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000-N33 - IBM i2 Intelligence Technical Mastery Test v1 - Dump Information

Vendor : IBM
Exam Code : 000-N33
Exam Name : IBM i2 Intelligence Technical Mastery Test v1
Questions and Answers : 27 Q & A
Updated On : April 24, 2019
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000-N33 IBM i2 Intelligence Technical Mastery Test v1

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000-N33 exam Dumps Source : IBM i2 Intelligence Technical Mastery Test v1

Test Code : 000-N33
Test Name : IBM i2 Intelligence Technical Mastery Test v1
Vendor Name : IBM
Q&A : 27 Real Questions

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IBM IBM i2 Intelligence Technical

Webinar: The ride-alongside: Intelligence analysis for real Time Crime centers

sequence: i2/hazard hunting Webinar Library Webinar: The experience-alongside: Intelligence analysis for precise Time Crime facilities ArrowView speakers Share Webinar: The ride-alongside: Intelligence evaluation for actual Time Crime facilities on Twitter Share Webinar: The ride-alongside: Intelligence analysis for precise Time Crime centers on facebook Share Webinar: The trip-alongside: Intelligence evaluation for true Time Crime centers on LinkedIn

On the primary episode of “The ride-along” series, be part of IBM i2 legislations Enforcement subject count consultants as they focus on applied sciences and strategies to more with no trouble and correctly combat crime the use of intelligence analysis and precise time crime facilities. You received’t need to miss their firsthand talents, technical abilities, and demos on how law enforcement agencies can use IBM i2 to track down criminals and fight crime.

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IBM Buys Crime Prevention And facts Intelligence utility Developer i2

IBM is saying the acquisition of i2, a corporation that gives intelligence and investigation management utility for legislations enforcement, protection, country wide security and personal sector businesses. fiscal terms had been now not disclosed.

With more than 4,500 valued clientele in a hundred and fifty international locations, i2 is gives intelligence analytics for crime and fraud prevention in sectors similar to banking, defense, health care, coverage, legislation enforcement, country wide safety and retail. i2 solutions are presently used via 12 of the true 20 retail banks globally and eight of the precise 10 greatest corporations on earth.

i2 does doesn’t substitute human intelligence however helps improve the know-how that may power crime-combating, fraud prevention and counter terrorism. i2 can be built-in into IBM’s application group.

IBM says the acquisition will help IBM’s shoppers harness statistics to fight fraud and security threats. From the release: using IBM actual-time analytical options in aggregate with the applied sciences of i2, public companies and private enterprises combating fraud will now have the ability to better collect, analyze and method all the critical records at their disposal.

while its already August, and here is only 1 of a handful of acquisitions IBM has made this 12 months. unlike 2010 (IBM spent $6 billion to purchase 17 corporations in 2010), 2011 has been a relatively low-key 12 months for IBM when it comes to purchasing businesses. This yr’s purchases include real property utility enterprise Tririga.


IBM is getting ready to shut its $34 billion acquisition of pink Hat, however Wall road has 'precise query marks' after its 'mediocre' quarter

Analysts say that IBM delivered a "mediocre" quarter, but now all eyes are on its imminent $34 billion acquisition of crimson Hat and even if IBM's massive guess will pay off.

On Tuesday, IBM introduced it generated $18.18 billion in earnings this previous quarter, lacking analysts' expectations of $18.fifty one billion. here day, IBM's stock changed into down four%.

IBM's multibillion-greenback acquisition bought the enterprise loads of attention, nevertheless it hasn't resolved fundamental questions about its huge-graphic imaginative and prescient and method.

"IBM nowadays, they go to a hockey video game donning a football uniform," Marty Wolf, the founder and president of the mergers and acquisitions advisory firm Martinwolf, told business Insider. "they are becoming slower, their margins are less. Their business mannequin is simply too complicated. They need to deconsolidate. Our belief is, they're searching like a rhino in a container of cheetahs."

As IBM prepares to close its crimson Hat acquisition within the coming quarters, the company has the improvement of a "wait and see" angle from buyers, Katy Ring, a analysis director for IT services at the 451 group, spoke of.

particularly, IBM is making a bet on hybrid cloud, which allows for businesses to run their workloads each on the general public cloud and on on-premise records centers. whereas crimson Hat may be a vital part of doubling down on this strategy, that by myself might not support IBM comfy the cloud business, analysts say.

"I consider IBM has received to make hybrid cloud work as a methodology as a way to stay technically central within the long term," Ring pointed out. "It is familiar with enormous business IT more advantageous than every other cloud company, and so, may potentially emerge as a plenty superior 21st century service company with the aid of taking open source utility to its large blue heart."

'every little thing in the kitchen sink'

IBM talked about its cloud company grew 10% year-over-year, generating earnings of $19.5 billion. despite the fact, analysts say that once IBM reviews cloud enterprise, it can additionally lump in different facets that are not always cloud purposes and cloud services however are regarding cloud, akin to consulting and hardware.

examine greater: IBM inventory sinks 3% after hours after missing Wall highway expectations on profits

And the growth nevertheless lags the performance of alternative cloud providers: Microsoft Azure grew 76% from a 12 months ago, and Amazon web services grew forty five%.

"they have got an 'everything in the kitchen sink' strategy to cloud," Andrew Bartels, vp and fundamental analyst at Forrester, advised company Insider. "They toss every thing that can be concerning cloud into that bucket. there's a lot of ambiguity and possibly misdirection which is of their cloud numbers."

IBM's cloud and cognitive-utility unit itself turned into down 2%, producing revenues of $5 billion, and analysts say here is as a result of IBM is dealing with fierce competition from different cloud suppliers, even in artificial intelligence.

"I feel this shows that organizations like Microsoft and Google and Amazon are gaining extra cognitive solution company," Maribel Lopez, the founder and most important analyst at Lopez research, told business Insider. "I consider IBM needs to get ahead of that trend and ensure they don't lose that marketplace to the different massive cloud providers."

Investing.com senior analyst Haris Anwar stated IBM's turnaround method "remains very tons a work in development," as all its segments either declined or were flat.

"That became a bit disappointing for traders," Anwar informed business Insider. "They have been expecting they'll see some clear improvements, but it is certainly now not the case. they may be still struggling to compete in this cloud-computing phase by which Amazon and Microsoft had a very good run."

That being mentioned, this lack of boom might not always be certain to IBM, Bartels mentioned. On the downside, it could be reflective of the market as a whole as other tech corporations document salary this month.

"It might neatly be that IBM is a harbinger of disappointing income to come back of other vendors within the coming weeks," Bartels noted.

and there is nonetheless some positives, John Roy, usas lead analyst, observed. He expects cloud and cognitive functions at IBM to develop, and the enterprise will additionally advantage from casting off underperforming assets of the enterprise that decelerate the enterprise.

"they are doing greater work in synthetic intelligence and hopefully i could hear greater about that," Roy stated.

'actual question marks'

Analysts say the arriving quarters may be vital for IBM as it closes its acquisition of crimson Hat. Analysts predict this may support IBM generate profits.

as soon as the deal closes, IBM can center of attention on technical integrations and making a product portfolio that comprises pink Hat's offerings — no longer to point out that there might be a cultural adjustment.

Wolf pointed out he has "true question marks" about this.

"a lot of individuals who work at pink Hat don't seem to be going to be that excited to work at IBM," Wolf said. "there is a mixture thing. red Hat looks like a small piece of IBM's company. one of the vital causes individuals like working at pink Hat is because it's no longer a large conglomerate."

besides the fact that children, analysts say, the question is whether or not the IBM salesforce can sell red Hat and if IBM capabilities can improvement from purple Hat being a part of the enterprise. It might take at least two quarters for that to turn up.

Ring believes the market sentiment toward IBM is still high quality, however the company will need to make "some bold strikes."

"The crimson Hat acquisition may be very respectable for IBM if it takes on board the open supply tradition that the enterprise brings to IBM and its valued clientele," Ring mentioned. "It generally is a disaster if IBM does not adapt its culture immediately satisfactory to tug via this benefit for commercial enterprise buyers."


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Google AI Beats Grandmaster in First 'Go' Showdown

A Google-developed supercomputer stunned South Korean Go grandmaster Lee Se-Dol by taking the first game of a five-match showdown between man and machine in Seoul on Wednesday.

After about 3-1/2 hours of play, Lee, one of the greatest players of the ancient board game in the modern era, resigned when it became clear the AlphaGo computer had taken an unassailable lead.

"I was shocked by the result," Lee acknowledged afterwards.

"AlphaGo made some moves that no human would ever make. It really surprised me," he said, adding that the computer had shut out the game "in a perfect manner."

Despite the shock loss, Lee said he had no regrets and was looking forward to the remaining four matches.

"I had some failures in the early stages today so if I improve on this, I think I still have some chance to win," he said.

Although the computer had whitewashed European champion Fan Hui 5-0 last October, it had been expected to struggle against 33-year-old Lee, who has topped the world rankings for most of the past decade.

But its creators had been bullish going into the match at the Four Seasons hotel in the South Korean capital, saying the computer, which employs algorithms that allow it to learn and improve from matchplay experience, was even stronger than when it took on Fan.

"We are very, very excited by this historic moment and very, very pleased with how AlphaGo performed," Demis Hassabis, the CEO of AlphaGo developer DeepMind, said after the victory.

"We think that Lee will come up with new strategies and... try some different things tomorrow. We'll have to see how AlphaGo will deal with it," Hassabis said.

The match-up sparked enough interest to warrant an Internet live-stream as well as live TV broadcasts in South Korea, China and Japan.

"I was shocked. Everyone was," said Kim Seong-Ryong, a Korean Go commentator and professional player.

"Something none of us thought would happen has just happened."

The five-day battle for supremacy between man and machine has been seen as a major test of what scientists and engineers have achieved in the sphere of Artificial Intelligence over the past 10 years or so.

The most famous A.I. victory to date came in 1997, when the IBM-developed supercomputer Deep Blue beat the then-world class chess champion Garry Kasparov.

But experts say Go presents an entirely different challenge as the complexity of the game and almost incalculable number of move options mean that the computer must be capable of human-like "intuition" to prevail.

"Go really is our Mount Everest," said Hassabis, adding that the public response to the clash with Lee had been "far bigger than we expected."

When Lee first accepted the A.I. challenge, he had confidently predicted a clear-cut win, saying that AlphaGo's performance against Fan had been nowhere near good enough to defeat him.

But the grandmaster had confessed to some pre-match nerves on Tuesday.

Technical Breakthrough Go involves two players alternately laying black and white stones on a checkerboard-like grid of 19 lines by 19 lines. The winner is the player who manages to seal off more territory.

The game reputedly has more possible board configurations than there are atoms in the Universe, and mastery by a computer was thought to be at least a decade away until the victory over Fan last year.

Creating "general" or multi-purpose, rather than "narrow," task-specific intelligence, is the ultimate goal in A.I. -- something resembling human reasoning based on a variety of inputs, and self-learning from experience In the case of Go, Google developers realized a more "human-like" approach would win over brute computing power.

AlphaGo uses two sets of "deep neural networks" containing millions of connections similar to neurons in the brain.

It is able to predict a winner from each move, thus reducing the search base to manageable levels -- something co-creator David Silver has described as "more akin to imagination."


Multisensory integration in rodent tactile but not visual thalamus

Evoked potentials in VPM and dLGN as a result of unimodal versus bimodal visual-tactile stimulation

Throughout the study we use the following terms as defined here: unimodal describes a modality-specific stimulus of one single sensory modality; cross-modal describes a modality non-specific stimulus; bimodal describes two co-occurring stimuli from different sensory modalities, e.g. simultaneous visual and tactile stimulation; multisensory integration describes a process involving multisensory processing that leads to a different neural response after bimodal when compared to unimodal stimulation (i.e. a statistically significant difference in the rate and/or temporal pattern of spiking activity at the level of single neurons, or a statistically significant difference of neural activity at subthreshold/local field potential (LFP) level).

To get first insights into the convergence of visual and tactile information in FO thalamus, we assessed the impact of unimodal and bimodal stimulation on the LFP in VPM and dLGN when simultaneously recorded with the primary sensory cortices S1 and V1 of postnatal day (P) 19–23 Brown Norway rats (n = 18) (Fig. 1a–d). All recordings were conducted under urethane anaesthesia to avoid the impact of spontaneous whisking and varying alert states on the feedforward mechanisms of multisensory processing24. The good visual acuity of pigmented Brown Norway rats compared to albino rats25 makes them well-suited for testing visual-tactile processing. Thalamic and cortical regions were targeted according to stereotaxic coordinates26 that were confirmed post-mortem by reconstructions of the electrode tracks confined to the area of interest (Fig. 1b–d) as well as according to electrophysiological landmarks (evoked potentials (EPs) in S1, V1, VPM, dLGN, and layer 4 reversal in S1 and V1).

Figure 1

Uni- versus bimodal evoked responses in primary sensory cortices and corresponding first-order thalamic nuclei. (a) Schematic drawing displaying the sensory stimulation via light flashes and whisker deflections as well as the position of recording electrodes in S1, VPM, V1, and dLGN of a Brown Norway rat. (b) (i) Schematic drawing displaying the location of all recording sites in VPM (n = 18) that were used for the analysis of the LFP and SUA. (ii) Same as (i) for dLGN. (c) (i) Nissl stained coronal section displaying the location of the custom-made DiI-labelled multi-site recording electrode in the S1 of a Brown Norway rat. Note that the silicon probe is inserted angularly to target both S1 and VPM. (ii) Same as (i) for VPM. (iii) Nissl stained coronal section displaying the position of recording sites in the VPM in (ii) when displayed at higher magnification. (d) Same as (c) for V1 and dLGN. (e) Positive (P1) and negative (N1) peaks of EPs averaged across animals after visual (red), tactile (orange) and bimodal visual-tactile (blue) stimulation in granular layers of S1 (top) and VPM (bottom). Inset displays the EP in VPM at larger magnification. Black arrow marks the stimulation. (f) (i) Bar diagrams displaying the maximum amplitude of P1 and N1 across animals after visual (red), tactile (orange) and bimodal visual-tactile (blue) stimulation in VPM. (ii) Bar diagrams displaying the latency of the maximum amplitude of P1 (left) and N1 (right) after tactile (orange) and bimodal visual-tactile (blue) stimulation in VPM. (g) Same as (e) for granular layers of V1 and dLGN. (h) Same as (f) for dLGN. A1 = primary auditory cortex, PtCx = parietal cortex, RSC = retrosplenial cortex. (*p < 0.05, **p < 0.01, ***p < 0.001; paired sample t-test). The schematic drawing in (a) was obtained and modified from Bieler et al80. Visual-tactile processing in primary somatosensory cortex emerges before cross-modal experience. Synapse. 2017 Jun;71(6). John Wiley & Sons, Inc., Hoboken, NJ, USA.

In the absence of sensory stimulation, neural activity in VPM and dLGN was characterized by network oscillations in delta (1–4 Hz) to beta-low gamma (12–45 Hz) frequency range. These spontaneous rhythms in VPM and dLGN had a similar temporal structure and low amplitude when compared with the simultaneously recorded activity of S1 and V1 (Supplementary Fig. S1). Their power peaked in the delta-theta band (Supplementary Fig. 1, Supplementary Table S1). Even in the absence of stimuli, multi-unit activity (MUA) discharge of thalamic neurons was prominent (VPM: 7.94 ± 2.78 Hz; dLGN: 4.47 ± 1.97 Hz) (Supplementary Fig. S1).

Tactile stimulation led to evoked responses with fast peaks (P1) followed by slower components (N1) in VPM and S1 (Fig. 1e). Previous investigations showed that bimodal stimulation enhances the amplitude of EPs in S17. Similarly, the amplitude of P1 of VPM EPs was significantly (paired sample t-test, n = 18, t = −2.62, p < 0.018) enhanced after bimodal visual-tactile stimulation (tactile: −73.08 ± 9.51 µV, bimodal: −91.37 ± 13.97 µV), whereas the amplitude of N1 remained unaffected (paired sample t-test, n = 18, t = −1.985, p = 0.063, tactile: 107.73 ± 10.97 µV, bimodal: 94.93 ± 8.87 µV) (Fig. 1fi, Table 1). The multisensory enhancement of the P1 response in VPM was not supra-additive, as the arithmetic sum of the P1 amplitude after visual and after tactile stimulation was not significantly larger when compared to bimodal visual-tactile stimulation (paired sample t-test, n = 18, t = 0.058, p = 0.96, arithmetic sum visual and tactile: −91.83 ± 13.13 µV, bimodal: −91.37 ± 13.97 µV). Bimodal stimulation did not decrease the latency of the P1 peak in VPM (paired sample t-test, n = 18, t = 0.59, p = 0.633, tactile: 15.41 ± 1.4 ms, bimodal: 15.66 ± 1.38 ms) (Fig. 1fii, Table 1). Thus, visual stimulation modulated the tactile evoked responses in VPM. This effect was variable as revealed by visual inspection of averaged EP traces across trials of individual animals. Overall, 10 animals showed an increase in the VPM P1 peak after bimodal visual-tactile stimulation when compared to unimodal tactile stimulation, 6 animals a decrease and 2 animals displayed no change (as compared to S1 where 12 animals displayed an increase and 6 animals a decrease in the amplitude of the EP after bimodal visual-tactile stimulation). The source of these variable responses is unknown, but it might represent the variable processing along sensory tracts27,28.

Table 1 Peak amplitude and latency of EPs in first-order thalamus and primary sensory cortex after unimodal and bimodal stimulation.

The prominent changes caused by bimodal stimulation in VPM seemed to be structure-specific, since visual-tactile stimulation did not similarly affect the EPs in dLGN along the visual tract (Fig. 1g,h, Table 1).

Thus, co-occurring visual stimuli facilitate the tactile evoked responses in VPM, yet tactile modulation of sensory evoked responses was absent in dLGN and V1.

Induced oscillatory activity in the VPM and dLGN as a result of unimodal versus bimodal visual-tactile stimulation

In addition to the spontaneous and stimulus-evoked network activity, stimulus-induced oscillations have been detected (Supplementary Fig. S2). In contrast to the stimulus-evoked activity, stimulus-induced network oscillations are causally related but not phase-locked to the sensory stimuli. Previous studies showed that in the superior colliculus (SC) and primary sensory cortices these patterns of oscillatory activity are profoundly modulated by bimodal stimulation7,17,29,30,31. To test whether visual-tactile stimulation modulates network activity in the corresponding FO thalamic nuclei, we assessed the temporal dynamics of stimulus-induced oscillations in different frequency bands. First, we analysed individual frequency spectra corresponding to a large number of unimodal stimulation trials. In VPM, shortly after the stimulus-evoked response (i.e. 50–150 ms post-stimulus), the amplitude of stimulus-induced oscillations significantly increased particularly in the 1–4 Hz, 4–8 Hz, and 8–12 Hz frequency bands when compared with baseline conditions (i.e. 200–300 ms pre-stimulus). In dLGN, unimodal visual stimulation led to similar modulation of network oscillations as observed after whisker deflections in VPM (Supplementary Fig. S2).

Second, we assessed the effects of bimodal visual-tactile stimulation on the induced oscillatory activity in FO thalamus. For this, we pooled the multiple-trial baseline normalised Morlet wavelet spectra across animals (n = 18) separately for the three different conditions: unimodal tactile, unimodal visual and bimodal visual-tactile stimulation. Visual or tactile stimulation led to minor modifications of the frequency distribution in VPM or dLGN, respectively (Fig. 2a,c). Unimodal stimulation (tactile for VPM and visual for dLGN) modulated the power of oscillatory activity mainly in beta-low gamma band (20–45 Hz) in VPM and in delta (1–4 Hz) and gamma band (50–128 Hz) in dLGN. The modulatory effects augmented during bimodal visual-tactile stimulation. To assess the power of oscillatory activity in VPM and dLGN after bimodal visual-tactile stimulation, we compared the baseline-normalised power range for each time-frequency point of the Morlet wavelet spectra. The time points with significant power changes after bimodal versus unimodal stimulation were clustered using a previously developed protocol7. In VPM, three such time-windows were identified in delta (Wilcoxon signed-rank test, n = 100035 time-frequency values, Z = −90.04, p < 0.001, tactile: 1.19 ± 0.0007, bimodal: 1.33 ± 0.0007), beta-low gamma (Wilcoxon signed-rank test, n = 7000 time-frequency values, Z = −75.50, p < 0.001, tactile: 1.09 ± 0.0006, bimodal: 1.21 ± 0.0004), and gamma frequency (Wilcoxon signed-rank test, n = 14400 time-frequency values, Z = −97.04, p < 0.001, tactile: 1.09 ± 0.0003, bimodal: 1.16 ± 0.0004) (Fig. 2a,b). In dLGN, two clustered time-windows with significant power changes in delta (Wilcoxon signed-rank test, n = 40425 time-frequency values, Z = 177.33, p < 0.001, visual: 1.12 ± 0.0001, bimodal: 1.06 ± 0.0001) and theta band (Wilcoxon signed-rank test, n = 2170 time-frequency values, Z = −42.41, p < 0.001, visual: 1.26 ± 0.003, bimodal: 1.48 ± 0.003) were detected (Fig. 2c,d). With the exception of delta power in dLGN, which decreased after visual-tactile stimulation, bimodal stimulation augmented the oscillatory power of network oscillations in FO thalamic nuclei in a frequency-specific pattern (Fig. 2b,d).

Figure 2

Uni- and bimodal induced responses in first-order thalamic nuclei. (a) Baseline normalised Morlet wavelet spectra of the LFP in VPM averaged for all rats (n = 18) 200 ms pre-stimulus and 1000 ms post-stimulus after unimodal visual (left), unimodal tactile (middle) and bimodal visual-tactile (right) stimulation. Magenta dotted boxes indicate significant time and frequency windows of power modulation (z-score <= −2.56/z-score >= 2.56) after bimodal stimulation when compared to unimodal tactile stimulation. Black arrow marks the stimulation. (b) Bar diagram displaying the mean power of oscillatory activity in VPM during previously defined post-stimulus time-windows normalised to the power of activity before stimulation (baseline). (c) Same as (a) for dLGN after bimodal visual-tactile stimulation when compared to unimodal visual stimulation. (d) Same as (b) for dLGN. (*p < 0.05, **p < 0.01, ***p < 0.001; Wilcoxon signed-rank test).

These results indicate that at the level of FO thalamus, visual-tactile stimulation modifies the power of induced network oscillations in VPM, while this effect is inconsistent in dLGN.

Cross-modal phase reset as a mechanism of multisensory integration in first-order thalamus

Next, we aimed to elucidate the mechanism by which the co-occurring visual-tactile stimuli modulate the evoked and induced network activity in FO thalamic nuclei. Alignment of oscillatory activity to a specific phase has been proposed to prime the neural system about forthcoming sensory stimuli, and by these means, to augment their effectiveness for multisensory perception and behavior32. While this cross-modal phase reset has been found ubiquitary in primary sensory cortices7,8, it remains unknown whether a similar mechanism exists at the level of FO thalamus. Therefore, we compared the phase synchrony of spontaneous oscillations in VPM and dLGN during a large number of cross-modal stimulation trials (visual stimulation when recording in VPM or tactile stimulation when recording in dLGN) and calculated the mean resultant vector (MRV) length of oscillatory phases of the previously identified spontaneous delta (1–4 Hz), theta (4–12 Hz) and beta-low gamma (12–45 Hz) activity in VPM and dLGN (Supplementary Fig. S1). If the oscillatory phase is the same in each trial, the MRV length will be 1, whereas if the oscillatory phase is absolutely random, the value will be 0 (Fig. 3a,c). In addition, to exclude the EP-related synchrony, we calculated the post-/pre-stimulus power ratio ([0–250 ms post-stimulus]/[200–300 ms pre-stimulus])8. In contrast to a true phase reset, the EP-related synchrony is accompanied by a pre- to post-stimulus power increase in the single-trial responses33,34. Therefore, a post-/pre-stimulus power ratio of 1 and a MRV > 0 correspond to true phase synchrony. These conditions were fulfilled only by the oscillatory activity in the 1–4 Hz range in VPM, but not dLGN (Fig. 3b,d). Thus, visual stimulation enhances the phase synchrony of oscillatory activity in VPM.

Figure 3

Event-related phase synchrony in first-order thalamic nuclei after visual or tactile stimulation. (a) Plot displaying the baseline normalised mean resultant vector (MRV) length of oscillatory phases in VPM after visual stimulation (dotted line, black arrow). The values were averaged over each time point in all 18 rats for three frequency bands, 1–4 Hz (red), 4–12 Hz (green) and 12–45 Hz (black). Note, visual stimulation caused strong and long-lasting (413 ms for 1–4 Hz, 266 ms for 4–12 Hz, 219 ms for 12–45 Hz) augmentation of phase synchrony in all frequencies compared to baseline (i.e. >99% confidence interval). (b) Boxplots displaying the post-/pre-stimulus power ratio for 1–4 Hz (red), 4–12 Hz (green) and 12–45 Hz (black) oscillations. (c) Same as (a) for dLGN. Note, a similar significant increase in phase synchrony (i.e. >99% confidence interval) for 4–12 Hz and 12–45 Hz as compared to VPM was found in dLGN. (d) Same as (b) for dLGN. (Horizontal coloured lines mark the border of confidence α = 0.001).

Neuronal firing in the VPM and dLGN as a result of unimodal versus bimodal visual-tactile stimulation

Previous studies revealed that the information content from multiple senses is encoded in primary sensory cortices not only at population level but also by the firing of individual neurons16,35. For this, both rate and temporal coding strategies are used. To assess whether single thalamic neurons process multisensory information by means of rate and temporal coding, we examined single unit activity (SUA) obtained by clustering the extracellularly recorded spikes according to their waveform shape. The patterns of thalamic firing resembled the previously described spiking patterns of cortical neurons after visual, tactile and bimodal visual-tactile stimulation (Supplementary Fig. 3)16. Only neurons that responded to stimulation by increasing their firing rate after unimodal and bimodal stimulation were considered for further analyses. In the VPM, unimodal tactile stimulation led in a large fraction of neurons (75 out of 126) to a strong firing increase peaking at 16.64 ± 0.63 ms after the stimulus (Fig. 4a, Table 2). When compared to baseline, the firing increase in VPM was followed by a non-significant decrease and subsequently, by a long-lasting low-magnitude augmentation of firing beginning 200 ms after stimulation (Fig. 4a). A fraction of neurons in VPM (50 out of 126) responded to visual stimuli by either augmenting or depressing their firing probability (Fig. 4c, Table 2). The firing of dLGN neurons was similarly affected by unimodal visual and bimodal visual-tactile stimulation (Fig. 4b), yet only 19 out of 66 dLGN neurons changed their firing probability after tactile stimulation, and the majority of those neurons (14 out of 19) displayed a reduction in firing probability (Table 2).

Figure 4

Neuronal firing in first-order thalamic nuclei after uni- and bimodal stimulation. (a) (i) Rasterplot depicting the firing of single cells for each trial after unimodal tactile (orange, top) and bimodal visual-tactile (blue, bottom) stimulation. (ii) Bar diagram displaying the firing probability after unimodal tactile (orange) and bimodal visual-tactile (blue) stimulation of single neurons that enhance their firing after stimulation. (iii) Bar diagram displaying the firing variability measured by the Fano factor of single neurons that enhance their firing after stimulation. (b) Same as (a) for dLGN. (c) (i) Rasterplot depicting the firing of single cells in VPM (12 out of 126) that showed a firing enhancement after unimodal visual stimulation (marked by dotted line and black arrow) (top) and line plot displaying the corresponding firing probability (bottom). Inset displays the firing response of the strongly responding neuron marked by black arrow above. Grey line corresponds to the firing probability after exclusion of the strong visual responsive neuron. (ii) Same as (i) for neurons in VPM that showed a depression of firing probability after visual stimulation (38 out of 126). (d) (i) Bar diagram displaying the MRV of VPM-S1 spike-LFP locking strength 0–80 ms after visual (red), tactile (orange) and bimodal visual-tactile (blue) stimulation. (ii) Polar plot displaying the coupling of spikes of VPM neurons to the phase of 1–4 Hz network oscillations in S1. (iii) Bar diagram displaying the fraction of significantly phase-locked neurons. White numbers indicate the total number of significantly phase-locked neurons after stimulation. (e) Same as (f) for dLGN spikes phase locked to 1–4 Hz oscillations in granular layers of V1. (*p < 0.05, **p < 0.01, ***p < 0.001; one sample t-test, z-test for two proportions).

Table 2 Firing patterns of thalamic neurons after uni- and bimodal stimulation.

Though the majority of clustered units in VPM (75 out of 126) enhanced their firing shortly after stimulation, there was no statistically significant difference in the augmentation of firing probability between unimodal and bimodal stimulation (Fig. 4aii). However, the neuronal firing after bimodal stimulation was more variable, since the Fano factor (FF), which captures the response variability of single neurons, increased when compared to unimodal tactile stimulation (one sample t-test, n = 59 units, t = 2.02, p = 0.04, tactile: 0.45 ± 0.04, bimodal: 0.57 ± 0.05) (Fig. 4aiii). Given that a similar amount of VPM neurons reponded to bimodal and unimodal stimulation, the increase in variance can most likely be attributed to a few individual neurons that increased their firing variability rather than to a population effect. Bimodal stimulation neither affected the firing probability (one sample t-test, n = 48 units, t = 0.44, p = 0.66, visual: 0.39 ± 0.06, bimodal: 0.41 ± 0.06) nor the firing variability of neurons in dLGN (one sample t-test, n = 48 units, t = 0.74, p = 0.46, visual: 0.55 ± 0.06, bimodal: 0.61 ± 0.07) (Fig. 4bii,iii).

To decide whether thalamic neurons process multisensory information using a temporal code, we assessed the impact of visual-tactile stimulation on the timing of neuronal firing in FO thalamic nuclei to network oscillations in primary sensory cortices. The analysis was performed during the first 80 ms after sensory stimulation, the time window with the strongest firing response. The spike-LFP coupling analysis revealed that VPM neurons increase their locking strength to delta oscillations in granular (G) layers of S1 after bimodal visual-tactile stimulation when compared to unimodal tactile stimulation (circ_cm test, n = 43 units tactile, n = 64 units bimodal, p = 0.0001, tactile: 0.52 ± 0.03, bimodal: 0.65 ± 0.02) (Fig. 4di,ii). The enhancement of VPM-S1 spike-LFP coupling strength was accompanied by a significant increase in the number of VPM neurons significantly phase-locked to delta oscillations in G layers of S1 after bimodal visual-tactile stimulation when compared to unimodal tactile stimulation (two proportion z-test, n = 126 units, Z = −2.68, p = 0.007, tactile: 43 out of 126 neurons, bimodal: 64 out of 126 neurons) (Fig. 4diii). This was not the case for the spike-LFP coupling in the dLGN-V1 circuit (two proportion z-test, n = 66 units, Z = −1.56, p = 0.12, visual: 44 out of 66 neurons, bimodal: 52 out of 66 neurons) (Fig. 4e). Few VPM neurons (20 out of 126 neurons) timed their firing to S1 network oscillations after unimodal visual stimulation as well, though their locking strength was not augmented when compared to bimodal stimulation (circ_cm test, n = 20 units visual, n = 64 units bimodal, p = 0.60, visual: 0.71 ± 0.05, bimodal: 0.65 ± 0.02) (Fig. 4d).

Finally, we tested whether VPM neurons apply a dual rate and temporal coding strategy to process and relay multisensory information. We found that the majority of VPM neurons that are phase-locked to S1 delta oscillations also showed an enhancement in firing probability after bimodal visual-tactile stimulation (tactile: 37 out of 43 phase-locked neurons showed an increase in firing probability, bimodal: 55 out of 64 phase-locked neurons showed an increase in firing probability). In contrast, visual responsive neurons in VPM that were phase-locked to delta oscillations in G layers of S1 were less likely to display an increase in their firing probability (4 neurons out of 12). Thus, unimodal as well as multisensory neurons in VPM display a dual rate and temporal coding mechanism, which was absent for visually responsive neurons in VPM.

Taken together, these results indicate that neurons in VPM largely enhance their firing probability after tactile and bimodal stimulation. Few VPM neurons respond to visual stimulation. Overall, VPM neurons process multisensory stimulation by increasing their spiking variability and timing their firing output to ongoing network oscillations in S1 when compared to unimodal tactile processing. These effects were absent in dLGN neurons.

Thalamo-cortical coupling as a result of unimodal versus bimodal visual-tactile stimulation

The data above indicate that the FO thalamic nuclei, in particular VPM, are sites of multisensory integration. Relaying visual-tactile information from FO thalamus to primary sensory cortices by timing the firing output of single thalamic neurons to cortical network oscillations is an efficient neuronal coding mechanism. Next, we investigated in more detail how visual-tactile stimuli are transferred from the thalamic to cortical level.

Phase-amplitude cross-frequency coupling (CFC) has been identified as a powerful mechanism of regulating multi-scale networks and transferring information between areas after sensory events36. This mechanism has received little attention in multisensory research despite its critical role in memory and executive processing37,38,39. In a first step, we assessed the thalamo-cortical phase-amplitude CFC along the tactile processing stream between VPM and G layers of S1. We calculated the relative change [(post-stimulus) – (pre-stimulus)] of the modulation index (MI), which captures the CFC induced by stimulation. Bimodal stimulation significantly augmented the coupling of theta-beta phase in VPM to the amplitude of gamma oscillations in the G layers of S1 when compared to unimodal tactile stimulation (one-tailed paired t-test, n = 17, t = 3.21, p = 0.002, tactile: 0.05 ± 0.04, bimodal: 0.62 ± 0.18) (Fig. 5a). Notably, the amplitude of gamma oscillations (30–100 Hz) in G layers of S1 significantly increased after both unimodal (one-tailed paired t-test, n = 17, t = −5.08, p < 0.001, pre-stimulation: 751.54 ± 102.95 µV2, post-stimulation: 1180.38 ± 154.57 µV2) and bimodal stimulation (one-tailed paired t-test, n = 17, t = −3.58, p = 0.001, pre-stimulation: 778.87 ± 116.05 µV2, post-stimulation: 1151.79 ± 160.77 µV2). Thus, despite the presence of cortical gamma oscillations induced by unimodal tactile and bimodal visual-tactile stimulation, the coupling between VPM beta and S1 gamma oscillations is only enhanced after bimodal stimulation. In a second step, we assessed the thalamo-cortical phase-amplitude CFC along the visual processing stream. The MI was not significantly modulated after bimodal visual-tactile stimulation when compared to unimodal visual stimulation for CFC between dLGN and G layers of V1 (one-tailed paired t-test, n = 17, t = −0.69, p = 0.25, visual: 0.56 ± 0.10, bimodal: 0.47 ± 0.11) (Fig. 5b).

Figure 5

Thalamo-cortical phase-amplitude cross-frequency coupling and directed interactions after uni- and bimodal stimulation. (a) Schematic diagram of assessed coupling. (i) Heat maps displaying the relative (post-stimulation – pre-stimulation) phase amplitude cross-frequency coupling (CFC) between VPM and G layers of S1 after visual, tactile and bimodal stimulation. (ii) Bar diagram displaying the averaged relative modulation index (MI) for visual (red), tactile (orange) and bimodal visual-tactile (blue) stimulation. (b) Same as (a) for the coupling between dLGN and G layers of V1. (c) Schematic diagram of assessed directed interactions. (i) Plot displaying the relative partial directed coherence change (post-stimulus/pre-stimulus) 100–600 ms after unimodal visual (red), unimodal tactile (orange) and bimodal visual-tactile (blue) stimulation when assessed for 2–40 Hz oscillations in VPM and G layers of S1. (ii) Bar diagram displaying the relative partial directed coherence change (post-stimulus/pre-stimulus) 100–600 ms after unimodal visual (red), unimodal tactile (orange) and bimodal visual-tactile (blue) stimulation when assessed for the frequency range marked in (i) by the grey box. (d) Same as (c) for the information flow between dLGN and G layers of V1. Red dotted lines mark the border of an enhanced information flow (>1) or decreased information flow (<1.0). (*p < 0.05, **p < 0.01, ***p < 0.001; paired sample t-test).

Thus, a change in coupling strength between VPM and G layers of S1 was only detected after bimodal visual-tactile stimulation despite the presence of induced cortical gamma oscillations by uni- and bimodal stimulation. The MI was similar after uni- and bimodal stimulation in dLGN. Thus, phase-amplitude CFC is a mechanism by which bimodal information is processed and conveyed between VPM and S1.

Directionality of information flow within thalamo-cortical circuits as a result of unimodal versus bimodal visual-tactile stimulation

The FO thalamic nuclei strongly project to G layers of primary sensory cortices40. By these means, modality-specific sensory information is reliably relayed from the periphery to the cortex. To decide whether co-occurring visual and tactile stimuli impact the thalamo-cortical information flow, we analysed the generalised partial directed coherence (gPDC), a measure that reflects the directionality of network interactions in different frequency bands41. First, we calculated the gPDC values for interactions between VPM and G layers of S1 after unimodal tactile and bimodal visual-tactile stimulation, normalised to the baseline condition (i.e pre-stimulus) (Fig. 5c). Values of relative gPDC >1 mirror an increase in the flow of sensory information between the investigated brain areas, whereas values <1 correspond to a decrease of directed interactions. Shortly (100–600 ms) after stimulus, the relative gPDC significantly increased in the 20–40 Hz frequency range when visual and tactile stimuli were presented concurrently (paired sample t-test, n = 17, t = 3.63, p = 0.001, tactile: 0.89 ± 0.04, bimodal: 1.09 ± 0.05). An augmented flow of information was not present along the visual tract, the directionality of interactions between dLGN and G layers of V1 being similar for unimodal and bimodal conditions (one-tailed paired t-test, n = 17, t = −1.24, p = 0.12, visual: 0.96 ± 0.06, bimodal: 0.86 ± 0.05) (Fig. 5d). In line with the analyses of thalamo-cortical coupling, these results indicate that bimodal visual-tactile stimulation modulates the information flow between VPM and S1 by augmenting the thalamic drive to the primary sensory cortex. In contrast, the information flow between dLGN and V1 is not modulated in a similar way.

Anatomical substrate of visual-tactile interactions in FO thalamic nuclei

Direct axonal connectivity between S1 and V1 has been proposed as substrate of functional interactions accounting for multisensory integration at the level of primary sensory cortices7,13,16. The presence of similar processing mechanisms at the level of FO thalamus raises the question whether thalamo-thalamic connectivity may account for multisensory integration observed in FO nuclei or whether visual-tactile information is integrated at even earlier stages such as the midbrain or brainstem. First, we assessed the direct connectivity between VPM and dLGN by anatomical tracing. Small amounts of cholera toxin subunit B (CTB) were injected either into the VPM or dLGN by iontophoresis (VPM: n = 3, dLGN: n = 4). The procedure confined the tracer to the area of interest without reflux to neighboring areas (Fig. 6aii,iii,bii,iii). CTB injections did not reveal direct projections between VPM and dLGN. Retrogradely stained neurons were neither detected in dLGN when VPM was targeted (Fig. 6aiv) nor in VPM when CTB was confined to dLGN (Fig. 6biv). To elucidate the potential source of visual inputs to VPM and the absent tactile responses in dLGN, we further analysed retrogradely labelled cell bodies in upstream targets. We found that CTB injections into VPM revealed backlabelled cell bodies particularly in the parafiscicular nucleus (PF) of the intralaminar thalamus (Fig. 6av–vii). The PF nucleus shares anatomical connections with other brain areas, such as the SC, brainstem and basal ganglia42,43. The dLGN received strong projections from the superficial layers of the SC (Fig. 6bv–vii).

Figure 6

Thalamo-thalamic and thalamo-cortical connectivity revealed by retrograde tracing. (a) Assessment of projections from dLGN to VPM. (i) Schematic diagram displaying the site of CTB injection into VPM. (ii) Photograph displaying the CTB injection confined to VPM. (iii) Larger magnification of the CTB injection in VPM and backlabelled neurons in infragranular layers of S1. (iv) Photograph illustrating the absence of dLGN neurons projecting to VPM. (v) Photograph displaying the backlabelled neurons in the intralaminar thalamus (yellow dotted box) after CTB injections into VPM. (vi) Photograph of the intralaminar thalamus at larger magnification of the area outlined by the dotted yellow box in (v) showing backlabelled neurons in the parafascicular nucleus (PF, yellow dotted box) after CTB injections into VPM. (vii) Larger magnification of the area outlined by the dotted yellow box in (vi) showing backlabelled neurons in PF. (b) Same as (a) for CTB injection into dLGN and backlabelled cells in superficial layers of the SC. (c) Assessment of projections from VPM to V1. (i) Schematic diagram displaying the site of FG injection into V1. (ii) Photograph displaying the FG injection confined to V1. (iii) Photograph of dLGN and VPM with a retrogradely labelled neuron in VPM. The inset shows the neuron at larger magnification in the area outlined by the dotted yellow box. APtN = anterior pretectal thalamic nucleus, PO = posterior nucleus.

Second, we investigated whether VPM projects not only to S1 but also to the V1, thereby relaying multisensory information from VPM to other primary sensory cortices7. For this, small amounts of the retrograde tracer fluorogold (FG) were injected into V1 and covered the supragranular (S), G, and infragranular (I) layers (Fig. 6c). FG injected into V1 stained few neurons in VPM. These results confirm the previously reported sparse feedforward VPM-V1 connections22.

Thus, these data suggest that the putatively unimodal tactile nucleus of the thalamus receives (multi-) sensory information from other thalamic nuclei, which is then further processed and sent to putatively unimodal primary somatosensory and visual cortices, bypassing the dLGN.



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IBM 000-N33 Exam (IBM i2 Intelligence Technical Mastery Test v1) Detailed Information



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