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Software Development, Software Engineering, Software Testing, Test Automation, Programming.
(targetPractice.categories.length) { return targetPractice.categories.every(c => sourcePractice1.categories.includes(c) || sourcePractice2.categories.includes(c)); } return true; } And here is the test with fast-check : describe('Merge two practices', function() { it('should merge categories of the | medium | 2,273 |
Software Development, Software Engineering, Software Testing, Test Automation, Programming.
source practices into the target practice based on random data', function() { fc.assert( fc.property( fc.array(fc.string()), fc.array(fc.string()), (cat1, cat2) => { const practice1 = { categories : cat1}; const practice2 = { categories : cat2}; const targetPractice = mergePractice(practice1, | medium | 2,274 |
Software Development, Software Engineering, Software Testing, Test Automation, Programming.
practice2); return noDuplicationsPredicate(targetPractice) && targetCategoriesShouldNotContainCategoriesNotInSourcePractices(practice1, practice2, targetPractice) }), { numRuns: 10000 }) }); }); In short: fc.assert runs the property, fc.property defines it, and fc.array(fc.string()) generates a | medium | 2,275 |
Software Development, Software Engineering, Software Testing, Test Automation, Programming.
random array of string values, possibly empty. We ask here to run 10,000 iterations of the test. Of course, the documentation of the framework will give your more information! After execution of our test (running with Mocha), we got the following error: 1) Merge two practices should merge | medium | 2,276 |
Software Development, Software Engineering, Software Testing, Test Automation, Programming.
categories of the source practices into the target practice: Error: Property failed after 1 tests { seed: 1361468347, path: "0:0:3:2:3:2", endOnFailure: true } Counterexample: [[],["",""]] See the counterexample? You could argue that such a case should not normally happen, as categories should | medium | 2,277 |
Software Development, Software Engineering, Software Testing, Test Automation, Programming.
never be null or empty, but this is not the point here; it's just an illustration of PBT :). We've got which inputs raise the error. Thanks to that, we can slightly edit our business function: function mergePractice(practice1, practice2) { return { categories: | medium | 2,278 |
Software Development, Software Engineering, Software Testing, Test Automation, Programming.
practice1.categories.concat(practice2.categories).filter(c => c && c !== "") }; } Next run, we realized that our business case did not cover duplicated categories : Error: Property failed after 161 tests { seed: -759575891, path: "160:1:0:4:6", endOnFailure: true } Counterexample: [["!"],["!"]] The | medium | 2,279 |
Software Development, Software Engineering, Software Testing, Test Automation, Programming.
mergePractice method should now be updated to avoid duplications. I think you've got it, right? PBT can complement your existing tests, and we showed an example of how this can help improve our codebase quality and the robustness of our tests. There are many more exciting features, such as | medium | 2,280 |
Software Development, Software Engineering, Software Testing, Test Automation, Programming.
Shrinking, which tries to simplify the understanding of a failing test by reducing the problem at its lowest level. In a future post, we'll discuss how mutation testing can also be relevant to improve our codebase. | medium | 2,281 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
Yao Lu 姚璐, Overlapping Waves and Lush Trees 重江叠嶂圖. 2007. Chromogenic print. Courtesy of the artist. Lisa Claypool discusses China’s ecological crises and how art can make a difference Chinese art and design historian and researcher Lisa Claypool opens a catalogue containing the works of artists on | medium | 2,282 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
display in ecoART China, a new online exhibit she is curating. Two pieces, laid out beside each other on opposite pages, seem like they belong to the same family of historical Chinese landscape paintings — think undulating green hills, cypress trees, mist, pagodas, and boats drifting along rivers | medium | 2,283 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
set into cracked parchment. But look closer and the image on the left isn’t a painting, but a photograph. The mountains in the photograph are actually heaps of garbage; the green isn’t grass and leaves, but nylon netting that conceals urban construction waste, and the mist is stained yellow. The | medium | 2,284 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
photograph, by Chinese artist Yao Lu 姚璐, challenges viewers to change their perspective, according to Claypool. “It creates a moment of discovery, of realization, that you’re actually looking at a devastated landscape, not a perfected, idyllic one,” she says. Claypool hopes viewers leave the | medium | 2,285 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
exhibit with many of these “moments of realization,” which she believes help “create an ethical shift in viewers by demanding we see our world in a different way.” It’s the same kind of experience she brings to the classroom, where she teaches history of Chinese visual art, design and visual | medium | 2,286 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
culture, along with a course in sustainable design in China, HADVC 216: China’s Design Revolution. I asked Claypool about the new exhibit, her sustainable design course and the environmental crisis in China. Both your sustainability-focused course and the ecoART exhibit are structured in a very | medium | 2,287 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
unique way. Can you tell us about that structure? Both the exhibit and the course are curated along the lines of an ancient Chinese philosophy that’s based in something called the five elemental phases: fire, earth, metal, water, and wood. The elements support each other; they generate each other; | medium | 2,288 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
they are part of a process of becoming, but they also damage each other. I wanted to use this correlative thinking to underscore the connectivity of the different ecological crises and also the connectivity between some of the problems we find in our local environment here in Edmonton and also in | medium | 2,289 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
Beijing. Each one of the participating artists in the exhibit is representing one of the elemental phases, or speaking to one of the ecological crises associated with each phase. In the course we’re looking at designers’ responses to crises having to do with each one of these phases, but I’m also | medium | 2,290 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
introducing new phases like “plastic” and “human beings.” What can students expect to learn in your sustainable design class? China has the reputation of being the factory of the world, and the question this course asks is: when will China stop being that factory and when will it start designing? | medium | 2,291 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
The truth is, that has happened already; it happened quite a while ago. We look at how environmentally- concerned designers of all types in China are approaching environmental problems in creative ways. For example, design projects like transforming quarried land into botanical gardens. There is a | medium | 2,292 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
willingness to radically experiment with sustainable design in China. In the city of Xi’an the state has erected a 61-meter tall air filter. It’s insane! It’s absolutely lunatic, but apparently it’s working. The problem of environmental degradation is so enormous there, and so visible. As a result, | medium | 2,293 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
there are large-scale efforts to fix some of these problems — and it’s inspiring. How do Chinese attitudes and perceptions of the environmental crisis differ from those in North America? In North America, as consumers, we don’t think about what happens to our stuff. But until recently that stuff | medium | 2,294 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
was ending up in China. There is a totally different perception about waste. For example, they have to think about how to turn waste into something else, turn it into a commodity, or maybe it already is a commodity and they’re going to turn it into something that can be sold. One reason I wanted | medium | 2,295 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
the ecoART exhibit to be focused around correlative thinking, is that I didn’t want to end up pointing fingers. Yeah, there’s a disaster in China. A lot of the problems all of us are dealing with are because of these crises in China. But we’re implicated in that, in every way. If we buy an Apple | medium | 2,296 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
computer, all of those toxins that are flowing into the environment through the production process to make an Apple computer, they’re flowing into the environment in China. That’s on us. Thinking about that interconnectivity is important. What role do you think visual art and design play in | medium | 2,297 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
addressing the environmental crisis? What is so powerful about art is that it has an ability to change the way that we perceive the world. And that is ethical; any time we see something differently, there’s an ethical shift that takes place. Artworks can encourage us to encounter our own spaces and | medium | 2,298 |
Industrial Design, Photography, Visual Art, China, Ecological Crisis.
our own worlds in a different way and to see them in a different way just by the kinds of demands that they make on the eye. It’s about ways of seeing. — Lisa Claypool teaches the history of Chinese art and design at the University of Alberta. She publishes regularly on Staring At The Ceiling, | medium | 2,299 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
What is quantum machine learning? How far away are we from tenable quantum machine learning systems? And what might quantum machine learning be used for? As a former quantum physicist turned artist, I am interested in the impacts of emerging technology. This article therefore explores the known | medium | 2,301 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
unknowns of quantum machine learning, as, at least to me, this area is really not clear at the moment. While I am not being particularly critical in this post, I hope to generate some discussion outside the quantum computing community about these new forms of technology and what they might mean for | medium | 2,302 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
us and the earth. After reviewing the literature, there seems to be three, or maybe four, options for a future quantum machine learning. The first option is using current machine learning algorithms to spot statistic patterns in quantum data. What is quantum data? It is data produced by quantum | medium | 2,303 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
physical systems. These are things like atoms, superconductors, lasers and molecules that need quantum mechanics for their physical description. If one collects data from these, current (non-quantum) machine learning techniques can analyse and interpret these quantum state-spaces up to a point, | medium | 2,304 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
which is useful for the development of quantum technologies and new materials. However, quantum physical systems can produce counter-intuitive patterns. That is, patterns that cannot be produced by everyday classical systems. It is very unlikely current classical machine learning algorithms will be | medium | 2,305 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
able to recognise quantum patterns. What we are talking about here is the ability to distinguish between patterns produced by large quantum physical systems like clusters of atoms and most other (classical) patterns like people walking in a city or the spread of viral content online. For what is | medium | 2,306 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
possible, the limitation will be the size of the neural networks you can run on existing super-computer clusters because the quantity of data from quantum systems grows at a much bigger rate than the data from non-quantum systems (even compared to really complex systems like earth’s climate). This | medium | 2,307 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
type of quantum machine learning system is already here, but in my mind it should not be considered quantum machine learning as no quantum computers are involved. The second option is a ‘hybrid’ model, where some of a classical (non-quantum) machine learning algorithm is outsourced to a quantum | medium | 2,308 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
computer. Here, the majority of the learning process takes place on a classical computer as it does now and only parts requiring a speed up are passed to a quantum computing chip. This means that bottle-neck routines on current supercomputers could be passed to a quantum computing chip and be run | medium | 2,309 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
more efficiently. Using this hybrid method, simulations of quantum systems, that are currently challenging to complete, such as the simulations of large complex molecules can be substantially sped up. Quantum algorithms such as the 2008 HHL algorithm for solving linear equations will assist in | medium | 2,310 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
providing the speed up. Scientists proposed this technique to solve the electronic states of molecules, which is useful for drug design and for new materials for energy storage and generation. This ‘hybrid’ technique may lead to more refined understandings of other complex systems such as our brain | medium | 2,311 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
and the climate. The downsides of a hybrid approach are the time and efficiency costs of passing data to and from the quantum computer. Quantum RAM or qRAM may be needed. Suitable hardware is a hugely limiting factor. Non-general quantum computers could be used instead of universal quantum | medium | 2,312 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
computers. These could be similar to D-Wave’s quantum computing system, which performs well only for specific tasks and are already available. As all outputs from the algorithm will be classical, it seem unlikely that it will spot patterns beyond classical machine learning algorithms. The third | medium | 2,313 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
option is to radically adapt and run existing machine learning algorithms on a quantum computer. I write ‘radically adapt’ as quantum computers run very differently compared to their classical counterparts. This is what some researcher have termed quantum machine learning (see for instance Xia and | medium | 2,314 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
Kais Nature Communications 2018). This approach is similar to the hybrid model, except the whole learning procedure runs on a quantum computer. Again both classical and quantum data can be used for the training set. The benefits of using a quantum computer arises when the learning utilises a | medium | 2,315 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
quantum speed up as in the ‘hybrid’ option. Since this approach adapts or rather quantizes existing machine learning algorithms, it is unclear whether it will be able to spot patterns not recognisable by classical computers, even though the output may be a quantum vector. This approach has been | medium | 2,316 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
theoretically proposed, but it is difficult to say when such systems will be put to use in efficient and cost effective ways. Hardware challenges are the main hurdle. Doing this depends on the ‘holy-grail’ development of a full scale quantum computer. While Google, IBM and co have been touting this | medium | 2,317 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
possibility for a while now, it seems to me they are currently aiming for a ‘dirty’ quantum computer. A ‘dirty’ quantum computer runs a quantum algorithms badly on a large number of qubits, which means they will be extremely error prone. So far the biggest device, announced on 11th December by | medium | 2,318 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
startup IonQ, apparently contains 160 error-prone qubits. Companies seem to be taking the dirty route because they are in a ‘space-age style race’ for ‘quantum supremacy’. That is, to create a quantum computer that computes something that is not possible on the biggest digital computer. However, it | medium | 2,319 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
is likely years if not decades before something meaningful comes out. With that said let us turn to my final option for a future quantum machine learning, which I’ll dub ‘genuine quantum machine learning’. Rather than simply speeding up existing algorithms, genuine quantum machine learning may | medium | 2,320 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
‘see’ the universe in completely different ways to how we do now. Given the nature of quantum mechanics, it is likely that a genuine quantum machine learning will access facets of reality that are seemingly off-limits to non-quantum systems, including us. A very tentative glance in this direction | medium | 2,321 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
was mentioned in Biamonte et al arxiv/1611.09347v2. There, the team argue that since quantum information processors can produce patterns that are computationally difficult to produce by classical computers, they should also be able to recognise patterns that are equally difficult to recognise | medium | 2,322 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
classically. As a practising artist, I would like to imagine what this might mean. I believe genuine quantum machine learning will explores the unknown-unknowns. When this occurs, we will be in a distant but plausible future where full scale quantum computers are up and running. New quantum | medium | 2,323 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
algorithms will be produced and tested with ease. Genuine quantum machine learning algorithms will be able to ‘see’ reality in fundamentally different ways to humans. It is doubtful whether our biological brains will be able to grasp the statistical patterns they find. And technically it may even | medium | 2,324 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
be impossible to convert the multi-dimensional and exponentially large quantum outputs into standard bit strings that we could then work with. Let us compare that to now. Currently, unsupervised classical machine learning algorithms can already recognise detailed statistical patterns humans are not | medium | 2,325 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
able to see. Frequently humans do not understand how deep learning algorithms make their predictions. But given those predictions, it is likely someone will understand what the outputs mean once they are analysed and tested. I believe this will change — at least for a while we’ll be entering a | medium | 2,326 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
quantum twilight zone. While quantum patterns are purported to exists mostly at small length and time scales and at cold temperatures, scientists have shown they also exist in biological systems like photosynthesising algae. Biological systems are large, warm and wet so finding quantum patterns | medium | 2,327 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
there is quite strange. Scientists also have theoretically shown that entropy lowering mechanisms may assist quantum patterns in manifesting at larger length scales and at higher temperatures. Therefore it is highly likely that genuine quantum machine learning will start to recognise quantum | medium | 2,328 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
patterns in places scientists never expected to find them. Genuine quantum machine learning algorithms will be able to spot hidden quantum signatures embedded within classical data, perhaps showing evidence for parallel universes or theories of quantum gravity. Maybe dark matter and dark energy | medium | 2,329 |
Quantum Computing, Speculative Design, Art, Artificial Intelligence, Science.
will come into focus. Quantum machine learning might provide new insights into individual and collective consciousness. It’s ability to spot and produce long range, non-local correlations suggests it may revolutionise natural language processing and other forms of communication synthesis and | medium | 2,330 |
Transitions, Sabbatical, Personal Development, Personal Growth, Life Lessons.
Download the framework here I spent my last year in a transition phase with two objectives: To figure out what I wanted to do next. To learn as much as possible about the process of transitioning, as I assume that I will go through many more transitions in the future. I’m happy to say that I hit my | medium | 2,332 |
Transitions, Sabbatical, Personal Development, Personal Growth, Life Lessons.
first goal and have figured out what I want to do next. And as part of the journey of wanting to become better at transitioning, I developed a structure that would guide me through this time of great uncertainty. At first, this framework was very, very basic. But then over time it became more | medium | 2,333 |
Transitions, Sabbatical, Personal Development, Personal Growth, Life Lessons.
refined and I realized that this might be helpful for others as well. So here it is for download (pdf, 13 pages, 213 kb). My objectives with this framework Identifying the key phases one goes through in a transition. Identifying key questions one can ask in a transition. Provide a structure that | medium | 2,334 |
Transitions, Sabbatical, Personal Development, Personal Growth, Life Lessons.
ultimately can make transitioning easier and provide a sense of stability and safety in a time where everything is in flux. 5 transition phases The framework is split into 5 phases: Start, Setup, Discovery, Test and Commitment. I assume that there is a good amount of overlap between these phases | medium | 2,335 |
Transitions, Sabbatical, Personal Development, Personal Growth, Life Lessons.
and that they mutually inspire each other. In reality most of these phases will run in parallel, but I think it helps for clarity purposes to lay them out separately and in a somewhat chronological order. Overview of the Framework, download the full pdf (13 pages) here. Download the Transition | medium | 2,336 |
Transitions, Sabbatical, Personal Development, Personal Growth, Life Lessons.
Framework here (pdf, 13 pages, 213kb) What comes next: developing an action guide While the framework attempts to identify some core question, these tend to be pretty broad and each section deserves more depth and inquiry than covered here. The next level for this framework will be to include an | medium | 2,337 |
Transitions, Sabbatical, Personal Development, Personal Growth, Life Lessons.
action guide for each section with concrete exercises, suggested reading and follow up material. If you’re interested in being updated about future versions, please leave you’re email here and I’ll message you once a new version is up. This is only v1 and I’d love your help to improve it Any | medium | 2,338 |
Transitions, Sabbatical, Personal Development, Personal Growth, Life Lessons.
feedback would be greatly appreciated — either through leaving a comment below, emailing me directly (my email is in the pdf) or joining our Facebook group for people in transition and sharing your thoughts there. My transition was at times quite challenging, but overall an incredibly enriching and | medium | 2,339 |
Transitions, Sabbatical, Personal Development, Personal Growth, Life Lessons.
powerful time and I hope that this might support your own transition in a tiny way. Best wishes & happy transitioning Fabian Thank you to Jennifer Lioy for her design help! This is part of my attempt to capture what I have learned during my last year of transition and to become better at | medium | 2,340 |
Transitions, Sabbatical, Personal Development, Personal Growth, Life Lessons.
“transitioning”: leaving a job/career/gig with no idea what will come next. You’ll find more stories and insights at www.transitionnotes.com. If you want to be notified of new transition related posts, sign up above. If you’re in transition or interested in learning more about transitioning, join | medium | 2,341 |
.
Advanced Micro Devices. Beast mode all year and I’ve watched a ton of bullish chirping and chattering all year on Stocktwits. For the year up over 500 percent. That’s better than momo darling Nvidia. This is a stock all the smartypants have been shorting all year and have been run over. Between the | medium | 2,347 |
Brain, Memory, Health, Human Brain, Neuroscience.
In new research, Boston University neuroscientist Dr. Steve Ramirez and collaborators examine the dynamic nature of fear responses in varied environments and their impacts. By Katherine Gianni In a world grappling with the complexities of mental health conditions like anxiety, depression, and PTSD, | medium | 2,350 |
Brain, Memory, Health, Human Brain, Neuroscience.
new research from Boston University neuroscientist Dr. Steve Ramirez and collaborators offers a unique perspective. The study, recently published in the Journal of Neuroscience, delves into the intricate relationship between fear memories, brain function, and behavioral responses. Dr. Ramirez, | medium | 2,351 |
Brain, Memory, Health, Human Brain, Neuroscience.
along with his co-authors Kaitlyn Dorst, Ryan Senne, Anh Diep, Antje de Boer, Rebecca Suthard, Heloise Leblanc, Evan Ruesch, Sara Skelton, Olivia McKissick, and John Bladon, explore the elusive concept of fear engrams, shedding light on the physical manifestation of memory in the brain. As Ramirez | medium | 2,352 |
Brain, Memory, Health, Human Brain, Neuroscience.
emphasizes, the initiative was led by Dorst and Senne, with the project serving as the cornerstone of Dorst’s PhD. Beyond its implications for neuroscience, their research marks significant strides in understanding memory formation and holds promise for advancing our comprehension of various | medium | 2,353 |
Brain, Memory, Health, Human Brain, Neuroscience.
behavioral responses in different situations, with potential applications in the realm of mental health. In this Q&A, Dr. Ramirez discusses the motivations, challenges, and key findings of the study. Dr. Steve Ramirez. Photo courtesy of Steve Ramirez. What motivated you and your research | medium | 2,354 |
Brain, Memory, Health, Human Brain, Neuroscience.
collaborators to study the influence of fear memories on behavior in different environments? The first thing is that with fear memories, it’s one of the most, if not the most, studied kind of memory in rodents. It’s something that gives us a quantitative, measurable behavioral readout. When an | medium | 2,355 |
Brain, Memory, Health, Human Brain, Neuroscience.
animal’s in a fearful state, we can begin looking at how its behavior has changed and mark those changes in behavior as like an index of fear. Fear memories in particular are our point because they lead to some stereotyped behaviors in animals such as freezing in place, which is one of many ways | medium | 2,356 |
Brain, Memory, Health, Human Brain, Neuroscience.
that fear manifests behaviorally in rodents. So, that’s one angle. The second angle is that fear is such a core component of a variety of pathological states in the brain — including PTSD, but also generalized anxiety, and even certain components of depression. There’s a very direct link between a | medium | 2,357 |
Brain, Memory, Health, Human Brain, Neuroscience.
fear memory and its capacity to evolve or devolve in a sense into a pathological state such as PTSD. It gives us a window into what’s going on in those instances as well. We studied fear because we can measure it predictably in rodents, and it has direct translational relevance in disorders | medium | 2,358 |
Brain, Memory, Health, Human Brain, Neuroscience.
involving dysregulated fear responses as well. Can you explain what fear engrams are and how you used optogenetics to reactivate them in the hippocampus? An engram is this elusive term that generally means the physical manifestation of memory. So, whatever memory’s physical identity is in the | medium | 2,359 |
Brain, Memory, Health, Human Brain, Neuroscience.
brain, that’s what we term an engram. The overall architecture in the brain that supports the building that is memory. I say elusive because we don’t really know what memory fully looks like in the brain. And we definitely don’t know what an engram looks like. But, we do have tips of the iceberg | medium | 2,360 |
Brain, Memory, Health, Human Brain, Neuroscience.
kind of hints that for the past decade, we’ve been able to really use a lot of cutting edge tools in neuroscience to study. In our lab, we’ve made a lot of headway in visualizing the physical substrates of memories in the brain. For instance, we know that there’s cells throughout the brain. It’s a | medium | 2,361 |
Brain, Memory, Health, Human Brain, Neuroscience.
3D phenomenon distributed throughout the brain but there’s cells throughout the brain that are involved in the formation of a given memory such as a fear memory and that there’s areas of the brain that are particularly active during the formation of a memory. What were the main findings about | medium | 2,362 |
Brain, Memory, Health, Human Brain, Neuroscience.
freezing behavior in smaller versus larger environments during fear memory reactivation? It’s thankfully straightforward and science is often anything but. First, if we reactivate this fear memory when the animals are in a small environment, then they’ll default to freezing–they stay in place. This | medium | 2,363 |
Brain, Memory, Health, Human Brain, Neuroscience.
is presumably an adaptive response so as to avoid detection by a potential threat. We think the brain has done the calculus of, can I escape this environment? Perhaps not. Let me sit in a corner and be vigilant and try to detect any potential threats. Thus, the behavior manifests as freezing. The | medium | 2,364 |
Brain, Memory, Health, Human Brain, Neuroscience.
neat part is that in that same animal, if we reactivate the exact same cells that led to freezing in the small environment, everything is the exact same: the cells that we’re activating, the fear memory that it corresponds to, the works. But, if we do that in a large environment, then it all goes | medium | 2,365 |
Brain, Memory, Health, Human Brain, Neuroscience.
away. The animals don’t freeze anymore. If anything, a different repertoire of behaviors emerge. Basically, they start doing other things that is just not freezing. This was the initial take home for us. When we reactivate the fear memory up, or artificially, when we do that in the small | medium | 2,366 |
Brain, Memory, Health, Human Brain, Neuroscience.
environment, they freeze, when we do that in the large environment, they don’t freeze. What was cool for us about that finding in particular was that it means that these fear memory cells are not hardwired to produce the same exact response every single time they’re reactivated. At some point, the | medium | 2,367 |
Brain, Memory, Health, Human Brain, Neuroscience.
brain determines, “I’m recalling a fear memory and now I have to figure out what’s the most adaptive response.” Dr. Ramirez believes that a small structure in the brain could hold the keys to future therapeutic techniques for treating depression, anxiety, and PTSD, someday allowing clinicians to | medium | 2,368 |
Brain, Memory, Health, Human Brain, Neuroscience.
enhance positive memories or suppress negative ones. Video courtesy of Boston University. Were there any challenges or obstacles you encountered during the research process, and how did you overcome them? There’s a couple. The first is that the behavior, ironically enough, was reasonably | medium | 2,369 |
Brain, Memory, Health, Human Brain, Neuroscience.
straightforward for us to reproduce and to do again and again and again–so that we were convinced that there was some element of truth there. In the second half of the study, and the one that takes up the most space in the paper, was figuring out what in the brain is mediating this difference. As | medium | 2,370 |
Brain, Memory, Health, Human Brain, Neuroscience.
we observed, the animals are freezing when we artificially activate a memory in a small environment, and they’re not freezing in the large environment. But, we’re activating the same cells. So, what is different about the animal’s brain state? What is the animal’s brain state when we’re | medium | 2,371 |
Brain, Memory, Health, Human Brain, Neuroscience.
reactivating this memory in the small environment compared to the large environment? Clearly it’s manifesting as totally opposite behaviors–freezing and lack thereof. We wanted to find out what in the brain is happening in those two conditions that are different. That led us down a multi-year | medium | 2,372 |
Brain, Memory, Health, Human Brain, Neuroscience.
rabbit hole of trying to map out activity patterns in the entire brain, as a result of stimulating these memories in these different sized environments. We went through a whole mess of technologies where we looked at the brain–we can actually make the brain completely transparent–so that we can | medium | 2,373 |
Brain, Memory, Health, Human Brain, Neuroscience.
take fancy microscopes and image the brain in three dimensions. Think of it as a cellular MRI for rodents. We created these brain wide maps of what’s responsive in the brain when we stimulate a memory. Then we asked ourselves, how does that map of the brain in the small environment compare to the | medium | 2,374 |
Brain, Memory, Health, Human Brain, Neuroscience.
map of the brain when we’re activating the memory in the large environment? In short, there’s similarities and there’s differences. That there’s certain parts of the brain that are always active when we stimulate a memory, regardless of the environments that the animals are in. But, then there’s | medium | 2,375 |
Brain, Memory, Health, Human Brain, Neuroscience.
other parts that are only active in the large environment or only active when we do the experiment in the small environment. That’s neat because that lets us know that those areas that are not in common between the two might be the ones that are actually important in mediating the brain’s decision | medium | 2,376 |
Brain, Memory, Health, Human Brain, Neuroscience.
to either freeze or to not freeze. However, this process was challenging because it required a lot of technical prowess such as making brains transparent and imaging them in three dimensions down at the cellular level. Photo by Robina Weermeijer on Unsplash. How might the insights from this | medium | 2,377 |
Brain, Memory, Health, Human Brain, Neuroscience.
research be applied or extended in the future, particularly in the context of understanding and treating fear-related disorders? Context clearly matters. One relatable example is that two people might be experiencing the same level of anxiety, but the underlying reason for that anxiety might be | medium | 2,378 |
Brain, Memory, Health, Human Brain, Neuroscience.
wildly different across the two people. The ways that anxiety affects the people behaviorally may also be very different. One person might be pacing up and down the room, whereas the other one is just kind of sitting and lost in their own thoughts. The same faculty of cognition can appear two very | medium | 2,379 |
Brain, Memory, Health, Human Brain, Neuroscience.
different ways, in how it’s expressed. In this case, we think it’s the same thing with fear memories — how they’re expressed will depend on what the animal is experiencing. Perhaps in people, how a given memory is expressed also is going to depend on the context — the who’s there, the what, where, | medium | 2,380 |
Brain, Memory, Health, Human Brain, Neuroscience.
why, and so on. So that’s one angle, but I think that the more direct relevance is that we’ve known for a decade that these cells in the hippocampus are enough to jumpstart a memory when we reactivate them. But then there’s the question of, what happens if we reactivate them, and we change up more | medium | 2,381 |
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