The Thousand Brains Theory: A Framework for Understanding the Neocortex and Building Intelligent Machines
https://www.youtube.com/watch?v=5LFo36g4Lug
#neuroscience #AI #numenta
https://www.youtube.com/watch?v=5LFo36g4Lug
#neuroscience #AI #numenta
YouTube
The Thousand Brains Theory
The Thousand Brains Theory: A Framework for Understanding the Neocortex and Building Intelligent Machines
Recent advances in reverse engineering the neocortex reveal that it is a highly-distributed sensory-motor modeling system. Each cortical column learns…
Recent advances in reverse engineering the neocortex reveal that it is a highly-distributed sensory-motor modeling system. Each cortical column learns…
A handful of podcasts, labs, projects, and groups which are involved both Neuroscience and Artificial Intelligence:
NeuroAILab: Aim to "reverse engineer" the algorithms of the brain, both to learn about how our minds work and to build more effective artificial intelligence systems.
Learning in Neural Circuits (LiNC) Laboratory: Study general principles of learning and memory in neural networks with the ultimate goal of understanding how real and artificial brains can optimize behaviour.
Human Brain Project: The Human Brain Project (HBP) is building a research infrastructure to help advance neuroscience, medicine and computing. It is one of four FET (Future and Emerging Tehcnology) Flagships, the largest scientific projects ever funded by the European Union.
Center for Brains, Minds and Machines: Understanding how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines is arguably one of the greatest challenges in science and technology. This group brings together computer scientists, cognitive scientists, and neuroscientists to create a new field—the Science and Engineering of Intelligence.
Center for Theoretical Neuroscience: they aim to establish, through the quality of the Center's research, the excellence of its trainees, and the impact of its visitor, dissemination, and outreach programs, a new cooperative paradigm that will move neuroscience to unprecedented levels of discovery and understanding. We believe we have one of the most exciting and interactive environments anywhere for bringing theoretical approaches to Neuroscience.
Unsupervised Thinking: a podcast about neuroscience, artificial intelligence and science more broadly
#NeuroScience #MachineLearning
NeuroAILab: Aim to "reverse engineer" the algorithms of the brain, both to learn about how our minds work and to build more effective artificial intelligence systems.
Learning in Neural Circuits (LiNC) Laboratory: Study general principles of learning and memory in neural networks with the ultimate goal of understanding how real and artificial brains can optimize behaviour.
Human Brain Project: The Human Brain Project (HBP) is building a research infrastructure to help advance neuroscience, medicine and computing. It is one of four FET (Future and Emerging Tehcnology) Flagships, the largest scientific projects ever funded by the European Union.
Center for Brains, Minds and Machines: Understanding how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines is arguably one of the greatest challenges in science and technology. This group brings together computer scientists, cognitive scientists, and neuroscientists to create a new field—the Science and Engineering of Intelligence.
Center for Theoretical Neuroscience: they aim to establish, through the quality of the Center's research, the excellence of its trainees, and the impact of its visitor, dissemination, and outreach programs, a new cooperative paradigm that will move neuroscience to unprecedented levels of discovery and understanding. We believe we have one of the most exciting and interactive environments anywhere for bringing theoretical approaches to Neuroscience.
Unsupervised Thinking: a podcast about neuroscience, artificial intelligence and science more broadly
#NeuroScience #MachineLearning
Neuroscience and Reinforcement Learning
#neuroscience #reinforcement_learning
https://www.princeton.edu/~yael/ICMLTutorial.pdf
#neuroscience #reinforcement_learning
https://www.princeton.edu/~yael/ICMLTutorial.pdf
Deep Learning and Computational Neuroscience
#neuroscience #reinforcement_learning
https://link.springer.com/article/10.1007/s12021-018-9360-6
#neuroscience #reinforcement_learning
https://link.springer.com/article/10.1007/s12021-018-9360-6
Neuroinformatics
Deep Learning and Computational Neuroscience
Neuroinformatics -
The Roles of Supervised Machine Learning in Systems Neuroscience
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML’s contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: 1) creating solutions to engineering problems, 2) identifying predictive variables, 3) setting benchmarks for simple models of the brain, and 4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists.
https://arxiv.org/ftp/arxiv/papers/1805/1805.08239.pdf
#neuroscience #machine_learning
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML’s contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: 1) creating solutions to engineering problems, 2) identifying predictive variables, 3) setting benchmarks for simple models of the brain, and 4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists.
https://arxiv.org/ftp/arxiv/papers/1805/1805.08239.pdf
#neuroscience #machine_learning
A fruitful relationship between neuroscience and AI
https://deepmind.com/blog/article/Dopamine-and-temporal-difference-learning-A-fruitful-relationship-between-neuroscience-and-AI
#reinforcement_learning #machine_learning #neuroscience #artificial_intelligence
https://deepmind.com/blog/article/Dopamine-and-temporal-difference-learning-A-fruitful-relationship-between-neuroscience-and-AI
#reinforcement_learning #machine_learning #neuroscience #artificial_intelligence
Google DeepMind
Dopamine and temporal difference learning: A fruitful relationship between neuroscience and AI
Learning and motivation are driven by internal and external rewards. Many of our day-to-day behaviours are guided by predicting, or anticipating, whether a given action will result in a positive...
Book: The SOAR Cognitive Architecture
Introduction: in development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.
https://dl.acm.org/doi/book/10.5555/2222503
#cognitive_science #neuroscience #reinforcement_learning #artificial_intelligence
Introduction: in development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.
https://dl.acm.org/doi/book/10.5555/2222503
#cognitive_science #neuroscience #reinforcement_learning #artificial_intelligence