Stories with Tag Brain Research

• How far neuroscience is from understanding brains (2023)

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  Posted: 2025-03-12 12:13:42

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  Neuroscience has made significant strides, yet a comprehensive understanding of the brain remains distant. While we've mapped connectomes and identified functional regions, we lack a unifying theory explaining how neural activity generates cognition and behavior. Current models, like predictive coding, are insightful but incomplete, struggling to bridge the gap between micro-level neural processes and macro-level phenomena like consciousness. Technological advancements, such as better brain-computer interfaces, hold promise, but truly understanding the brain requires conceptual breakthroughs that integrate diverse findings across scales and disciplines. Significant challenges include the brain's complexity, ethical limitations on human research, and the difficulty of studying subjective experience.

  The article "How far neuroscience is from understanding brains (2023)" by Erik Hoel explores the significant chasm between current neuroscientific knowledge and a true, comprehensive understanding of the brain. Hoel argues that while neuroscience has made impressive strides in mapping the connectome, identifying specific neural circuits, and correlating brain activity with behaviors, these advancements do not yet constitute a genuine understanding of how the brain gives rise to consciousness, cognition, and subjective experience.

  He posits that the field is currently experiencing a "connectome crack-up," where the sheer complexity of neural connectivity data, even at relatively small scales, overwhelms current analytical and theoretical frameworks. He illustrates this with the example of the C. elegans worm, whose relatively simple nervous system, despite being fully mapped, still lacks a corresponding understanding of its behavior in terms of information processing. This suggests that even with complete structural information, understanding function remains an elusive goal.

  Hoel further emphasizes the crucial distinction between correlational studies, which identify relationships between brain activity and behavior, and a true causal understanding of how neural activity generates behavior and experience. He argues that while correlational studies are valuable, they are insufficient to explain the underlying mechanisms of consciousness. He uses the analogy of a television set: observing correlations between the internal components and the image displayed does not explain how the television actually produces the image.

  The article also delves into the theoretical challenges of bridging different levels of analysis in neuroscience, from molecular interactions to large-scale network dynamics. Hoel suggests that current theories lack the explanatory power to integrate these disparate levels, hindering a holistic understanding of brain function. He highlights the problem of "explanatory emergence," where higher-level phenomena, such as consciousness, seemingly emerge from lower-level physical processes, but the mechanisms of this emergence remain unclear.

  Furthermore, Hoel discusses the limitations of current computational models of the brain. He notes that while these models can simulate specific aspects of neural activity, they often fall short of capturing the complexity and adaptability of real brains. He argues that focusing solely on computational models, without addressing the fundamental biological and physical principles underlying brain function, risks overlooking crucial aspects of the system.

  Finally, the article concludes with a call for a more integrated and theoretically grounded approach to neuroscience. Hoel emphasizes the need for new conceptual frameworks that can bridge different levels of analysis, incorporate principles of information processing and thermodynamics, and ultimately explain how physical processes in the brain give rise to subjective experience. He suggests that a deeper understanding of fundamental physics and information theory might be necessary to unlock the secrets of the brain and overcome the current limitations of neuroscience. He paints a picture of neuroscience as a field still in its early stages, with much work to be done before a true understanding of the brain is achieved.

  • Neuroscience
  • Brain Research
  • Understanding the Brain
  • Complexity of the Brain
  • Cognitive Science
  • Neurobiology
  • Scientific Progress
  • Current Research
  • Brain Function
  • neural networks
  • Connectome
  • Brain Mapping
  • 2023

  Summary of Comments ( 7 )
  https://news.ycombinator.com/item?id=43342407

  Verbose tl;dr

  HN commenters discuss the challenges of understanding the brain, echoing the article's points about its complexity. Several highlight the limitations of current tools and methods, noting that even with advanced imaging, we're still largely observing correlations, not causation. Some express skepticism about the potential of large language models (LLMs) as brain analogs, arguing that their statistical nature differs fundamentally from biological processes. Others are more optimistic about computational approaches, suggesting that combining different models and focusing on specific functions could lead to breakthroughs. The ethical implications of brain research are also touched upon, with concerns raised about potential misuse of any deep understanding we might achieve. A few comments offer historical context, pointing to past over-optimism in neuroscience and emphasizing the long road ahead.

  The Hacker News post "How far neuroscience is from understanding brains (2023)" linking to a PMC article elicited a moderate discussion with several compelling threads.

  One commenter highlighted the distinction between "understanding" at different levels. They argue that while neuroscience has made impressive strides in mapping specific brain regions to functions and understanding the mechanics of individual neurons, it's a far cry from understanding the emergent properties of consciousness and subjective experience. They use the analogy of understanding the physics of individual transistors versus understanding how a complex computer program works. Knowing the low-level details doesn't automatically translate to comprehending the higher-level complexities.

  Another commenter expressed skepticism about the usefulness of large-scale brain simulations, referencing the Human Brain Project. They suggested that the focus should be on understanding fundamental principles first, before attempting to simulate the entire brain. They also questioned the assumption that simply simulating a brain would lead to understanding consciousness.

  Building on the simulation skepticism, another user compared brain simulation to simulating weather patterns. While we can predict weather with increasing accuracy, we don't truly understand it in a deep, causal sense. They argued that a similar situation might arise with brain simulations – we might be able to replicate behavior without truly understanding the underlying mechanisms of consciousness.

  Another discussion thread touched on the philosophical implications of consciousness and the hard problem of subjectivity. One commenter argued that understanding the physical mechanisms of the brain might not be enough to explain subjective experience. They suggest that consciousness might be an emergent property that cannot be reduced to its constituent parts.

  Several comments also focused on the limitations of current neuroscientific tools and techniques. One user pointed out the difficulty of studying live human brains in detail, and the reliance on animal models which may not fully translate to human cognition. Another commenter discussed the limitations of fMRI in capturing the complex dynamics of brain activity.

  Finally, a more optimistic commenter argued that while neuroscience has a long way to go, the progress made in recent decades is undeniable. They point to advancements in neuroimaging, brain-computer interfaces, and treatments for neurological disorders as evidence of the field's progress. They suggest that continued investment in research will eventually lead to a deeper understanding of the brain and consciousness.

  In summary, the comments on the Hacker News post reflect a range of perspectives on the current state of neuroscience. While some express skepticism about the feasibility of truly understanding the brain, others are more optimistic about the potential for future breakthroughs. The discussion highlights the significant challenges that remain in understanding consciousness and the complex interplay between brain activity and subjective experience.

• Using AI to develop a fuller model of the human brain

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  Posted: 2025-01-25 20:36:26

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  UCSF researchers are using AI, specifically machine learning, to analyze brain scans and build more comprehensive models of brain function. By training algorithms on fMRI data from individuals performing various tasks, they aim to identify distinct brain regions and their roles in cognition, emotion, and behavior. This approach goes beyond traditional methods by uncovering hidden patterns and interactions within the brain, potentially leading to better treatments for neurological and psychiatric disorders. The ultimate goal is to create a "silicon brain," a dynamic computational model capable of simulating brain activity and predicting responses to various stimuli, offering insights into how the brain works and malfunctions.

  The University of California, San Francisco (UCSF) article, "Building a Silicon Brain," delves into the ambitious endeavor of utilizing artificial intelligence (AI) as a crucial tool in constructing a more comprehensive and nuanced understanding of the intricate workings of the human brain. The piece meticulously outlines the challenges inherent in deciphering the brain's complex architecture and functionality, highlighting the limitations of current neuroscientific methods. It underscores the sheer complexity of the brain, with its billions of interconnected neurons and trillions of synapses, a system whose intricate interplay gives rise to cognition, emotion, and behavior.

  The article posits that AI, specifically machine learning algorithms, offers a novel approach to unraveling this complexity. These algorithms, trained on vast datasets of neurological data – ranging from fMRI scans to electrophysiological recordings – can identify patterns and relationships within the data that might otherwise remain obscured to human observation. By discerning these subtle correlations, AI can assist researchers in formulating hypotheses about the functional organization of different brain regions and the mechanisms underlying specific cognitive processes.

  Specifically, the article discusses the work of UCSF neuroscientists who are employing AI to study the neural basis of speech and language. By training algorithms on recordings of brain activity during speech production and comprehension, the researchers aim to map the neural circuits involved in these complex cognitive functions. The hope is that such detailed mapping will eventually lead to a deeper understanding of language disorders like aphasia and potentially inform the development of more effective therapeutic interventions.

  Furthermore, the article explores the potential of AI to bridge the gap between animal models and human neuroscience. While animal models have provided invaluable insights into fundamental brain mechanisms, their direct applicability to the human brain is often limited. AI, by analyzing data from both animal and human studies, can potentially identify common principles and extrapolate findings from animal models to the human context, thereby accelerating the pace of discovery.

  The overarching goal, as articulated in the article, is to leverage the power of AI to create a sophisticated, computational model of the human brain, a "silicon brain," that accurately captures its multi-layered complexity. Such a model would not only advance our fundamental understanding of the brain but also hold immense promise for developing novel treatments for neurological and psychiatric disorders, paving the way for a future where personalized medicine for brain-related illnesses becomes a reality. The article emphasizes that this is a long-term vision, requiring ongoing collaboration between neuroscientists, computer scientists, and engineers, but the potential benefits are profound and justify the significant investment in this emerging field of research.

  • artificial intelligence
  • AI
  • Brain Modeling
  • Neuroscience
  • Computational Neuroscience
  • Human Brain
  • Silicon Brain
  • Simulation
  • neural networks
  • Cognitive Science
  • machine learning
  • Brain Research
  • UCSF

  Summary of Comments ( 1 )
  https://news.ycombinator.com/item?id=42824625

  Verbose tl;dr

  HN commenters discuss the challenges and potential of simulating the human brain. Some express skepticism about the feasibility of accurately modeling such a complex system, highlighting the limitations of current AI and the lack of complete understanding of brain function. Others are more optimistic, pointing to the potential for advancements in neuroscience and computing power to eventually overcome these hurdles. The ethical implications of creating a simulated brain are also raised, with concerns about consciousness, sentience, and potential misuse. Several comments delve into specific technical aspects, such as the role of astrocytes and the difficulty of replicating biological processes in silico. The discussion reflects a mix of excitement and caution regarding the long-term prospects of this research.

  The Hacker News post titled "Using AI to develop a fuller model of the human brain," linking to a UCSF Magazine article about building a silicon brain, has generated a modest number of comments, predominantly focused on the complexities and challenges inherent in brain simulation and the potential implications of such research.

  Several commenters express skepticism about the feasibility of fully replicating the human brain in silicon, citing the sheer complexity of biological systems and the current limitations of our understanding of consciousness and cognition. One commenter highlights the vast interconnectedness of brain regions, arguing that even if individual components could be modeled, replicating the dynamic interactions between them would be an immense hurdle. Another questions the article's focus on individual neurons, suggesting that focusing on higher-level abstractions and emergent properties might be a more fruitful approach.

  The ethical implications of creating a silicon brain are also raised. One commenter speculates about the potential for such a model to achieve consciousness, raising questions about its moral status and the responsibility of its creators. Another expresses concern that the focus on replicating the human brain might divert resources away from more pressing societal problems.

  A few commenters offer more optimistic perspectives. One suggests that even if a complete simulation proves impossible, the research could still lead to valuable insights into brain function and potential treatments for neurological disorders. Another notes the potential for silicon brains to contribute to the development of more advanced artificial intelligence.

  Some comments delve into specific technical aspects of brain simulation. One commenter discusses the challenges of modeling the complex electrochemical processes within neurons, while another questions the scalability of current computing technologies to handle the immense data involved in simulating a complete brain.

  While the overall tone is cautious, the comments reflect a diverse range of perspectives on the challenges and potential benefits of this complex and ambitious area of research. Notably absent is any strong advocacy for the approach outlined in the article; the discussion largely revolves around the limitations and potential pitfalls. The thread doesn't delve deep into specific technical proposals or solutions, staying at a relatively high level of discussion about the broader implications and feasibility.