10:34 AM
drandygreen.bsky.social
New paper with unique comparison of the genetic structure of sympatric fairy and brine shrimp populations in Canadian fishless lakes: #Branchinecta was more diverse than #Artemia @ebdonana.bsky.social @freshwaterscience.bsky.social
@aillimnologia.bsky.social @aslo.org
doi.org/10.1080/2044...
a) Geographic distribution of examined lakes in Saskatchewan and anostracan taxa found in them; b) Bliss lake, one of the sites with Branchinecta; c) one small edge of Bliss lake with numerous Branchinecta swimming in the water (tiny white stripes). a) Branchinecta (blue) and Artemia franciscana (yellow) from the Saskatchewan region genetic distances measured as Kimura 2-parameter (K2P) distances in relation to spatial distance, based on the mitochondrial CO1 gene fragment. There was a significant distance decay in the assembled dataset (Mantel test; nperm=999; Branchinecta: r = 0.485, P < 0.001; A. franciscana: r = 0.149, P < 0.001). b) Mantel correlogram for genetic distance (Hellinger-transformed) and geographic distance (log-transformed) in Branchinecta and A. franciscana from the lakes we studied in Saskatchewan, Canada. The filled circles indicate significant correlation within each distance class.
January 18, 2025 at 9:43 AM
dritzwoller.bsky.social
Here's a correlogram for counties in CA, NY, and ND, sorted by state and population. Urban counties in CA correlate more strongly with urban counties in NY than with rural counties in CA. Rural CA counties correlate more closely with ND counties than with urban areas within CA.
May 4, 2025 at 2:04 AM
tomdonoghue.bsky.social
A fun find in an old paper - in 1956, Mary Brazier noting that of course EEG contains both periodic and aperiodic activity, and relating it to the auto-correlation based analyses they were developing at the time:
Screenshot of text that reads: "When both classes of potentials are present, both periodic and aperiodic, as is usual in the EEG, the correlogram shows a periodically recurrent correlation, alternately positive and negative, riding on the initial decay curve caused by the aperiodic components”
January 7, 2024 at 10:28 PM
ihstevenson.bsky.social
We have one model here for monosynaptic effects:
doi.org/10.51628/001.... Sometimes the monosynaptic model doesn't work well, though. I haven't really dug into these cases, but suspect there's probably high-frequency common input and/or reciprocal connections
Figure from a neuroscience paper with cross-correlograms between pairs of spiking neurons. Shows examples where presynaptic dynamics affect the shape of the correlogram and model fit.
January 11, 2025 at 4:55 PM
jeffmyau.bsky.social
Breaking the cluster apart (eg, the larger red cluster) yields 2 clusters with matching statistics other than spike amplitude. The cross-correlogram also suggests the split cluster should be merged as one unit. I appreciate any suggestions/advice! 2/2 #spikesorting #neurophysiology
December 9, 2024 at 6:51 PM
neuronasko.bsky.social
Fastest cross-correlogram on the market. I’ve been wondering recently if it can be made faster if written in assembly?

github.com/cortex-lab/s...
github.com
November 30, 2025 at 7:46 PM