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Life is full of processes to learn and then relearn when they become more elaborate. One day you log in to an app with just a password, then the next day you also need a code texted to you. One day you can just pop your favorite microwavable lunch into the oven for six straight minutes, but then the packaging changes and you have to cook it for three minutes, stir, and then heat it for three more. Our brains need a way to keep up. A new study by neuroscientists at The Picower Institute for Learning and Memory at MIT reveals some of the circuitry that helps a mammalian brain learn to add steps.
In Nature Communications the scientists report that when they changed the rules of a task, requiring rats to adjust from performing just one step to performing two, a pair of regions on the brain's surface, or cortex, collaborated to update that understanding and change the rats' behavior to fit the new regime. The anterior cingulate cortex (ACC) appeared to recognize when the rats weren't doing enough and updated cells in the motor cortex (M2) to adjust the task behavior.
"I started this project about 7 or 8 years ago when I wanted to study decision making." said Daigo Takeuchi, a researcher at the University of Tokyo who led the work as a postdoc at the RIKEN-MIT Laboratory for Neural Circuit Genetics at The Picower Institute directed by senior author and Picower Professor Susumu Tonegawa. "New studies were finding a role for M2. I wanted to study what upstream circuits were influencing this."
Tripping up the second step
Takeuchi and Tonegawa traced neural circuit connections that led into M2 and found that many originated in the ACC. They began to see the ACC's role in guiding M2's sequential decisions when they instilled a genetic manipulation in ACC cells that allowed them to suppress their activity. This "chemogenetic" disabling of the ACC had a very specific effect. When the task rules changed so that instead of having to poke their snout into just one hole to gain a little reward, rats had to poke their nose into a sequence of two holes, the rodents with silenced ACCs took much longer to realize the rule change. Compared to rats with normal ACC activity, they failed for much longer to realize the second poke was necessary. Rats had no trouble, however, going from two steps back to just one, regardless of whether their ACC was silenced.
When the scientists chemogenetically silenced the ACC cells' terminals in M2, they got the same results as silencing the ACC overall. They also silenced other areas of the cortex, but doing that didn't affect the ability of the rats to notice and adjust to the rule switch. Together these manipulations confirmed that it was specifically the ACC's connections with M2 that help the rats notice and adjust to the one-step-to-two-step change.
But what effect does the ACC have in M2? Takeuchi and his co-authors measured the electrical activity of cells in M2 as the rats played their nose-poking, rule-changing game. They found that many cells were particularly activated by different task rules (i.e. one-step or two-steps). When they silenced the ACC, though, that suppressed this rule selectivity.
Within M2 Takeuchi and the team also noticed populations of neurons that responded preferentially to positive outcomes (reward for doing the task right) and negative outcomes (not getting a reward for doing the task wrong). They found that when they silenced the ACC, this actually increased the activity of the negative-outcome encoding neurons during negative feedback, particularly for the first 10-20 rounds after the rules changed from one step to two. This correlated strongly with the timing, or "epoch," of the rats' worst performance.
"It seems likely the epoch-specific disruption of animals' second-choice performance is associated with the excessive enhancement of the activity of negative outcome activated neurons caused by the ACC silencing," they wrote in the study.
The team further confirmed that the feedback, or outcomes, stage mattered by using a different technique to silence the ACC. By engineering ACC neurons to be suppressed by flashes of light (a technique called "optogenetics") they could precisely control when the ACC went offline. They found that if they did so after the rats made an incorrect choice when the rules switched from one poke to two, they could cause the rats to continue to err. Optogenetic silencing of the ACC after rats made a correct choice didn't undermine their subsequent behavior.
"These results indicate that ACC neurons process error feedback information following an erroneous second response and use this information to adjust the animal's sequential choice responses in subsequent trials," they wrote.
Too high a threshold
The evidence painted a clear picture: When the rats needed to notice that an extra step was now required, the ACC's job was to learn from negative feedback and signal M2 to take the second step. If the ACC wasn't available when feedback was provided, then M2 cells that emphasize negative outcomes apparently would become especially active and the rats would fail to do the required second step for a time before finally catching on.
Why would less ACC activity somehow increase the negative outcome encoding cells' activity in M2? Takeuchi hypothesizes that what the ACC is actually doing is stimulating inhibitory cells in M2 that normally modulate the activity of those cells. With ACC activity reduced, the negative outcome encoding M2 cells experience less inhibition. The behavioral result, he theorizes, is that the rats therefore require more evidence than they should of the rule change. The mechanism isn't completely clear, Takeuchi acknowledged, but the rats apparently need more time to experience outcome feedback from making the right decision of taking a second step before they'll become convinced that they are on the right track doing so.
Takeuchi said that while the results demonstrate the circuit necessary for adapting to a rule change requiring more steps in a process, it also raises some interesting new questions. Is there another circuit for noticing when a multi-step process has become a one-step process? If so, is that circuit integrated with the one discussed in this study? And if the threshold model is the right one, how exactly is it working?
The implications not only matter for understanding the neural basis of natural sequential decisions but might also for AI applications ranging from game playing or industrial work, each of which can involve tasks with multiple steps.
In addition to Takeuchi and Tonegawa, the study's other authors are Dheeraj Roy, Shruti Muralidhar, Takashi Kawai, Andrea Bari, Chanel Lovett, Heather Sullivan and Ian Wickersham.
The RIKEN Center for Brain Science, the Howard Hughes Medical Institute, the JPB Foundation and the Human Frontier Science Program Fellowship provided funding for the study.
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Data streaming platform operator Confluent has appointed Kamal Brar to the role of senior vice president for the APAC region.
"Confluent is committed to providing world-class customer experiences in every region. Kamal is an influential leader primed to empower our teams to embody that commitment, while his smart, strategic approach will guide our success as we grow in a diverse region," said Confluent chief revenue officer Larry Shurtz.
Brar brings to Confluent two decades of enterprise technology strategic leadership.
He joins the company from Rubrik, where he worked as APJ vice president and general manager.
Brar previously held broadly similar roles at Hortonworks, Talend, MongoDB, Oracle and MySQL.
Earlier in his career he worked in sales at HP (following its acquisition of Outerbay Technologies) and Oracle, and in network design at Optus.
He is also a board advisor to SingleStore.
Brar holds a bachelor's degree in computing from Macquarie University and a master's in technology management from the University of New South Wales.
"Confluent is a market leader in the data streaming space, addressing mission-critical problems that drive massive business impact. I'm thrilled to become part of an outstanding team that helps businesses unlock the possibilities of real-time data," he said.
"One of my goals will be getting to know our customers and the innovative use cases they're deploying the platform to deliver true time to value. Our continued commitment to delighting our customers and delivering a world class experience is top of mind, and we will continue to invest further in our support and services capabilities for the APAC region.
"In addition, our partners play a pivotal role in building our presence in the region and we will look to double-down on focused partners to help us accelerate our revenue growth while supporting the Kafka eco-system."
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DUBLIN, Aug. 3, 2022 /PRNewswire/ -- The 'Data as a Service Market by Enterprise, Industrial, Public, and Government Data Applications and Services 2022 - 2027' report has been added to ResearchAndMarkets.com's offering.
Select Report Findings:
North America and Western Europe represent the two largest regional markets for DaaS
IoT DaaS is growing nearly three times as fast as non-IoT DaaS, with much of its streaming data
Structured data market remains greater than unstructured, but the latter will overtake the former
Machine-sourced data is growing twice as fast as non-machine data, largely due to IoT apps and services
Analytics as a Service is the largest opportunity and also one of the fastest-growing segments through 2027
The DaaS market will receive a huge boost in both usage and revenue from edge computing and real-time data analytics
Corporate data syndication will become a major driver of DaaS growth, but data security and privacy challenges will limit the expansion
There is considerable competition in the market, happening at a variety of different levels, with features highly variable between vendors. This causes confusion for the enterprise and causes them to often choose two or more providers. Barriers to enterprise adoption of the DaaS model include security concerns, reliability, regulation, vendor lock-in/interoperability, IT management overhead, and other costs.
However, the reasons for implementing DaaS far outweigh the concerns, especially when it comes to IoT data, which must have flexible and scalable platforms for storage, processing, and distribution. Accordingly, enterprise organizations are five times more likely to implement DaaS for machine-generated IoT data than for static data located in corporate repositories or data lakes. The DaaS market must support both static and dynamic data, but the latter will benefit significantly more, especially as edge computing is implemented and real-time data is available.
Another important opportunity area for DaaS is enterprise data syndication, which is the opportunity for companies of various sizes to syndicate (e.g. share and monetize) their data. This is one of the biggest opportunities for the Data as a Service market as a whole. However, there remain challenges above and beyond the core adoption barriers, which include specific security, privacy, and care of custody concerns.
Barriers and Challenges to DaaS Adoption
Enterprises Reluctance to Change
Responsibility of Data Security Externalized
Complexity is a Deterrent
Lack of Cloud Interoperability
Service Provider Resistance to Audits
Viability of Third-party Providers
No Move of Systems and Data is without Cost
Lack of Integration Features in the Public Cloud = Reduced Functionality
Data as a Service Market Segmentation
DaaS by Sector: Public, Business, and Government Data
DaaS by Data Collection Type: IoT Data and Non-IoT Data
DaaS by Data Source Type: Machine Data and Non-machine Data
DaaS by Data Structure Type: Structured Data and Unstructured Data
Key subjects Covered:
1.0 Executive Summary
2.0 Data as a Service Technologies
3.0 Data as a Service Market Advantages, Use Cases, and Framework
4.0 Data as a Service Market
5.0 Data as a Service Strategies
6.0 Data as a Service Applications
7.0 Market Outlook and Future of DaaS
8.0 Data as a Service Market Analysis and Forecasts 2022 - 2027
9.0 Regional DaaS Market Analysis and Forecasts 2022 - 2027
10.0 Conclusions and Recommendations
3i Data Scraping
Alibaba Group Holding Limited
Amazon Web Services, Inc.
Bloomberg Finance L.P.
Cisco Systems Inc.
DC Frontiers Pte. Ltd.
Dow Jones & Company, Inc.
Hewlett Packard Enterprise
HG Data Company
Hitachi Data Systems
Information Builders Inc.
Information Resources, Inc
MapR Technologies Inc
Mighty AI, Inc.
Nielsen Holdings Plc
Snapshot (On Demand)
The Dun & Bradstreet Corporation
The Weather Company, LLC
Thomson Reuters Corp.
TIBCO Software Inc
Verizon Communications, Inc.
Wisers Information Limited
For more information about this report visit https://www.researchandmarkets.com/r/iqui2s
Research and Markets
Laura Wood, Senior Manager
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SOURCE Research and Markets
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