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Every day, people are constantly learning and forming new memories. When you pick up a new hobby, try a recipe a friend recommended, or read the latest world news, your brain stores many of these memories for years or decades. But how does your brain achieve this incredible feat? In our newly published research in the journal Science, we have identified some of the rules the brain uses to learn. Learning in the brain The human brain is made up of billions of nerve cells. These neurons conduct electrical pulses that carry information, much like how computers use binary code to carry data. These electrical pulses are communicated with other neurons through connections between them called synapses. Individual neurons have branching extensions known as dendrites that can receive thousands of electrical inputs from other cells. Dendrites transmit these inputs to the main body of the neuron, where it then integrates all these signals to generate its own electrical pulses. It is the collective activity of these electrical pulses across specific groups of neurons that form the representations of different information and experiences within the brain. For decades, neuroscientists have thought that the brain learns by changing how neurons are connected to one another. As new information and experiences alter how neurons communicate with each other and change their collective activity patterns, some synaptic connections are made stronger while others are made weaker. This process of synaptic plasticity is what produces representations of new information and experiences within your brain. In order for your brain to produce the correct representations during learning, however, the right synaptic connections must undergo the right changes at the right time. The rules that your brain uses to select which synapses to change during learningwhat neuroscientists call the credit assignment problemhave remained largely unclear. Defining the rules We decided to monitor the activity of individual synaptic connections within the brain during learning to see whether we could identify activity patterns that determine which connections would get stronger or weaker. To do this, we genetically encoded biosensors in the neurons of mice that would light up in response to synaptic and neural activity. We monitored this activity in real time as the mice learned a task that involved pressing a lever to a certain position after a sound cue in order to receive water. We were surprised to find that the synapses on a neuron dont all follow the same rule. For example, scientists have often thought that neurons follow what are called Hebbian rules, where neurons that consistently fire together, wire together. Instead, we saw that synapses on different locations of dendrites of the same neuron followed different rules to determine whether connections got stronger or weaker. Some synapses adhered to the traditional Hebbian rule where neurons that consistently fire together strengthen their connections. Other synapses did something different and completely independent of the neurons activity. Our findings suggest that neurons, by simultaneously using two different sets of rules for learning across different groups of synapses, rather than a single uniform rule, can more precisely tune the different types of inputs they receive to appropriately represent new information in the brain. In other words, by following different rules in the process of learning, neurons can multitask and perform multiple functions in parallel. Future applications This discovery provides a clearer understanding of how the connections between neurons change during learning. Given that most brain disorders, including degenerative and psychiatric conditions, involve some form of malfunctioning synapses, this has potentially important implications for human health and society. For example, depression may develop from an excessive weakening of the synaptic connections within certain areas of the brain that make it harder to experience pleasure. By understanding how synaptic plasticity normally operates, scientists may be able to better understand what goes wrong in depression and then develop therapies to more effectively treat it. These findings may also have implications for artificial intelligence. The artificial neural networks underlying AI have largely been inspired by how the brain works. However, the learning rules researchers use to update the connections within the networks and train the models are usually uniform and also not biologically plausible. Our research may provide insights into how to develop more biologically realistic AI models that are more efficient, have better performance, or both. There is still a long way to go before we can use this information to develop new therapies for human brain disorders. While we found that synaptic connections on different groups of dendrites use different learning rules, we dont know exactly why or how. In addition, while the ability of neurons to simultaneously use multiple learning methods increases their capacity to encode information, what other properties this may give them isnt yet clear. Future research will hopefully answer these questions and further our understanding of how the brain learns. William Wright is a postdoctoral scholar in neurobiology at the University of California, San Diego. Takaki Komiyama is a professor of neurobiology at the University of California, San Diego. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Just a few months into Donald Trumps second term, are the manosphere influencers who championed him already starting to backpedal? In a recent episode of The Joe Rogan Experience, host Joe Rogan raised concerns about the presidents decision to send undocumented immigrants directly to El Salvadors mega-prisonswithout trial, lawyers, or, as critics argue, any semblance of due process. “What if you are an enemy of, lets not say any current president. Lets pretend we got a new president, totally new guy in 2028, and this is a common practice now of just rounding up gang members with no due process and shipping them to El Salvador, youre a gang member. No, Im not. Prove it. What? I got to go to court. No. No due process,” said Rogan. We gotta be careful we dont become monsters, while fighting monsters. For those who had been sounding the alarm during Trumps campaign, it was a painful watch. Watching Joe Rogan figure this shit out in real time is painful, one commenter wrote. That ol Even a broken clock is right twice a day idiom comes to mind, another added. As one Reddit comment pointed out, Why does he need to use a hypothetical president to make this point? This entire commentary describes the current administration. View this post on Instagram A post shared by The Tennessee Holler (@thetnholler) This election cycle, Trump owes at least part of his victory to Rogan and other manosphere influencers who endorsed him. After hosting the now-president on The Joe Rogan Experiencein what became one of the most-watched podcast episodes of all time, with 58 million views at the time of writingRogan followed up with a full-throated endorsement just one day before the 2024 election. Are we now seeing the first cracks appear? Rogan isnt the only vocal Trump supporter expressing unease in recent weeks. Barstool Sports founder Dave Portnoy, who publicly backed Trump during the campaign, voiced frustration after the presidents rollout of sweeping tariffs sent markets into a nosedive. Portnoy claimed he lost $7 million in the aftermath. So, Trump rolls out the tariffs, right? Portnoy said in a livestream posted April 7. This is a decision that one guy made that crashed the whole stock market. Thats why were calling it Orange Monday and not Black Monday. Just days earlier, Portnoy had reaffirmed his support for Trump. I voted for Trump, I think hes a smart guy, he said in a clip. I also think hes playing a high-stakes game here. Im gonna roll with him for a couple days, a couple weeks, see how this pans out. By Monday, he said his estimated losses had climbed to $20 million.
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The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. Across industries, a new era of climate innovation is accelerating. The momentum is visible in the data: Global clean energy investment surpassed $2 trillion for the first time in 2024, double the amount invested in fossil fuels. While solar panels, wind turbines, and grid-connected batteries often grab the headlines, the low carbon economy is growing in far more corners than many realize. Since founding Supercool last summer to cover proven and scaling climate solutions, Ive seen needle-moving innovation accelerating across farms, factories, and finance departments. One sector in particular shows remarkable progressthe built environment, which accounts for 34% of global carbon emissions. From hard tech and material breakthroughs to AI-powered intelligence to novel business models, here are three approaches to decarbonizing buildings happening now. 1. Hardtech innovation: Build with carbon-negative materials The engineered materials we use to build our suburbs and citiesprimarily timber, concrete, and steelcreate a lot of carbon emissions in their manufacture. Concrete and steel account for nearly 18% of global greenhouse gas emissions. Wood-based materials like oriented strand board (OSB), which are commonly installed in new homes, generate most of their manufacturing carbon emissions from burning wood to generate heat during production. Plantd transforms the built environment using carbon-negative building materials derived from alternative biomassa hardy, fast-growing grass. Four years ago, I cofounded the company with two former SpaceX engineers. To realize its ambitions, Plantd established a new agricultural supply chain innovating at every step, from building an in-house tissue culture lab to establishing full-scale greenhouse operations to supplying commercial farmers with the companys proprietary grass. [Photo: Courtesy of Plantd] Why grass? Because it grows incredibly fast, like bamboo, rapidly removing atmospheric carbon in the process, and possesses the structural characteristics to be transformed into durable engineered building materials. Yet, the key to sequestering carbon in our materials is Plantds manufacturing technology. Our team pioneered a modular, electric-powered production line that turns grass into finished products that replace plywood and OSB in new home construction. Its a first-of-its-kind technology that distinguishes a Plantd production facility from every other engineered wood facility in the world; ours is the only one without a smoke stack on top of the building. This past fall, D.R. Horton, the largest homebuilder in America, which builds about one in every 10 U.S. homes, ordered 10 million Plantd panels, enough to form the walls and roofs of 90,000 new single-family homes. 2. Software innovation: Give buildings brains An even bigger source of building-related carbon emissions is the energy required to operate them. Globally, this accounts for 26% of all greenhouse gas emissions. The top culprit: HVAC systems. The heating, cooling, and ventilation equipment needed to keep us comfortable indoors are responsible for about 35% of all energy used in U.S. buildings. The challenge is that thermostats, even the smart ones, arent very bright. They can track whats already happened and react to whats happening right now, but they cannot anticipate changes in weather, occupancy, carbon intensity of the grid, and energy costs. BrainBox AI can. Using AI-powered intelligence, its cloud-based control system connects to the hundreds, sometimes thousands, of HVAC components in a building and sends them real-time instructions. The companys platform provides over 15,000 buildings worldwidefrom Nordstrom to Family Dollarwith the intelligence to see six hours into the future with 96% accuracy. By knowing the future, BrainBox AI cuts energy, costs, and carbon emissions and improves comfort. Its an easy-to-install solution that works with existing systems and equipment. The results? HVAC-related emissions reductions of up to 40% and energy savings as high as 25%. 3. Finance innovation: Make efficiency upgrades free Many buildings are stuck with legacy equipment that gets the job done but consumes far more energy than their more efficient modern counterparts. Yet, new equipment can cost hundreds of thousands of dollars, often placing upgrades out of reach. Budderfly has built one of the fastest-growing businesses in America by removing the cost barrier. The company identifies energy-intensive businesses like fast food chains and offers them a deal that sounds almost too good to be true: free upgrades to energy-efficient systems, including HVAC, lighting, refrigeration, and secrity. Budderfly foots the bills and shares the monthly energy savings with its customers. Scale is key to making this business model work. Budderfly has raised nearly $1 billion to pay for the equipment it installs in customer locations. Its rapid expansion enables it to secure preferential pricing from global equipment suppliers that individual owners and franchisees could never obtain independently. Budderfly also takes over billing, which is one less thing for customers to worry about, and gives the company a trove of data to drive further energy reductions and cost savings. From Taco Bell to McDonalds to Sonic, clients are guaranteed to see savings from day one. In 2024, Budderfly generated $200 million in revenue and now operates in more than 7,000 locations nationwide. Its customers collective energy use dropped 43% last year. The takeaway Whether its growing new materials, giving buildings the ability to think ahead, or reimagining who pays for energy systems, the low carbon economy isnt just coming someday. Its already being built. Josh Dorfman is the CEO and host of Supercool.
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