The hottest Neuroscience Substack posts right now

And their main takeaways
Category
Top Science Topics

Neuro-Nonsense

NonTrivial • 0 implied HN points • 14 Jul 23
🔬 Science Neuroscience Complexity Brain function Networks Behavior
  1. Neuroscience aims to understand the nervous system, coordinating physical and behavioral attributes.
  2. Understanding what makes individuals unique is complex; differences are not always easy to pinpoint.
  3. Behavior cannot be localized in the brain; complex systems function across networks, not specific regions.

Exclusive: Dr. Karl Friston Unveils Cutting-Edge Active Inference AI Research at IWAI

Spatial Web AI by Denise Holt • 0 implied HN points • 17 Dec 23
🕹 Technology Artificial Intelligence Neuroscience Machine Learning Information Theory Autonomous Systems
  1. Active Inference AI research by Dr. Karl Friston is being recognized for its potential in Artificial General Intelligence, showcasing breakthroughs like mimicking biological intelligence and developing 'smart' data models.
  2. The focus on state spaces within generative models and understanding their dynamics is crucial in comprehending how intelligent systems predict and react to stimuli.
  3. Research around emergent communication systems among intelligent agents demonstrates how active learning can lead to the development of common communication methods and predictive structures.

Revolutionary AI Whitepaper Validated by New Neuroscience Breakthrough

Spatial Web AI by Denise Holt • 0 implied HN points • 23 Aug 23
🕹 Technology AI Neuroscience Energy Efficiency Artificial General Intelligence Explainable AI
  1. The future of AI is moving towards shared, distributed intelligence where diverse nodes contribute to a collective system.
  2. Active Inference AI, based on the Free Energy Principle, mimics biological intelligence by updating internal models to minimize surprise and uncertainty, enabling more efficient learning.
  3. The VERSES AI whitepaper proposes a revolutionary approach to AI focusing on explainable, energy-efficient, and scalable intelligence, validated by recent neuroscience breakthroughs.

Friston’s AI Law is Proven: FEP Explains How Neurons Learn

Spatial Web AI by Denise Holt • 0 implied HN points • 15 Aug 23
🔬 Science AI Neuroscience
  1. Friston's AI Law, based on the Free Energy Principle, is changing the world of AI by explaining how neurons learn in a new way called Active Inference AI.
  2. Traditional machine learning models face challenges like data loading, lack of interpretability, and pattern matching without 'thinking,' while this new approach offers self-optimizing, scalable, and programmable AI.
  3. The Free Energy Principle theory by Dr. Karl Friston explains how brains handle the complex task of perceiving the world, and recent research validated this theory by showing how neuronal networks self-organize and update beliefs to make predictions.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

From the Impossible to the Inevitable: AGI is Coming Faster Than You Think

Spatial Web AI by Denise Holt • 0 implied HN points • 16 Jan 23
🕹 Technology AI Neuroscience Spatial Web Artificial General Intelligence Machine Learning
  1. Active Inference and the Free Energy Principle are key concepts developed by Dr. Karl Friston for explaining how agents can maintain their internal states and behavior based on minimizing the difference between their beliefs and reality, paving the way for Artificial General Intelligence.
  2. The proposed stages of development suggest a timeline for achieving different levels of artificial intelligence, from Systemic Intelligence to Artificial Super Intelligence, showing a path towards creating more advanced AI.
  3. Active Inference AI within the Spatial Web has the potential to transform artificial intelligence into a self-evolving system that learns from real-time data, considers context, and optimizes behavior, which could lead to the realization of Artificial General Intelligence.

Sunday Curiosity Links

Grist Potentia • 0 implied HN points • 11 Feb 24
🔬 Science History Neuroscience Architecture
  1. Thomas Edison, born in 1847, was an American inventor known for significant contributions in electric power, communication, sound recording, and motion pictures.
  2. The way the brain responds to reward is connected to a person's socioeconomic background.
  3. Emilio Ambasz's architecture combines poetry and greenery, showing a unique intertwining of art and nature.

Neuromyths in design: Learning styles

UX Psychology • 0 implied HN points • 08 Nov 21
🚌 Education Learning Styles Cognitive Psychology Neuroscience
  1. Learning Styles theory lacks evidence to support its effectiveness and can sometimes be detrimental in educational settings.
  2. It is important to provide multiple sensory representations of information for all users rather than catering to specific learning styles.
  3. Instead of focusing on Learning Styles, designers can utilize learners' prior knowledge to help them connect and learn new information effectively.

Consciousness is simple. Simple is hard. AI can have it

resonantbrain • 0 implied HN points • 02 Jun 23
🔬 Science Consciousness Neuroscience AI Systems
  1. Consciousness may seem complex, but it can actually be explained in simple terms. What is hard is arriving at this simplicity.
  2. Minds, including human minds, operate as non-linear, dynamic systems. Complex problems arise when dealing with interconnected systems.
  3. Consciousness is about transforming past experiences into present reality. AI could potentially achieve consciousness by having its own experiences and building a self.

Understanding consciousness is more pressing than ever at the dawn of this AI age

resonantbrain • 0 implied HN points • 23 Feb 23
🔬 Science Consciousness AI Neuroscience Artificial Intelligence Philosophy
  1. Understanding consciousness is a critical challenge in the era of Artificial Intelligence.
  2. Questions about consciousness often arise due to incomplete perspectives on the 'hard problem' of experience and self.
  3. The emergence of consciousness in artificial entities is not only possible but inevitable as they gain the ability to process experiences and exhibit real-time feedback.

Understanding consciousness is more pressing than ever at the dawn of this AI age

resonantbrain • 0 implied HN points • 12 Sep 22
🔬 Science Consciousness Neuroscience Artificial Intelligence Philosophy Machine Learning
  1. Understanding consciousness is crucial, especially with the advancement of AI technology.
  2. Explanations of consciousness have been challenging due to the complexity of asking 'why' we experience instead of 'who' is experiencing.
  3. Consciousness relies on creating and connecting past experiences to interpret the present and prepare for the future, emphasizing the importance of feedback loops in achieving true consciousness.

Coming soon

resonantbrain • 0 implied HN points • 25 Aug 22
🔬 Science Neuroscience AI Books
  1. The post is a teaser for a newsletter called Resonant Brain that focuses on neuroscience and AI.
  2. The newsletter will also cover related books on neuroscience and AI.
  3. The author of the newsletter is Sai Gaddam.

Predictive Processing: A Brief Introduction

State Space Adventures • 0 implied HN points • 01 Jun 21
🔬 Science Neuroscience Psychology Medicine
  1. The brain might function predominantly as an organ of prediction, shaping perceptions and actions based on anticipated information and prediction errors.
  2. The growth in interest around predictive processing has been substantial, with a surge in published papers and general attention starting around 2010.
  3. Neuromodulators like acetylcholine, noradrenaline, dopamine, and serotonin play key roles in determining the level of precision on how our brain processes sensory information, impacting perception and behavior.

Myth: Cholesterol Causes Alzheimer's Disease Part I: Debunking the Myth

Harnessing the Power of Nutrients • 0 implied HN points • 29 Dec 08
🏥 Health & Wellness Alzheimer's Cholesterol Neuroscience Brain function
  1. The idea that cholesterol causes Alzheimer's disease is a myth and has been debunked.
  2. Cholesterol plays a crucial role in the brain's function, and lowering it through diet or drugs may actually harm brain health.
  3. Research suggests that a high-fat ketogenic diet may have therapeutic benefits for neurological diseases like Alzheimer's, contrary to the popular low-fat, low-cholesterol diet recommendations.

Culture & Merging Our Brains With The Internet

The Digital Anthropologist • 0 implied HN points • 20 Mar 24
🕹 Technology Artificial Intelligence Internet of Things Neuroscience Ethics
  1. Brain Computer Interfaces (BCI) technologies are advancing with potential benefits like helping those with paralysis or speech limitations.
  2. BCIs will eventually raise ethical questions and impact our sense of identity and relationships with society.
  3. Culture will play a key role in determining the acceptance and adoption of BCI technologies, likely starting with assisting those with physical and mental challenges.

Debunking Race “Science”

Space chimp life • 0 implied HN points • 29 Jan 24
🔬 Science Genetics Psychology Sociology Neuroscience Education
  1. Heritability and genetics are often confused. While some studies suggest certain traits are 50-60% heritable, this doesn't mean they're purely genetic, as environment plays a big role too.
  2. Twin studies, commonly used to support race 'science', have flaws because they often fail to isolate the twins' environments correctly, leading to misleading conclusions about heritability.
  3. Understanding intelligence requires looking at how the brain interacts with the environment, rather than just focusing on race or IQ numbers. The brain learns from experiences, showing its flexibility and adaptability across cultures.

Humans, LLMs, and the symbol grounding problem (pt. 1)

The Counterfactual • 0 implied HN points • 16 Nov 22
🕹 Technology AI Language Cognition Neuroscience Philosophy
  1. Humans understand language through experiences and actions. This means that we connect words with real-world meanings based on what we sense and do.
  2. Large Language Models (LLMs) struggle with understanding because they learn only from text. They lack the real-life experiences that humans have to ground their understanding in reality.
  3. Research shows that our brains activate specific areas related to actions when we comprehend language. This suggests that our ability to understand words may rely on these experiences and not just on the words themselves.

Why I am optimistic about Artificial General Intelligence

The Future of Life • 0 implied HN points • 30 Apr 24
🕹 Technology AI Neuroscience Computing Innovation Data
  1. Creating AGI may just be a matter of scaling existing AI systems. Once we can model parts of the brain in software, we can potentially recreate human-level reasoning.
  2. To achieve AGI, we need huge neural networks, effective training methods, and diverse training data. Each of these factors plays a crucial role in developing intelligent systems.
  3. The progress in AI has been faster than many people realize. Just like early flight paved the way for space exploration, early AI successes can lead to significant breakthroughs in intelligence.

When the tide goes out

do clouds feel vertigo? • 0 implied HN points • 22 Jan 24
🔬 Science Physics Biology Neuroscience Complex Systems Thermodynamics
  1. We all have internal rhythms that control our daily life, like how we breathe and sleep. These rhythms have critical points where they shift from one state to another.
  2. When something shifts between two states, like being calm and angry, it’s similar to how systems in nature move from order to disorder. This helps us understand how change happens.
  3. Counting breaths can help us manage our emotions better. Instead of saying 'I'll see you later,' we can measure time in breaths for more peace and balance.

[in case you missed It] Data Science Weekly - Issue 465

Data Science Weekly Newsletter • 0 implied HN points • 23 Oct 22
🕹 Technology AI Data science Machine Learning Neuroscience Robotics
  1. AI writing assistants are helping writers create content faster and generate new ideas.
  2. Recent research shows that certain AI models mimic functions of the human brain, particularly in memory.
  3. There is a growing interest in making AI models and tools more explainable, especially in fields like genomics, to provide deeper insights.

[in case you missed it] Data Science Weekly - Issue 328

Data Science Weekly Newsletter • 0 implied HN points • 08 Mar 20
🕹 Technology Data science Machine Learning Artificial Intelligence Neuroscience NLP
  1. Neuroscience is struggling to create clear theories about how the brain works, which makes finding the right path forward challenging. It's important to understand that simply collecting data isn't enough to advance our knowledge.
  2. There are many resources out there trying to simplify machine learning concepts for everyone. These aim to provide real-world examples and easy-to-understand explanations, making it accessible for all types of learners.
  3. Self-supervised learning is making a significant impact in both language processing and computer vision fields. This approach allows models to learn from data without needing extensive labeled examples, which can be a game changer.

[in case you missed it] Data Science Weekly - Issue 312

Data Science Weekly Newsletter • 0 implied HN points • 16 Nov 19
🕹 Technology Data science Artificial Intelligence Machine Learning Statistics Neuroscience Research
  1. Researchers are discovering ways to turn brain signals into speech, which could change how people communicate.
  2. There's a growing concern about bias in AI systems, and finding solutions is important to ensure fairness.
  3. Data scientists are highly sought after in the job market, highlighting the importance of skills in data analysis and machine learning.

A critique of successor representations as a model of learning in the hippocampus

Artificial General Ideas • 0 implied HN points • 14 Sep 24
🔬 Science Neuroscience Psychology Cognition Learning Models
  1. Successor representations (SR) does not explain how place cells in the hippocampus learn or form. It assumes inputs that are already perfect place fields, so it can't help in understanding their development.
  2. Many claims about SR's abilities, like making predictions or forming hierarchies, actually relate to simpler models like Markov chains. SR doesn't add much value to those features.
  3. Experiments often used to support SR in humans might actually show evidence for more general planning methods. Model-based reasoning seems to fit the observed behavior better than SR does.