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compneuropapers · 9 months

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Interesting Papers for Week 30, 2023

Adult-born neurons inhibit developmentally-born neurons during spatial learning. Ash, A. M., Regele-Blasco, E., Seib, D. R., Chahley, E., Skelton, P. D., Luikart, B. W., & Snyder, J. S. (2023). Neurobiology of Learning and Memory, 198, 107710.

Behavioral origin of sound-evoked activity in mouse visual cortex. Bimbard, C., Sit, T. P. H., Lebedeva, A., Reddy, C. B., Harris, K. D., & Carandini, M. (2023). Nature Neuroscience, 26(2), 251–258.

Exploration patterns shape cognitive map learning. Brunec, I. K., Nantais, M. M., Sutton, J. E., Epstein, R. A., & Newcombe, N. S. (2023). Cognition, 233, 105360.

Distinct contributions of ventral CA1/amygdala co-activation to the induction and maintenance of synaptic plasticity. Chong, Y. S., Wong, L.-W., Gaunt, J., Lee, Y. J., Goh, C. S., Morris, R. G. M., … Sajikumar, S. (2023). Cerebral Cortex, 33(3), 676–690.

An intrinsic oscillator underlies visual navigation in ants. Clement, L., Schwarz, S., & Wystrach, A. (2023). Current Biology, 33(3), 411-422.e5.

Not so optimal: The evolution of mutual information in potassium voltage-gated channels. Duran-Urriago, A., & Marzen, S. (2023). PLOS ONE, 18(2), e0264424.

Successor-like representation guides the prediction of future events in human visual cortex and hippocampus. Ekman, M., Kusch, S., & de Lange, F. P. (2023). eLife, 12, e78904.

Residual dynamics resolves recurrent contributions to neural computation. Galgali, A. R., Sahani, M., & Mante, V. (2023). Nature Neuroscience, 26(2), 326–338.

Dorsal attention network activity during perceptual organization is distinct in schizophrenia and predictive of cognitive disorganization. Keane, B. P., Krekelberg, B., Mill, R. D., Silverstein, S. M., Thompson, J. L., Serody, M. R., … Cole, M. W. (2023). European Journal of Neuroscience, 57(3), 458–478.

A striatal circuit balances learned fear in the presence and absence of sensory cues. Kintscher, M., Kochubey, O., & Schneggenburger, R. (2023). eLife, 12, e75703.

Hippocampal engram networks for fear memory recruit new synapses and modify pre-existing synapses in vivo. Lee, C., Lee, B. H., Jung, H., Lee, C., Sung, Y., Kim, H., … Kaang, B.-K. (2023). Current Biology, 33(3), 507-516.e3.

Neocortical synaptic engrams for remote contextual memories. Lee, J.-H., Kim, W. Bin, Park, E. H., & Cho, J.-H. (2023). Nature Neuroscience, 26(2), 259–273.

The effect of temporal expectation on the correlations of frontal neural activity with alpha oscillation and sensory-motor latency. Lee, J. (2023). Scientific Reports, 13, 2012.

Describing movement learning using metric learning. Loriette, A., Liu, W., Bevilacqua, F., & Caramiaux, B. (2023). PLOS ONE, 18(2), e0272509.

The geometry of cortical representations of touch in rodents. Nogueira, R., Rodgers, C. C., Bruno, R. M., & Fusi, S. (2023). Nature Neuroscience, 26(2), 239–250.

Contextual and pure time coding for self and other in the hippocampus. Omer, D. B., Las, L., & Ulanovsky, N. (2023). Nature Neuroscience, 26(2), 285–294.

Reshaping the full body illusion through visuo-electro-tactile sensations. Preatoni, G., Dell’Eva, F., Valle, G., Pedrocchi, A., & Raspopovic, S. (2023). PLOS ONE, 18(2), e0280628.

Experiencing sweet taste is associated with an increase in prosocial behavior. Schaefer, M., Kühnel, A., Schweitzer, F., Rumpel, F., & Gärtner, M. (2023). Scientific Reports, 13, 1954.

Cortical encoding of rhythmic kinematic structures in biological motion. Shen, L., Lu, X., Yuan, X., Hu, R., Wang, Y., & Jiang, Y. (2023). NeuroImage, 268, 119893.

Mindful self-focus–an interaction affecting Theory of Mind? Wundrack, R., & Specht, J. (2023). PLOS ONE, 18(2), e0279544.

#science #Neuroscience #computational neuroscience #Brain science #research #cognition #neurons #cognitive science #neural networks #neural computation #neurobiology #psychophysics #scientific publications

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prokopetz · 2 years

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One of the perennial problems with deep learning models like DALL-E is that if you train them too well, eventually they start precisely reproducing material from their training data set that just happens to match whatever criteria they’re given.

Given that these models are a. trained on random images scraped in bulk from the Internet, largely without human curation, and b. being touted as a potential substitute for human artists in certain commercial applications, I’m just waiting for the inevitable lawsuit where one of these models spits out an exact copy of some reasonably well-known piece of art, that copy is used in a commercial publication whose author is unaware of what the model has done, and some poor judge has to rule on whether an AI can commit plagiarism.

#computers #technology #ai #neural networks #dall-e #art #law #intellectual property #plagiarism #the stupidest cyberpunk future

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slack-wise · 2 months

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#memes #AI #generative #llm #llms #tech #computers #gan #neural networks #funny #lol

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the-awesomecosmos-studies · 2 months

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Day 19/100 days of productivity | Fri 8 Mar, 2024

Visited University of Connecticut, very pretty campus

Attended a class on Computer Vision, learned about Google ResNet, which is a type of residual neural network for image processing

Learned more about the grad program and networked

Journaled about my experience

Y’all, UConn is so cool! I was blown away by the gigantic stadium they have in the middle of campus (forgot to take a picture) for their basketball games, because apparently they have the best female collegiate basketball team in the US?!? I did not know this, but they call themselves Huskies, and the branding everyone on campus is on point.

#100 days of productivity #grad school #computer vision #google resnet #neural network #deep learning #UConn #uconn huskies #uconn women’s basketball #university of Connecticut

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vampiremansionpoolboy-moved · 1 year

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going insane over the fact that happiness and care and concern and love is underneath every interaction between newt and hermann in pacific rim

#HEAR ME OUT. they’re introduced and newt and being a groupie and behind him hermann is all huffing and rolling his eyes and shaking his #head but he’s Not Angry. no. he jumps to defend newt albeit in a somewhat mocking and sarcastic way BUT THE THOUGHT IS THERE. and then when #hermann is rambling on about numbers being the handwriting of god newt is in the background smiling and laughing and making silly #hand motions and yes the hand motion was a bit mocking BUT THATS THEIR WHOLW THINF. anyways i’m not done. when newt drifts with the kaiju #and pentecost is there talking to him and hermann and newt r yelling back in forth u can hear the unease and shakiness in their voices and #especially the frustration in hermanns. he’s frustrated abt newt risking his life and is worried abt that which translates out in anger.#and yeah maybe he’s salty abt being proven wrong too lmao. BUT CONTINUING ON. stacker could have just told newt to go to hannibal chau and #he would have done it. but instead they watch the film of him on HERMANNS computer as HERMANN controls the computer to look at the film. if #thé film was shown it was for a reason. newt doesn’t seem like the type to need reassurance abt chau before he goes. he was willing to die #for his trash drift. and stacker gave him the card and info so there’s no need to do anything else. the video is most likely there for the #viewers but it needs a reason to be there in the show. hence my reasoning that HERMANN asked to see it out of concern for newt who would be #doinf this alone. hermann demanded to see some proof to reassure himself. stacker having the card on him makes sense. him having that bulky #tape doesn’t. meaning hermann pressured him into leaving getting the tape and coming back to show him. anyways one more bit. so the drift.#hermann is clearly scared out of his mind and thinking abt the impending triple event. yet he still drifts with newt he does it to protect #him to take part of the neural load. and it takes a toll on hermann it makes a big enough mess of his brain that he ends with him bleeding #and shaking and sweating and coughing and throwing up. and he knew it would take a toll. he knew it would be a lot he’s seen the jaegers.#he’s seen what happens. he knows it will be rough. he knows it’ll be much worse for him who wasn’t drifted then for newt who has. yet he #still does it to help newt and to show his care and trust and concern and love and THEYRE DRIFT COMPATIBLE U DONT UNDERSTANDABLE HOW #EMOTIONAL I AM OVER THIS FUCKING OVER THEM #anyways one last thing. the way that they full body slapping each other on the back bear hugged when the throat collapsed (they were behind #herc and tendo so it was a little hard to see. i missed it the first time) in pure adrenaline happiness before we see the quiet tender hug #when they know everything is over for good (for now at least) when it’s time to celebrate when it time to think abt their drift and their #bond and their relationship and their LOVE. i’m so ok abt them rn actually #toad.txt #i wish i wrote this in a keep reading bit and not the tags now. anyways #pacific rim #pacific rim spoilers #newton geiszler #hermann gottlieb #newmann

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frosteee-variation · 6 months

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you ever get the uncontrollable urge to pursue a creative project because you got one (1) piece of information that you haven't been able to stop thinking about

#anyways. just got told that scientists are considering the use of fungi in computers #specifically for neural networks #and I thought of the humongous fungus and whatnot #and that also reminded me of pando and how I've been thinking passively about trees in horror or something #idk they just feel a bit eldritch to me. great things that I have respect for. it's the way they sway in the wind I think #anyways just like. I don't know. sci-fi horror using semi-biological computers sounds so interesting to me as a concept #and I've always wanted to do a podcast or write something or something y'know #anyways. don't know what or how I'd do something with it but I've just been rotating the thought #frost talks

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astraltrickster · 1 year

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My absolute least favorite genre of negative response to neural net chat bots and the like is "OMG it's SENTIENT this is SCARY............WE HAVE TO DESTROY IT!!!!!!"

Like do you hear yourself? You're claiming that what's scary here is that it is allegedly SENTIENT (which it isn't)...so we have to KILL it? Your first response to a sensationalized, false report that we have created a sentient intelligent life form is "EEEK, KILL IT!!"? End a sentient, sapient life? That's your FIRST go-to? No questions? Not a moment's hesitation?

All right then, I'm sure this has absolutely no terrifying implications about your attitudes toward other things in life that are unfamiliar and scary to you...

#tech #ai tag #the only scary thing about neural nets is the profit motives behind their rushed and shoddy implementation #which dont get me wrong thats fucking terrifying #the fact that people are out here selling chatbots that will lie to and gaslight and sexually harass you is uh............bad #the fact that theyre trying to use bots to put people out of work with no contingency plans for anyone displaced is also absolute trash #but neural nets as a whole are genuinely one of the most fascinating breakthroughs in computer science #i am so happy im alive to see them #i just wish people didnt have to try to shove them into some of the most inappropriate cognitohazardous applications possible #because they HAVE to turn a PROFIT for our corporate overlords to be WORTH anything right???#fight the ceos not the bots

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aholefilledwithtwigs · 2 years

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you know what

for a casual pursuit this involves an awful lot of running

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atozofsoftwareengineering · 5 months

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Power of Natural Language Processing with AWS

Dive into the world of Natural Language Processing on AWS and learn how to build intelligent applications with services like Amazon Comprehend, Transcribe, and Polly. Explore the future of language-driven AI and cloud computing #AWSNLP #AI #CloudComputing

Natural Language Processing (NLP) has emerged as a transformative force in the realm of artificial intelligence, enabling computers to comprehend and generate human-like text. As businesses increasingly recognize the value of language-driven insights and applications, cloud platforms such as Amazon Web Services (AWS) have played a pivotal role in democratizing access to advanced NLP capabilities.…

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jcmarchi · 4 months

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The Way the Brain Learns is Different from the Way that Artificial Intelligence Systems Learn - Technology Org

New Post has been published on https://thedigitalinsider.com/the-way-the-brain-learns-is-different-from-the-way-that-artificial-intelligence-systems-learn-technology-org/

The Way the Brain Learns is Different from the Way that Artificial Intelligence Systems Learn - Technology Org

Researchers from the MRC Brain Network Dynamics Unit and Oxford University’s Department of Computer Science have set out a new principle to explain how the brain adjusts connections between neurons during learning.

This new insight may guide further research on learning in brain networks and may inspire faster and more robust learning algorithms in artificial intelligence.

Study shows that the way the brain learns is different from the way that artificial intelligence systems learn. Image credit: Pixabay

The essence of learning is to pinpoint which components in the information-processing pipeline are responsible for an error in output. In artificial intelligence, this is achieved by backpropagation: adjusting a model’s parameters to reduce the error in the output. Many researchers believe that the brain employs a similar learning principle.

However, the biological brain is superior to current machine learning systems. For example, we can learn new information by just seeing it once, while artificial systems need to be trained hundreds of times with the same pieces of information to learn them.

Furthermore, we can learn new information while maintaining the knowledge we already have, while learning new information in artificial neural networks often interferes with existing knowledge and degrades it rapidly.

These observations motivated the researchers to identify the fundamental principle employed by the brain during learning. They looked at some existing sets of mathematical equations describing changes in the behaviour of neurons and in the synaptic connections between them.

They analysed and simulated these information-processing models and found that they employ a fundamentally different learning principle from that used by artificial neural networks.

In artificial neural networks, an external algorithm tries to modify synaptic connections in order to reduce error, whereas the researchers propose that the human brain first settles the activity of neurons into an optimal balanced configuration before adjusting synaptic connections.

The researchers posit that this is in fact an efficient feature of the way that human brains learn. This is because it reduces interference by preserving existing knowledge, which in turn speeds up learning.

Writing in Nature Neuroscience, the researchers describe this new learning principle, which they have termed ‘prospective configuration’. They demonstrated in computer simulations that models employing this prospective configuration can learn faster and more effectively than artificial neural networks in tasks that are typically faced by animals and humans in nature.

The authors use the real-life example of a bear fishing for salmon. The bear can see the river and it has learnt that if it can also hear the river and smell the salmon it is likely to catch one. But one day, the bear arrives at the river with a damaged ear, so it can’t hear it.

In an artificial neural network information processing model, this lack of hearing would also result in a lack of smell (because while learning there is no sound, backpropagation would change multiple connections including those between neurons encoding the river and the salmon) and the bear would conclude that there is no salmon, and go hungry.

But in the animal brain, the lack of sound does not interfere with the knowledge that there is still the smell of the salmon, therefore the salmon is still likely to be there for catching.

The researchers developed a mathematical theory showing that letting neurons settle into a prospective configuration reduces interference between information during learning. They demonstrated that prospective configuration explains neural activity and behaviour in multiple learning experiments better than artificial neural networks.

Lead researcher Professor Rafal Bogacz of MRC Brain Network Dynamics Unit and Oxford’s Nuffield Department of Clinical Neurosciences says: ‘There is currently a big gap between abstract models performing prospective configuration, and our detailed knowledge of anatomy of brain networks. Future research by our group aims to bridge the gap between abstract models and real brains, and understand how the algorithm of prospective configuration is implemented in anatomically identified cortical networks.’

The first author of the study Dr Yuhang Song adds: ‘In the case of machine learning, the simulation of prospective configuration on existing computers is slow, because they operate in fundamentally different ways from the biological brain. A new type of computer or dedicated brain-inspired hardware needs to be developed, that will be able to implement prospective configuration rapidly and with little energy use.’

Source: University of Oxford

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saw5 · 4 months

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not reading the rest of this post cuz it talks about the talos principle and i want to play those games blind (& soon) but this is exactly how i feel too -- exactly what i've said in the past as well -- & it is mega validating hearing it from someone who works in tech

#i learned about image-gen neural nets in college like a year before they became pop culture knowledge #so im well aware of the fact that they are in many ways a natural next step in the development of technology intended to emulate human capab #ilities #trying to model different processes and outputs of the brain is like computational neuroscience 101. you dont get to Tell People Not To Do T #hat #the step that follows figuring out how something works is figuring out how to replicate it

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compneuropapers · 2 months

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Interesting Papers for Week 10, 2024

Children seek help based on how others learn. Bridgers, S., De Simone, C., Gweon, H., & Ruggeri, A. (2023). Child Development, 94(5), 1259–1280.

Dopamine regulates decision thresholds in human reinforcement learning in males. Chakroun, K., Wiehler, A., Wagner, B., Mathar, D., Ganzer, F., van Eimeren, T., … Peters, J. (2023). Nature Communications, 14, 5369.

Abnormal sense of agency in eating disorders. Colle, L., Hilviu, D., Boggio, M., Toso, A., Longo, P., Abbate-Daga, G., … Fossataro, C. (2023). Scientific Reports, 13, 14176.

Different time scales of common‐cause evidence shape multisensory integration, recalibration and motor adaptation. Debats, N. B., Heuer, H., & Kayser, C. (2023). European Journal of Neuroscience, 58(5), 3253–3269.

Inferential eye movement control while following dynamic gaze. Han, N. X., & Eckstein, M. P. (2023). eLife, 12, e83187.

Dissociable roles of human frontal eye fields and early visual cortex in presaccadic attention. Hanning, N. M., Fernández, A., & Carrasco, M. (2023). Nature Communications, 14, 5381.

Neural tuning instantiates prior expectations in the human visual system. Harrison, W. J., Bays, P. M., & Rideaux, R. (2023). Nature Communications, 14, 5320.

Acute exercise has specific effects on the formation process and pathway of visual perception in healthy young men. Komiyama, T., Takedomi, H., Aoyama, C., Goya, R., & Shimegi, S. (2023). European Journal of Neuroscience, 58(5), 3239–3252.

Locating causal hubs of memory consolidation in spontaneous brain network in male mice. Li, Z., Athwal, D., Lee, H.-L., Sah, P., Opazo, P., & Chuang, K.-H. (2023). Nature Communications, 14, 5399.

Development of multisensory processing in ferret parietal cortex. Medina, A. E., Foxworthy, W. A., Keum, D., & Meredith, M. A. (2023). European Journal of Neuroscience, 58(5), 3226–3238.

Optimal routing to cerebellum-like structures. Muscinelli, S. P., Wagner, M. J., & Litwin-Kumar, A. (2023). Nature Neuroscience, 26(9), 1630–1641.

In vivo ephaptic coupling allows memory network formation. Pinotsis, D. A., & Miller, E. K. (2023). Cerebral Cortex, 33(17), 9877–9895.

Sex-dependent noradrenergic modulation of premotor cortex during decision-making. Rodberg, E. M., den Hartog, C. R., Dauster, E. S., & Vazey, E. M. (2023). eLife, 12, e85590.

Propagation of activity through the cortical hierarchy and perception are determined by neural variability. Rowland, J. M., van der Plas, T. L., Loidolt, M., Lees, R. M., Keeling, J., Dehning, J., … Packer, A. M. (2023). Nature Neuroscience, 26(9), 1584–1594.

High-precision mapping reveals the structure of odor coding in the human brain. Sagar, V., Shanahan, L. K., Zelano, C. M., Gottfried, J. A., & Kahnt, T. (2023). Nature Neuroscience, 26(9), 1595–1602.

The locus of recognition memory signals in human cortex depends on the complexity of the memory representations. Sanders, D. M. W., & Cowell, R. A. (2023). Cerebral Cortex, 33(17), 9835–9849.

Velocity of conduction between columns and layers in barrel cortex reported by parvalbumin interneurons. Scheuer, K. S., Judge, J. M., Zhao, X., & Jackson, M. B. (2023). Cerebral Cortex, 33(17), 9917–9926.

Acetylcholine and noradrenaline enhance foraging optimality in humans. Sidorenko, N., Chung, H.-K., Grueschow, M., Quednow, B. B., Hayward-Könnecke, H., Jetter, A., & Tobler, P. N. (2023). Proceedings of the National Academy of Sciences, 120(36), e2305596120.

Rats adaptively seek information to accommodate a lack of information. Yuki, S., Sakurai, Y., & Yanagihara, D. (2023). Scientific Reports, 13, 14417.

Beta traveling waves in monkey frontal and parietal areas encode recent reward history. Zabeh, E., Foley, N. C., Jacobs, J., & Gottlieb, J. P. (2023). Nature Communications, 14, 5428.

#neuroscience #science #research #brain science #scientific publications #cognitive science #neurobiology #cognition #psychophysics #computational neuroscience #neural computation #neural networks #neurons

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slack-wise · 15 days

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Margaret Thatcher being crucified

#Margaret Thatcher #Thatcher #politics #idiots #morons #AI #generated #computer generated #tech #LLM #neural network

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oatbugs · 2 years

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thinking abt that psychology lecture where they taught us how thinking about good memories makes your life obiectively better over time

#personal #i think i subconsciously equated memory and nostalgia. and i dislike the feeling of nostalgia so i avoided so many memories #i asked the masters student if every love song he listens to is about philosophy and he said everything is #everything is about the thing you love if you love it enough. i saw a star through the london light pollution (caught in an eternal nightly #daylight) . i was with a friend and another friend who had just gotten an unexpected diagnosis #we told her congratulations you're autistic and that means you may now explore a revolutionary depth #inside yourself. and it was all still about philosophy. (you sent us back a letter in said in capital letters #THE UNIVERSE IS GOING TO CATCH YOU.) one day i grabbed my friends arm and we jumped over a rusted metal fence #the soap-beaten bleach-eaten clothes i was wearing at the time still smell like rust and metal #for a brief moment i sympathise with the rusted case of a computer i saw when i was 5. i wondered if it had died #violently. i am spending my life protecting their ability to learn. and each time i ask a neural network what led to its choice of #planetary object it gives me the same blank stare of a young child which is in truth a black box to drown in.#when i was too young and i used to think of death too often i imagined my body was a machine. i imagined #liquid gold around my joints. i could never hurt a machine. i could never hurt a body that was a machine.#my neuroscience professor paused after a long lecture and told us #your body is not a computer，it is a flawed and gooey and imprecise mechanism. your nervous sytem is an intricate machine.#is every song about philosophy? is every song about the way machines learn? on the weekend i ignore the parts of him that have #rotted and pull the passion right out of his nerves. he told me he needs a way to kickstart critical periods so that he may learn well agai #and i told him taking every drug on the planet wont make a clever brain cleverer. he confessed he didnt plan #on making it far enough for it to matter. i checked his pulse and i told him that his body is a liquid imprecise delicate machine.#sometimes you become terrible but you are not an exception to being a winged thing. if you hold me you will smell like metal for the rest #of your life.

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neatokeanosocks · 1 year

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#finally came to terms with myself that it (neural implants for bci) aint ever gonna happen and if it does it aint gonna be good #shitpost #meme #brain computer interface

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geometrymatters · 1 year

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Functional connectivity and causal connections across different neural units are two main categories for how fMRI data on brain connectivity patterns are categorized. Recently, computational techniques—especially those based on graph theory—have been crucial in helping us comprehend the structure of brain connections.

In an effort to understand the neural bases of human cognition and neurological illnesses, a team at the University of Florida conducted a systematic review of how brain features might arise through the interactions of different neural units in various cognitive and neurological applications utilizing fMRI. This was made possible by the development of graph theoretical analysis.

A central and enduring aim of research in the psychological and brain sciences is to elucidate the information-processing architecture of human intelligence. Does intelligence originate from a specific brain structure or instead reflect system-wide network mechanisms for flexible and efficient information processing?

#geometry #geometrymatters #geometriccognition #cognitivegeometry #graph #network #theory #brain #neural #neuroscience #model #computation #information #knowledge

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