Download Pinging VERIFIED
Anja Tabatabai
I don't consider this "normal" for any manufacuter. For me it is only the 2 Orbi satellites I have and they are only pinging the default gateway given by the dhcp server of my firewall. I only started capturing the logs into Splunk yesterday but for 20 hours I show over 40,000 pings (screenshot attached) from the 2 sats. Is it impacting my home network performance, probably not. But does it need to be this excessive? I am a security sales engineer and when I do demos I now have to filter out this excessive garbage so it doesn't skew graphs and reports. Bit annoying.
"Normal behavior" in terms of WiFi access points pinging the gateway is also a mystery. The first time I used Wireshark on my network, I was astounded at the amount of "chatter" going on. Even though I have disabled IPv6, there are IPv6 packets flying around my network. Devices are ARPing their brains out. My Tivo's seem inordinately curious, broadcasting incessantly. The WiFi access points are transmitting broadcasts several times a second (even the hidden 5G WiFi backhaul link). I am not even sure what Google search terms would produce a standards document (if there is one).
Viol Ruina has an FM input, so we can modulate its frequency and create some more interesting and varied patterns. Some filters even track 1v/8va so we can use this technique alongside a sequencer to create melody lines. Most often, I use filter pinging alongside filtered noise to create snare drums, as I find the melodic plucks are perfect for emulating snare bodies:
So I'm a chat regular, and today somebody came into chat and asked a question. When he didn't get an answer, he started pinging random people in the room (which I would find very annoying if somebody did to me, so I told him to stop). Well, okay, that annoyed me but at least he didn't do it again. Now I come back a few hours later and he pings people when they join and asks for help. I mean, come on, even users that haven't ever talked on chat before.
(A) Strength of stimulus decoding from raw voltage traces as in [7]. As in the original study, unattended or (B) discarded memories cannot be decoded from raw voltage traces. (C, D) Same as (A) and (B), but decoding from alpha power (Methods), which reveals a sustained representation of the unattended stimulus. In (A) and (C), we analyze data from experiment 2 [7], while in (B) and (D), data from experiment 1 [7]. Light gray bars mark stimulus presentation periods. Notice that data immediately preceding pinging stimulus presentation are shown in this figure. Dashed lines mark the periods in which memories are irrelevant for upcoming behavior, following an instruction cue (dark green). All error bars are bootstrapped SEM, and color bars on the top mark the periods where bootstrapped 95% CI was above zero. Data from Wolff and colleagues (2017) [7].
If there is an active EEG code for both attended and unattended stimuli prior to the visual impulse, as our analyses suggest, then what is the interpretation of the observed increase in EEG decodability [7,18]? We reasoned that EEG reactivation events may emerge from either an increase in the signal about the stimulus (as assumed in the activity-silent interpretation) or through a reduction in the across-trial variability (S3C Fig). In the data, we found that pinging reduces across-trial variability of EEG voltage (Fig 4A), as expected for neural responses to sensory stimuli [38]. In addition, we found that trials with stronger EEG decodability showed lower across-trial variability than trials with weaker EEG decodability during pinging (Fig 4B), demonstrating a link between trial-by-trial EEG variability and pinging-induced increase in EEG decodability. We argue that a reduction of variability with an otherwise intact active memory representation (Fig 3) is a parsimonious interpretation of the visual pinging effect. Alternatively, there is another interpretation of pinging-induced increases in EEG decodability consistent with our findings. Recent modeling work has shown how the enhancement of active representations [20,21] is expected when pinging recurrent neural networks with no need for activity-silent mechanisms [29,39]. An existing representation maintained in an attractor supported by recurrent and competitive interactions enhances its tuning when it is stimulated unspecifically (attractor-boost model, S2 Fig). Also, this mechanism would be consistent with these data, as it shows reduced variability (Fig 4C, gray), concomitant with boosted attractor tuning (S2 Fig). While both of these possible interpretations do not exclude an interplay of active representations with activity-silent mechanisms [5,17,40,41], they offer a parsimonious view that renders activity-silent working memory an inadequate framework to understand increases in decodability induced by nonspecific stimuli [7,18,20,21]. To further support this, we sought to evaluate variability predictions from a computational model where reactivations occur because of factual memory reactivation from silent, synaptic traces. We tested an available biophysical network model for (continuous) activity-silent working memory [5], which is an extension of the canonical but discrete model of activity-silent working memory [1] (Methods). In these simulations, a nonspecific input induced reactivations in some trials, causing an increase of across-trial variability (Fig 4C, black). This is because reactivations in such attractor networks are an all-or-none phenomenon, and great variability is expected when triggering them from weak, decaying activity-silent traces in noisy spiking networks. In sum, pinging reveals an underlying active memory, perhaps by reducing noise (Figs 4A, 4B, and S3C) in the presence of an active code (Figs 1 and 3) or by enhancing tuning in an active representation (S2 Fig), but not by reactivating stimulus signals from silent traces.
(A) Percentage of variability change, relative to 0.2 s before the impulse, computed when the impulse was a visual stimulus in Wolff and colleagues (2017) [7] in gray and Wolff and colleagues (2015) [18] in black. (B) Difference in variances computed across trials with low vs high stimulus decoding (computed at the time of maximal decodability, black arrow) in Wolff and colleagues (2015) [18]. Trials with strong memory decoding showed significantly lower across-trial variance than trials with weak or absent memory decoding. We did not find a significant correlation in Wolff and colleagues (2017) [7] possibly because of a weaker pinging stimulus, which may have contributed to weaker increase in EEG decodability, not visible without baselining the data during the delay period (see S3 Fig). (C) Simulations of the activity-silent working memory model with short-term plasticity (dark) predict an increase of across-trial variability (Fano factor) following reactivations induced by a nonspecific drive. Simulations of the bump-attractor model without short-term plasticity (light) predict a decrease in the Fano factor following a nonspecific drive. See also S3 Fig. (D) Same as (A) but when the impulse was a single-pulse TMS (data from Rose and colleagues, 2016 [6]). Solid bars mark where the change in variability was significant (two-sided t test, p < 0.005), and error bars are bootstrapped 95% CI of the mean. Six out of 54 sessions had outlier TMS artifact variance (i.e., extremely high variance at the time of the impulse) and were removed from this analysis. EEG, electroencephalography; TMS, transcranial magnetic stimulation.
We analyzed 2 available datasets of visual pinging [7,18]. For decoding and EEG variability analyses, we focused on both experiments from [7]. In experiment 1 (n = 30), subjects were cued for which item was going to be probed (cued item, here also called attended, or uncued item, here called discarded memory). In experiment 2 (n = 19), subjects had to alternate their attention between 2 items (their early/late, here attended/unattended memory item). Experiment 1 consisted of 1 session, while experiment 2 consisted of 2 sessions (separated by approximately 1 to 2 weeks) on the same set of subjects. For variability analyses, we also analyzed the experiment of [18] (n = 24). In this experiment, the subjects had to memorize 1 item, thus always within the focus of attention. Importantly, the item decodability from raw voltage never dropped to chance. Additionally, we also analyze the voltage variance of the experiment 2 (n = 6) of a TMS study [6]. We refer the reader to the original studies for extra details [6,7,18]. All these datasets were made available in a fully anonymized format and had been approved by the corresponding institutional review boards, as indicated in the original publications.
To compute the variability drop in the simulated spiking activity, we used the Fano factor [38], which is defined as the variance of spike counts in a given window (100 ms) divided by their mean. We then computed ΔFF, as the difference relative to the baseline period of 2 s before pinging stimulus.
To study how single-trial baseline correction impacts pinging-induced increases in EEG decodability, we applied our decoders to synthetic EEG data generated by a model where spurious EEG reactivations are caused by a reduction in noise variability. First, we simulated 2 hypothetical delay maintenance EEG time series representing 2 independent experimental conditions (i.e., grating oriented 0 0 versus 45 0; n = 200 each condition) using the following Gamma function (f) as a single-trial waveform:
The gist of this is that we are now permanently banning use of pinging services (third-party or built on Glitch) to keep apps on Glitch awake, as part of our efforts to stabilize and support the platform.
I recently started having ping issues in every game I play where the latency jumps every couple of seconds. Jumps of about 40ms. I tried pinging 8.8.8.8 and saw a similar pattern. Typical times were about 12-16ms but every 10 pings or so there is a spike up to 50-100ms. I then tried pinging the 192.168.86.1 (nest router) and again saw the same pattern. Typically 2-5ms with spikes up to 45ms.
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