Neuroscience

Working memory is a central topic of cognitive neuroscience because it is critical for solving real world problems in which information from multiple temporally distant sources must be combined to generate appropriate behavior. However, an often neglected fact is that learning to use working memory effectively is itself a difficult problem. The Gating" framework is a collection of psychological models that show how dopamine can train the basal ganglia and prefrontal cortex to form useful working memory representations in certain types of problems. We bring together gating with ideas from machine learning about using finite memory systems in more general problems. Thus we present a normative Gating model that learns, by online temporal difference methods, to use working memory to maximize discounted future rewards in general partially observable settings. The model successfully solves a benchmark working memory problem, and exhibits limitations similar to those observed in human experiments. Moreover, the model introduces a concise, normative definition of high level cognitive concepts such as working memory and cognitive control in terms of maximizing discounted future rewards."

This work describes a conceptually simple method for structured sparse coding and dictionary design. Supposing a dictionary with K atoms, we introduce a structure as a set of penalties or interactions between every pair of atoms. We describe modifications of standard sparse coding algorithms for inference in this setting, and describe experiments showing that these algorithms are efficient. We show that interesting dictionaries can be learned for interactions that encode tree structures or locally connected structures. Finally, we show that our framework allows us to learn the values of the interactions from the data, rather than having them pre-specified.

Elon Musk said he thinks his neural-technology company, Neuralink, will be able to "solve" schizophrenia and autism. On the latest "Artificial Intelligence" podcast with Lex Fridman, published Tuesday, the Tesla and SpaceX CEO was asked about the most exciting effects he foresees for Neuralink, whose goal is to develop an AI-enabled chip that could be implanted in a person's brain to record brain activity and potentially stimulate it. "So Neuralink I think at first will solve a lot of brain-related diseases," Musk said. "So could be anything from, like, autism, schizophrenia, memory loss -- like, everyone experiences memory loss at certain points in age. Parents can't remember their kids' names and that kind of thing."

Today neural networks dominate the landscape of AI and AIOps, but I've said many times that this is unsustainable. Neural networks have peaked in their ability to deliver effective and meaningful results. The science has issues with basic intractability, mismatch and inherent latency. Even though there is a lot of investment in neural networks, it's bearing on AIOps and the real-time business community is limited. Which brings me on to computational neuroscience, which I believe will benefit AI enormously.

A brain circuit that controls the compulsive drinking of alcohol has been discovered in mice, offering a hope of one day finding a cure for alcoholism in humans. Scientists have long sought to understand why some people are prone to develop drinking problems while others are not. The team's discovery in mice, if translated to humans, may provide doctors a way to reveal whether someone is likely to become a compulsive drinking later in life. Alcoholism is a chronic brain disease in which an individual drinks compulsively -- often accompanied by negative emotions. Whereas previous studies have focused on examining the brain after a drinking disorder develops, the researchers from the Salk Institute in California set out to prove that brain circuits can make some people more likely to be alcoholics.

Elon Musk believes his neural technology company Neuralink will be able to "solve" schizophrenia and autism. Speaking on the Artificial Intelligence podcast with Lex Fridman, published Tuesday, Musk was asked what he thinks are the most exciting impacts he foresees for his company Neuralink. Neuralink's goal is to develop an AI-enabled chip that could be implanted in a person's brain, where it would be able to both record brain activity and potentially stimulate it. "So Neuralink, I think at first will solve a lot of brain-related diseases. So could be anything from like autism, schizophrenia, memory loss -- like everyone experiences memory loss at certain points in age. Parents can't remember their kids' names and that kind of thing," replied Musk.

Elon Musk's Neuralink has been on a hiring spree since summer. Tesla and SpaceX CEO Elon Musk doesn't often publicly talk about his low-profile side hustle at biotech startup Neuralink. But when he does, the news is usually far more exiting than any of his updates on electric cars or rockets. In July, Neuralink published a white paper about an implantable brain chip it had been working on, which Musk said would help "merge biological intelligence with machine intelligence." This week, speaking on the Artificial Intelligence podcast hosted by MIT research scientist Lex Fridman, Musk shared a more detailed explanation of how things are unfolding at Neuralink and his ultimate vision for the sci-fi-sounding device that's in the making.

Elon Musk believes his neural technology company Neuralink will be able to "solve" schizophrenia and autism. Speaking on the Artificial Intelligence podcast with Lex Fridman, published Tuesday, Musk was asked what he thinks are the most exciting impacts he foresees for his company Neuralink. Neuralink's goal is to develop an AI-enabled chip that could be implanted in a person's brain, where it would be able to both record brain activity and potentially stimulate it. "So Neuralink, I think at first will solve a lot of brain-related diseases. So could be anything from like autism, schizophrenia, memory loss -- like everyone experiences memory loss at certain points in age. Parents can't remember their kids' names and that kind of thing," replied Musk.

Despite ample evidence that our concepts, our cognitive architecture, and mathematics itself are all deeply compositional, few models take advantage of this structure. We therefore propose a radically compositional approach to computational neuroscience, drawing on the methods of applied category theory. We describe how these tools grant us a means to overcome complexity and improve interpretability, and supply a rigorous common language for scientific modelling, analogous to the type theories of computer science. As a case study, we sketch how to translate from compositional narrative concepts to neural circuits and back again.