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Google Brain Team

Make machines intelligent. Improve people’s lives.

Research Freedom

Google Brain team members set their own research agenda, with the team as a whole maintaining a portfolio of projects across different time horizons and levels of risk.

Google Scale

As part of Google and Alphabet, the team has resources and access to projects impossible to find elsewhere. Our broad and fundamental research goals allow us to actively collaborate with, and contribute uniquely to, many other teams across Alphabet who deploy our cutting edge technology into products.

Open Culture

We believe that openly disseminating research is critical to a healthy exchange of ideas, leading to rapid progress in the field. As such, we publish our research regularly at top academic conferences and release our tools, such as TensorFlow, as open source projects.

Working on the Brain Team

• Looking Back on 2017
• Meet a few of our machine learning makers
• Colin Raffel's Year at Brain
• Google Brain Residency Program — 7 months in and looking ahead
• Google Brain Residency Program — One Year Later
• Google Brain Team's Approach to Research
• Looking Back on 2016
• Reddit AMA 2016
• Reddit AMA 2017
• The Great A.I. Awakening

Papers Accepted to NIPS, 2017

• A Meta-Learning Perspective on Cold-Start Recommendations for Items
• AdaGAN: Boosting Generative Models
• Affine-Invariant Online Optimization
• Approximation and Convergence Properties of Generative Adversarial Learning
• Attention is All You Need
• Avoiding discrimination through causal reasoning
• Bridging the Gap Between Value and Policy Based RL
• Dynamic Routing between Capsules
• Filtering Variational Objectives
• Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
• Investigating the learning dynamics of deep neural networks using random matrix theory
• Learning Hierarchical Information Flow with Recurrent Neural Modules
• Modulating early visual processing by language
• Multi-Armed Bandits with Metric Movement Costs
• Nonlinear random matrix theory for deep learning
• On Blackbox Backpropagation and Jacobian Sensing
• PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
• REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
• Reducing Reparameterization Gradient Variance
• SGD learns the conjugate class of the network
• SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement
• The Unreasonable Effectiveness of Random Orthogonal Embeddings
• Value Prediction Network

Papers Accepted to CoRL, 2017

• End-to-End Learning of Semantic Grasping
• Learning Robotic Manipulation of Granular Media
• Manifold Regularization for Kernelized LSTD
• Trust-PCL: An Off-Policy Trust Region Method for Continuous Control

Some of Our Research Areas

Some of our team, join the team.

Most of the Brain team is based in Mountain View, but we have smaller groups of team members in Cambridge (Massachusetts), London, Montreal, New York, San Francisco, Toronto and Zurich.

MIT Technology Review

• Newsletters

Google helped make an exquisitely detailed map of a tiny piece of the human brain

A small brain sample was sliced into 5,000 pieces, and machine learning helped stitch it back together.

• Cassandra Willyard archive page

A team led by scientists from Harvard and Google has created a 3D, nanoscale-resolution map of a single cubic millimeter of the human brain. Although the map covers just a fraction of the organ—a whole brain is a million times larger—that piece contains roughly 57,000 cells, about 230 millimeters of blood vessels, and nearly 150 million synapses. It is currently the highest-resolution picture of the human brain ever created.

To make a map this finely detailed, the team had to cut the tissue sample into 5,000 slices and scan them with a high-speed electron microscope. Then they used a machine-learning model to help electronically stitch the slices back together and label the features. The raw data set alone took up 1.4 petabytes. “It’s probably the most computer-intensive work in all of neuroscience,” says Michael Hawrylycz, a computational neuroscientist at the Allen Institute for Brain Science, who was not involved in the research. “There is a Herculean amount of work involved.”

Many other brain atlases exist, but most provide much lower-resolution data. At the nanoscale, researchers can trace the brain’s wiring one neuron at a time to the synapses, the places where they connect. “To really understand how the human brain works, how it processes information, how it stores memories, we will ultimately need a map that’s at that resolution,” says Viren Jain, a senior research scientist at Google and coauthor on the paper, published in Science on May 9 . The data set itself and a preprint version of this paper were released in 2021 .

Brain atlases come in many forms. Some reveal how the cells are organized. Others cover gene expression. This one focuses on connections between cells, a field called “connectomics.” The outermost layer of the brain contains roughly 16 billion neurons that link up with each other to form trillions of connections. A single neuron might receive information from hundreds or even thousands of other neurons and send information to a similar number. That makes tracing these connections an exceedingly complex task, even in just a small piece of the brain..  

To create this map, the team faced a number of hurdles. The first problem was finding a sample of brain tissue. The brain deteriorates quickly after death, so cadaver tissue doesn’t work. Instead, the team used a piece of tissue removed from a woman with epilepsy during brain surgery that was meant to help control her seizures.

Once the researchers had the sample, they had to carefully preserve it in resin so that it could be cut into slices, each about a thousandth the thickness of a human hair. Then they imaged the sections using a high-speed electron microscope designed specifically for this project. 

Next came the computational challenge. “You have all of these wires traversing everywhere in three dimensions, making all kinds of different connections,” Jain says. The team at Google used a machine-learning model to stitch the slices back together, align each one with the next, color-code the wiring, and find the connections. This is harder than it might seem. “If you make a single mistake, then all of the connections attached to that wire are now incorrect,” Jain says. 

“The ability to get this deep a reconstruction of any human brain sample is an important advance,” says Seth Ament, a neuroscientist at the University of Maryland. The map is “the closest to the  ground truth that we can get right now.” But he also cautions that it’s a single brain specimen taken from a single individual. 

The map, which is freely available at a web platform called Neuroglancer , is meant to be a resource other researchers can use to make their own discoveries. “Now anybody who’s interested in studying the human cortex in this level of detail can go into the data themselves. They can proofread certain structures to make sure everything is correct, and then publish their own findings,” Jain says. (The preprint has already been cited at least 136 times .) 

The team has already identified some surprises. For example, some of the long tendrils that carry signals from one neuron to the next formed “whorls,” spots where they twirled around themselves. Axons typically form a single synapse to transmit information to the next cell. The team identified single axons that formed repeated connections—in some cases, 50 separate synapses. Why that might be isn’t yet clear, but the strong bonds could help facilitate very quick or strong reactions to certain stimuli, Jain says. “It’s a very simple finding about the organization of the human cortex,” he says. But “we didn’t know this before because we didn’t have maps at this resolution.”

The data set was full of surprises, says Jeff Lichtman, a neuroscientist at Harvard University who helped lead the research. “There were just so many things in it that were incompatible with what you would read in a textbook.” The researchers may not have explanations for what they’re seeing, but they have plenty of new questions: “That’s the way science moves forward.” 

Biotechnology and health

A controversial Chinese CRISPR scientist is still hopeful about embryo gene editing. Here’s why.

He Jiankui, who went to prison for three years for making the world’s first gene-edited babies, talked to MIT Technology Review about his new research plans.

• Zeyi Yang archive page

Controversial CRISPR scientist promises “no more gene-edited babies” until society comes around

In a public interview, Chinese biophysicist He Jiankui said he is receiving offers of financial support from figures in the US.

• Antonio Regalado archive page

IVF alone can’t save us from a looming fertility crisis

Family-friendly policies and gender equality might be more helpful than technology

• Jessica Hamzelou archive page

How our genome is like a generative AI model

Our genetic code works a bit like DALL-E, apparently

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Mouse brain research is helping us better understand human minds

Jul 08, 2024

[[read-time]] min read

Researchers on our Connectomics team have completed the largest ever AI-assisted digital reconstruction of human brain tissue. Here’s why they’re taking on the mouse brain next.

• General summary

Researchers at Google are using mice to better understand the human brain. By mapping the mouse's hippocampus, which is responsible for memory, attention, and spatial navigation, scientists hope to gain insights into how our own minds work. This project is a major technical challenge, as the dataset from one mouse brain connectome could be the largest biological dataset ever collected.

• Bullet points
• Mouse brain research helps us understand human minds.
• Scientists are mapping the mouse's hippocampus, responsible for memory and spatial navigation.
• Mouse brains are like miniature human brains and offer valuable insights.
• Mapping a mouse brain is technically challenging due to vast data volumes.
• Google's Connectomics team develops tools to process and analyze large brain datasets.
• Basic explainer

Scientists are studying mouse brains to better understand human brains. Mouse brains are like tiny versions of human brains. Mapping a mouse brain is super hard, but it's a step towards understanding our own minds. This research could help us learn more about memory, sleep, and brain diseases.

Explore other styles:

Google researchers recently unveiled the largest, most detailed map of the human brain yet . It described just 1 cubic millimeter of brain tissue — the size of half a grain of rice — but at high enough resolution to show individual neurons and their connections to each other, and required 1.4 petabytes of data to encode.

Although it’s only a tiny sliver of the brain, the map led to several surprising discoveries. “For example, we found some of the wires will wrap themselves into these giant knots,” Google Research Scientist Viren Jain says of the neurons. “We have no idea why — nobody's ever seen it before.”

Now, Viren and his team have got mice on the brain. And for good reason — these mammals may help solve mysteries about our minds that have eluded us since our beginnings. Mysteries like: How are memories stored and retrieved? How do we recognize objects and faces? Why do we need so much sleep? And what goes wrong in Alzheimer’s and other brain diseases?

“One reason we don’t have answers to these questions is that we don’t yet have the data we need in order to study the brain,” Viren says.

The human brain has about 86 billion neurons connected to each other by more than 100 trillion synapses that enable you to think, feel, move and interact with the world. By creating a map of these neural connections — known as a “connectome” — we can unlock new understanding about how our brains work, and why sometimes they don’t.

To build detailed maps at the synaptic level, researchers need to image the brain at nanometer resolution and work with massive amounts of data. It’s a significant technical challenge that requires continued innovation in imaging techniques, AI algorithms and data management tools. That’s why, 10 years ago, Google Research formed its Connectomics team .

Over the past decade, the team has developed technologies to more efficiently process, analyze and share data , enabling researchers to dramatically scale up progress on understanding the brain. For example, they introduced flood-filling networks , which replaced the manual effort of coloring in cells across brain images by using machine learning to automatically trace the paths of neurons through layers of tissue. Building on this, their SegCLR algorithm automatically identifies distinct parts of cells and cell types within these networks. And they created software such as TensorStore and Neuroglancer which helps store, process and visualize large multidimensional images and volumes.

Still, mapping the entire human brain connectome would require gathering and analyzing as much as a zettabyte of data (one billion terabytes), which is beyond the current capabilities of existing technologies. “If we were to map the whole human brain right now, it might take billions of dollars and hundreds of years,” Viren says.

Instead, researchers focus on either mapping large portions of the brains of small animals, or small pieces of brain tissue from large animals. In 2020, the Connectomics team mapped half of a fruit fly’s brain , revealing the connections among 25,000 neurons. Through collaborations with researchers in the field, they’ve also created connectomes for portions of the brains of the zebra finch and zebrafish larvae . And in May, the aforementioned map of 1 cubic millimeter of human brain tissue was published in Science .

Thousands of researchers around the world have used the datasets from these projects, resulting in hundreds of published discoveries.

Researchers built a 3D image of nearly every neuron and their connections within a small piece of human brain tissue. The top image shows excitatory neurons and the bottom image shows inhibitory neurons.

Now, the Connectomics team is working with partners at Harvard, Princeton and elsewhere to map the mouse’s hippocampus — the part of the brain responsible for encoding memories, attention and spatial navigation, and representing 2-3% of the entire mouse brain.

Without the time or technology to map the entire human brain, analyzing a mouse connectome is the next best thing. It’s small enough to be technically feasible and could potentially deliver insights relevant to our own minds. “When you look at a mouse brain in the electron microscope, it looks exactly like a human brain. It is, in fact, a miniature version of a human brain,” Jeff W. Lichtman, a professor of molecular and cellular biology at Harvard, says. That's why scientists frequently use mice to study human brain disorders.

Mice are just the latest connectome frontier; neuroscientists have been working for decades to map increasingly larger and more complicated brains. The first connectome was for a worm’s brain — published in 1986, it took 16 years to map.

Connectomics across the decades.

Even though a mouse brain is 1,000 times smaller than a human brain, mapping one is still an immense technical challenge. The dataset from one mouse brain connectome at nanometer resolution could be the largest biological dataset ever collected, estimated at about 20,000-30,000 terabytes.

“So not just acquiring the data, but even just storing and accurately processing all that data is a major challenge,” Viren says. “But that’s been our unique contribution to the field: developing tools that push the state of the art in accuracy and then really applying them at scale to larger and larger datasets.”

If successful, the Connectomics team's mouse brain project will be the first time scientists have mapped part of a mammalian hippocampus. It’s also the largest-ever chunk of any brain researchers have attempted to map.

“Fundamental research generates extraordinary value,” Viren says. “What gets me excited is that one day, we’ll have a precise understanding of how we form memories and what underlies mental disorders or diseases. But in order to do that, we have to create this loop of technology that would have been unimaginable as recently as two decades ago.”

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Neural mapping

Google Research is driving progress toward precisely mapping the connections between every cell in the brain.

What is connectomics?

The human brain is perhaps the most computationally complex machine in existence, consisting of networks of billions of cells. Researchers currently don’t understand the full picture of how glitches in its network machinery contribute to mental illnesses and other diseases, such as dementia. However, the emerging  connectomics field, which aims to precisely map the connections between every cell in the brain, could help solve that problem.

The Connectomics team at Google Research has played a key role in advancing the connectonomics field by developing new technologies that have accelerated scientific progress. These technologies enabled us to map parts of the  fruit fly , mouse and human brain , and could one day help us better understand how the human brain works and how to treat brain diseases. The timeline and chart below demonstrate how connectomics has evolved since the 1970s.

Connectomics milestones

1986: roundworm connectome.

In 1986, a team of researchers successfully completed the  C. elegans roundworm’s connectome , which contains 302 neurons. This effort was completed over 16 years, and required researchers to use markers to trace the paths of the roundworm’s neurons on enlarged photographs.

2020: Fruit fly hemibrain

In 2020, Google Research and collaborators released the fruit fly "hemibrain"  connectome , an online database providing the morphological structure and synaptic connectivity of roughly half of the brain of a fruit fly. This database and its  visualization  has  reframed  the way that neural circuits are studied and understood in the fly brain. The project used machine learning tools  developed at Google  to map about 125,000 neurons an order of magnitude faster than would have been possible without these tools.

2021: The first human connectome

In 2021, Google Research and collaborators released the  H01 dataset , a 1.4 petabyte rendering of a small sample of human brain tissue. This was the first-ever  human connectome project , which used tools and software developed at Google Research to map a small part (roughly one cubic millimeter) of the human brain — all easily accessible with the  Neuroglancer browser interface . The project unveiled preliminary insights into the structure of the human cortex.

2023: Mapping the mouse brain

In September 2023, Google Research and  collaborators  announced the launch of the largest connectome project yet: mapping the connectome of 2-3 percent of the  mouse brain  over the next five years. The project will specifically target the  hippocampal  region, which is responsible for encoding memories, attention and spatial navigation. The initiative, supported by the  National Institutes of Health , is part of a program that aims to create a whole mouse brain connectome.

Collaborations with the research community

A photo of Gerry Rubin. Photo credit: Matt Staley, HHMI’s Janelia Research Campus

More than a "mass of spaghetti"

Gerry Rubin, director of the Howard Hughes Medical Institute's Janelia Research Campus, and a team of researchers constructed the fruit fly connectome, revolutionizing brain mapping.

Link to full story

A photo of Gwyneth Card . Photo credit: John Abbott, Columbia's Zuckerman Institute

Escaping danger

Gwyneth Card, a neuroscientist who leads a lab at Columbia University's Zuckerman Institute, researches how insect brains are wired to help them flee from predators.

A photo of Jeff Lichtman .

The mouse connectome moonshot

Jeff Lichtman, a neuroscientist who leads the Lichtman Lab at Harvard University, is working towards mapping the first whole mammalian brain.

Harvard University COVID-19 updates

Department News

Lichtman lab teams with google to map 150 million synapses in human brain sample.

• May 9, 2024

Since 2018, the Lichtman Lab has been painstakingly mapping every cell and synapse in a tiny brain sample from a human patient. Although the sample represents only one millionth of the volume of a human brain, mapping it in such detail generated 1.4 million gigabytes of data. To manage such a large dataset, the Lichtman Lab teamed with Google’s connectomics research group. The collaboration yielded the largest connectomics map of a human sample produced so far. This week, the project is being capped off with a paper in Science . Data from the project has been made available online for other researchers to peruse or analyze.

Seeking insight into the human brain’s microstructure was a strong motivator for the team behind the new paper. “All of the thousands of brain cells and tens of millions of synaptic connections identified in our study would show up on a standard MRI scan as a single pixel!” says co-first author Alexander Shapson-Coe, who was a postdoc in the Lichtman Lab at the time of the study. 

In their map, the co-authors saw several unexplained phenomena. Many pairs of neurons in the sample had extremely strong connections, with dozens of synapses connecting the same two cells. Such strong connections have not been reported in mouse brains. They also identified pairs of triangular cells pointing in opposite directions and “whorls” or “spools” of axons, where the axons would form complex curlicue shapes. The researchers don’t know how any of these structures influence function. This paper is only a first glimpse.

“We provide a lot of basic measurements for the distribution of different cell types and  synapses in the sample”, says research scientist Daniel Berger of the Lichtman Lab, who is one of the co-first authors on the paper. “It establishes some ground rules for the human brain, with the caveat that it’s from an epilepsy patient, so we will need some other samples.” Connectomic studies of even larger samples of human brain tissue are already in the works. 

Berger adds that this particular study came about because of a series of lucky coincidences. The first was that collecting an excised bit of tissue from a surgery on an epileptic patient yielded a sample, nicknamed “H01”, which was of high enough quality to undergo intense connectomic analysis. The second was Shapson-Coe joining the Lichtman Lab and eagerly pushing for imaging and studying the sample. The third was that Google’s connectomics group was interested in a new data set and able to help store and process the data. 

“We (the Connectomics Group at Google Research) started collaborating with Professor Lichtman all the way back in 2018,” says Google staff research scientist and co-first author Michal Januszewski. “My expertise was in analyzing the largest connectomics datasets available at the time—about 2% the size of the H01 sample. Our group was looking to scale up our methods, and the Lichtman lab had the technology and know-how to acquire the necessary data.”  

The co-authors are proud of their accomplishment and look forward to future findings. “To image a piece of human brain tissue, with a size on the order of millimeters, and a resolution on the order of nanometers, is unprecedented, and we have only scratched the surface in terms of biological analyses of the dataset,” says Shapson-Coe. “We have developed tools to enable the exploration and analysis of this dataset by any interested individual or group; I expect the most notable discoveries from this dataset are yet to be made!” 

Jeff Lichtman

Lichtman Lab

(l to r) Jeff Lichtman, Daniel Berger, Neha Karlupia, Jakob Troidl, Evelina Sjostedt, Hanspeter Pfister, Shuohong Wang, Yuelong Wu, and Richard Schalek

The Google Brain Team — Looking Back on 2017 (Part 2 of 2)

Brain Health

What is brain health and why is it important, yongjun wang.

1 Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

2 China National Clinical Research Center for Neurological Diseases, Beijing, China

Yuesong Pan

Yongjun Wang and colleagues discuss the definition of brain health and the opportunities and challenges of future research

The human brain is the command centre for the nervous system and enables thoughts, memory, movement, and emotions by a complex function that is the highest product of biological evolution. Maintaining a healthy brain during one’s life is the uppermost goal in pursuing health and longevity. As the population ages, the burden of neurological disorders and challenges for the preservation of brain health increase. It is therefore vital to understand what brain health is and why it is important. This article is the first in a series that aims to define brain health, analyse the effect of major neurological disorders on brain health, and discuss how these disorders might be treated and prevented.

Definition of brain health

Currently, there is no universally recognised definition of brain health. Most existing definitions have only a general description of normal brain function or emphasise one or two dimensions of brain health. The US Centers for Disease Control and Prevention defined brain health as an ability to perform all the mental processes of cognition, including the ability to learn and judge, use language, and remember. 1 The American Heart Association/American Stroke Association (AHA/ASA) presidential advisory defined optimal brain health as “average performance levels among all people at that age who are free of known brain or other organ system diseases in terms of decline from function levels, or as adequacy to perform all activities that the individual wishes to undertake.” 2

The brain is a complex organ and has at least three levels of functions that affect all aspects of our daily lives: interpretation of senses and control of movement; maintenance of cognitive, mental, and emotional processes; and maintenance of normal behaviour and social cognition. Brain health may therefore be defined as the preservation of optimal brain integrity and mental and cognitive function at a given age in the absence of overt brain diseases that affect normal brain function.

Effect of major neurological disorders on brain health

Several neurological disorders may disrupt brain function and affect humans’ health. Medically, neurological disorders that cause brain dysfunction can be classified into three groups:

• Brain diseases with overt damage to brain structures, such as cerebrovascular diseases, traumatic brain injury, brain tumours, meningitis, and communication and sensory disorders
• Functional brain disorders with detectable destruction of brain connections or networks, such as neurodegenerative diseases (eg, Parkinson’s disease, Alzheimer’s disease, and other dementias) and mental disorders (eg, schizophrenia, depression, bipolar disorder, alcoholism, and drug abuse)
• Other brain disorders without detectable structural or functional impairment, such as migraine and sleep disorders.

These neurological disorders may have different or common effects on brain health and function. For instance, Alzheimer’s disease is the main type of dementia, with a decline in different domains of cognitive function. Mood disorders may cause dysfunction in execution, reward processing, and emotional regulations. In addition to physical disability, aphasia, gait and balance problems, and cerebrovascular diseases may lead to cognitive impairment and dementia, which are neglected by both patients and physicians.

Ageing and burden of neurological disorders

Human ageing is mainly reflected in the aspects of brain ageing and degradation of brain function. The number of people aged 60 years and over worldwide was around 900 million in 2015 and is expected to grow to two billion by 2050. 3 With the increases in population ageing and growth, the burden of neurological disorders and challenges to the preservation of brain health steeply increase. People with neurological disorders will have physical disability, cognitive or mental disorders, and social dysfunction and be a large economic burden.

Globally, neurological disorders were the leading cause of disability adjusted life years (276 million) and the second leading cause of death (9 million) in 2016, according to the Global Burden of Diseases study. 4 Stroke, migraine, Alzheimer’s disease and other dementias, and meningitis are the largest contributors to neurological disability adjusted life years. 4 About one in four adults will have a stroke in their lifetime, from the age of 25 years onwards. 5 Roughly 50 million people worldwide were living with dementia in 2018, and the number will more than triple to 152 million by 2050. 6 In the following decades, governments will face increasing demand for treatment, rehabilitation, and support services for neurological disorders.

Opportunities and challenges of future research on brain health

Opportunities and challenges exist in the assessment of brain health, the mechanism of brain function and dysfunction, and approaches to promote brain health ( box 1 ).

• Lack of metrics or tools to comprehensively assess or quantify brain health
• Little knowledge about the mechanisms of brain function and dysfunction
• Few effective approaches to prevent and treat brain dysfunction in some major neurological disorders, such as dementia
• Need to precisely preserve brain functions for people with neurosurgical diseases

Defining and promoting optimal brain health require the scientific evaluation of brain health. However, it is difficult to comprehensively evaluate or quantify brain health through one metric owing to the multidimensional aspects of brain health. Many structured or semistructured questionnaires have been developed to test brain health by self-assessments or close family member assessments of daily function or abilities. In recent decades new structural and functional neuroimaging techniques have been applied to evaluate brain network integrity and functional connectivity. 7 However, these subjective or objective measures have both strengths and weaknesses. For instance, scales such as the mini-mental state examination and Montreal cognitive assessment are simple and easy to implement but are used only as global screening tools for cognitive impairment; tests such as the digit span, Rey-Osterrieth complex figure test, trail making A and B, Stroop task, verbal fluency test, Boston naming test, and clock drawing test are used mainly to assess one or two specific domains of memory, language, visuospatial, attention, and executive function; and neuroimaging techniques, although non-invasive and objective, still have disadvantages of test contraindications, insufficient temporal or spatial resolution, motion artefact, and high false discovery rates, which limit their clinical transformation.

Another difficulty in measuring brain health is that age, culture, ethnicity, and geography specific variations exist in the perception of optimal brain health. Patient centred assessment of brain function, such as self-perception of cognitive function and quality of life, should also be considered when measuring brain health. 8 Universal acceptable, age appropriate, multidimensional, multidisciplinary, and sensitive metrics or tools are required to comprehensively measure and monitor brain function and brain health.

To promote optimal brain health, we need a better understanding of the mechanisms of brain function and dysfunction. Unfortunately, little is known about the working mechanism of the brain. Although we have made considerable developments in neuroscience in recent decades, we still cannot totally decipher the relations between spatiotemporal patterns of activity across the interconnected networks of neurons and thoughts or the cognitive and mental state of a person. 9 Recent progress in brain simulation and artificial intelligence provides a vital tool to understand biological brains, and vice versa. 10 11 The development of brain inspired computation, brain simulation, and intelligent machines was emphasised in the European Union and China Brain Project. 9 12

Meanwhile, the mechanisms behind the brain dysfunction in some neurological disorders are still not well understood, especially for mental and neurodegenerative disorders. Further investigation of the mechanisms of brain diseases may indicate approaches to treatment and improve brain function. Brain imaging based cognitive neuroscience may unravel the underlying brain mechanism of cognitive dysfunction and provide an avenue to develop a biological framework for precision biomarkers of mood disorders. 13

Most common neurological diseases, such as cerebrovascular diseases and Alzheimer’s disease, have complex aetiopathologies, typically involving spatial-temporal interactions of genetic and environmental factors. However, a single genetic factor could account for the disease progression of monogenic neurological disorders. These diseases could be more readily investigated by simplified cross species modelling, leading to better understanding of their mechanisms and greater efficiency in testing innovative therapies. Such research may provide a window to promote the investigation of common neurological disorders and general brain health, as discussed by Chen and colleagues elsewhere in this series. 14

Few effective approaches are available to prevent and treat brain dysfunction in some major neurological disorders, such as dementia. Neurons are not renewable, and brain dysfunction is always irreversible. Recent trials targeting amyloid clearance and the selective inhibition of tau protein aggregation failed to improve cognition or modify disease progression in patients with mild Alzheimer’s disease. 15 16 More attention has focused on other potential therapeutic targets, such as vascular dysfunction, inflammation, and the gut microbiome, as discussed by Shi and colleagues. 17 In particular, recent studies showed that the early impairment of cognition was induced by the disruption of neurovascular unit integrity, which may cause hypoperfusion and the breakdown of the blood-brain barrier and subsequent impairment in the clearance of proteins in the brain. 18 19 Physical activity, mental exercise, a healthy diet and nutrition, social interaction, ample sleep and relaxation, and control of vascular risk factors are considered six pillars of brain health. The AHA/ASA presidential advisory recommended the AHA’s Life’s Simple 7 (non-smoking, physical activity, healthy diet, appropriate body mass index, blood pressure, total cholesterol, and blood glucose) to maintain optimal brain health. 2 Pan and colleagues discuss how this may indicate a new dawn of preventing some cognitive impairment and brain dysfunction by preventing vascular risk factors or cerebrovascular diseases. 20

For other neurological disorders with potential therapeutic approaches, the main aim is to preserve brain function. Impaired brain function due to anatomical structural damage is underestimated in patients with neurosurgical diseases such as brain tumours, trauma, and epilepsy. In recent years, treatment targets for neurosurgical diseases have changed from focusing on survival or life expectancy to balancing brain structures and functions. Precise preservation of brain function requires an understanding of the exquisite relation between brain structure and function and advanced technologies to visualise brain structure-function relations. 21

Another example of the predicament associated with protection of brain function is uncertainty in the treatment response in epilepsy management. Current standard care for epilepsy relies on a trial and error approach of sequential regimens of antiseizure medications. The time delay due to this treatment approach means that such treatments may be less effective and irreversible damage may occur. Chen and colleagues 22 describe how recent advances in personalised epilepsy management based on artificial intelligence, genomics, and patient derived stem cells are bringing some hope to overcome this predicament in epilepsy management and promise a more effective strategy. 23 24

Brain health is the maintenance of multidimensional aspects of brain function. However, several neurological disorders may affect brain health in one or more aspects of brain function. Deciphering and promoting the function and health of the brain, the most mysterious organ in the human body, will have a dramatic impact on science, medicine, and society. 25 In the past seven years, a number of large scale brain health initiatives have been launched in several countries to promote the development of neuroscience, brain simulation, and brain protection. 9 However, further challenges are raised by the different key research directions of brain projects in different countries. In the face of these challenges, Liu and colleagues argue that collaboration on brain health research is urgently needed. 26 As the other articles in this series describe, coordinated research has enormous potential to improve the prognosis of brain disorders.

Key messages

• Brain health is the preservation of optimal brain integrity and mental and cognitive function and the absence of overt neurological disorders
• Human ageing increases the burden of brain dysfunction and neurological diseases and the demands for medical resources
• Further studies are required to assess brain health, understand the mechanism of brain function and dysfunction, and explore effective approaches to promote brain health.

Contributors and sources: YW proposed the idea for this series on brain health. YW and YP drafted the first manuscript. All the authors critically reviewed and revised the manuscript. YP and HL expertise is in the area of clinical research methods and clinical research on stroke. YW is an expert in clinical research on stroke and neurological diseases. YW is the guarantor.

Competing interests We have read and understood BMJ policy on declaration of interests and declare that the study was supported by grants from the National Science and Technology Major Project (2017ZX09304018), National Key R&D Program of China (2018YFC1312903, 2017YFC1310902, 2018YFC1311700, and 2018YFC1311706), National Natural Science Foundation of China (81971091), Beijing Hospitals Authority Youth Programme (QML20190501), and Beijing Municipal Science and Technology Commission (D171100003017002).

Provenance and peer review: Commissioned; externally peer reviewed.

This article is part of a series launched at the Chinese Stroke Association annual conference on 10 October 2020, Beijing, China. Open access fees were funded by the National Science and Technology Major Project. The BMJ peer reviewed, edited, and made the decision to publish these articles.

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Princeton Neuroscience Institute

Pni innovator awards spur new research on ai, the brain, and hormones.

For the first time, two senior postdoctoral researchers earned the annual Innovator Awards this year at the Princeton Neuroscience Institute (PNI), along with the traditional two awards reserved for pairs of collaborating interdisciplinary faculty members.

“There’s a dearth of funding opportunities for senior postdoctoral researchers,” said Mala Murthy, Ph.D. , PNI's director and the Karol and Marnie Marcin ’96 professor of neuroscience. “By opening up PNI’s Innovator Awards to postdoctoral scholars, we aim to support exciting new research directions undertaken in collaboration with their advisors, and to facilitate transitions to the next career stage.” 

PNI postdoctoral research associate Rich Pang, Ph.D., and associate research scholar Chris Langdon, Ph.D. are the inaugural postdoc innovator awardees, as well as faculty member duos Uri Hasson, Ph.D. and Casey Lew-Williams, Ph.D., and Andrew Leifer, Ph.D. and Joshua Shaevitz, Ph.D.

Employing Machine Learning to Bridge Behavior and Whole-Brain Activity

Pang works across the labs of Dr. Murthy, PNI professor Jonathan Pillow, Ph.D. , and associate PNI faculty member and the John Archibald Wheeler/Battelle professor of physics William Bialek, Ph.D. Pang’s project will use recurrent neural networks to analyze troves of whole-brain activity recordings to better understand how neural activity propagates throughout the brain and how it produces behavior.

“All of the neural data I have studied to date has been from recordings of small numbers of neurons or in very limited conditions,” Pang wrote in his application. “This proposal is a new direction into the analysis and modeling of large-scale recordings across a variety of complex conditions.”

Inspired by recent work from the lab of Hakan Türeci, Ph.D. , a theoretical physicist and professor of electrical and computer engineering at Princeton, Pang will explore if Türeci’s newly developed framework for charting nonlinear dynamic systems, dubbed “eigentests,” can further reveal the computational properties of neural networks. 

Similar to Pang, Langdon, an associate research scholar in the lab of PNI associate professor Tatiana Engel, Ph.D. , will use the Innovator Award funds to support his work on using recurrent neural networks to bridge our understanding of brain circuit connectivity, neural activity, and behavior.

“Chris’s project has the potential to transform our understanding of how cognitive functions arise from dynamic interactions in neural circuits,” Engel wrote about Langdon’s proposal. “Confirmation of theoretical predictions about the existence of functional cell types and their relationship to the neural response dimensionality may reveal a universal organizing principle of cortical circuits.”

Watching 1,000 Days of Home Movies to Learn How Children Develop Language

The old parenting adage goes that when you start a family and have kids, “the days are long, but the years are short”.

For PNI and psychology professor Uri Hasson, Ph.D. and associated PNI faculty member Casey Lew-Willaims, Ph.D. , each day is about 1,500 hours long and stretches out until a child’s third birthday. 

That’s because Hasson and Lew-Williams will use their funds to support their unprecedented research that tracks children from the day they arrive home from the hospital until their third birthday.

Multiple cameras and microphones are installed across different rooms in each participating family’s house to track the development of fifteen babies in their natural home environment. In total, each three-year-old will have the most comprehensive and detailed home videos ever collected of the first 1000 days of their lives. 

In doing so, Hasson and Lew-Williams aim to gain a better understanding of how children naturally learn language by interacting with their supportive social environments. 

After resolving various ethical and technical obstacles, Hasson and Lew-Williams have recruited a diverse group of 15 families across New Jersey and Pennsylvania, including mixed-race, multigenerational, and LGBTQ+ families, which reflects each state’s demographics.

The Innovator Award funds will help support the team’s current recording endeavors and help build on their deep learning pipeline to sort through the petabytes of data collected across the project.

“With 100,000 one-minute audiovisual clips uploaded daily, we are collecting 36.5 million minutes annually, making the dataset too large to be manually labeled by human annotators,” the team wrote in their application. “The machine learning module will enable our team to quantify, for the first time, the natural, everyday statistics in infants’ environments that give rise to learning.”

Furthermore, their data will enable them to build a new generation of large language models capable of learning language from the child-centered linguistic input that each child receives in their natural environments.

Mapping How Brain Hormones Circulate to Understand Neuronal Communication

The pioneering neuroscientist Eve Marder often quips that neural circuit diagrams, like connectomes, are “…absolutely necessary but completely insufficient for understanding nervous system function.”

Missing from the brain’s road map is a fuller understanding of the traffic patterns, speed limits, and other cues that dictate how cells talk to one another. Neuroscientists have often focused on classic chemical messengers, like GABA and glutamate, but those neurotransmitters are only one of many modes of communication.

Unlike neurotransmitters, which go from one neuron to the next in like, neuropeptides are released from large packets that can travel many cells away from its host. That makes understanding who each cell is trying to communicate with much trickier to track.

To address this, PNI and physics associate professor Andrew Leifer, Ph.D. , and associated PNI professor Joshua Shaevitz, Ph.D. will collaborate for their Innovator Award to better understand where neuropeptides and what rules govern their communication style by studying such phenomena in the roundworm Caenorhabditis elegans . C. elegans , as it’s often abbreviated, has a relatively simple nervous system totaling 302 well understood and mapped neurons.

Leifer and Shaevitz aim to take advantage of the roundworm’s manageable roadmap by adding a new layer of understanding how peptides travel along its neural circuitry.

“We propose to use sophisticated new peptide sensors and volumetric functional imaging to directly measure brain-wide neuropeptide dynamics in C. elegans,  including where peptides go once released, how quickly they diffuse, and how anatomy influences their dynamics,” Leifer and Shaevitz wrote in their proposal. “The speed, extent and principles that govern how peptides travel will provide insights into their role in neural function.”

The Innovator Awards are generously supported by Endowments from the McDonnell Center for Systems Neuroscience, the Bezos Center for Neural Circuit Dynamics, and the Scully Center for the Neuroscience of Mind and Behavior.

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• NEWS EXPLAINER
• 28 August 2024

Mpox is spreading rapidly. Here are the questions researchers are racing to answer

• Sara Reardon

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Monkeypox virus particles (shown in this coloured electron micrograph) can spread through close contact with people and animals. Credit: NIAID/Science Photo Library

When the World Health Organization (WHO) declared a public-health emergency over mpox earlier this month , it was because a concerning form of the virus that causes the disease had spread to multiple African countries where it had never been seen before. Since then, two people travelling to Africa — one from Sweden and one from Thailand — have become infected with that type of virus, called clade Ib, and brought it back to their countries.

Monkeypox virus: dangerous strain gains ability to spread through sex, new data suggest

Although researchers have known about the current outbreak since late last year, the need for answers about it is now more pressing than ever. The Democratic Republic of the Congo (DRC) has spent decades grappling with monkeypox clade I virus — the lineage to which Ib belongs. But in the past, clade I infections usually arose when a person came into contact with wild animals, and outbreaks would fizzle out.

Clade Ib seems to be different, and is spreading largely through contact between humans, including through sex . Around 18,000 suspected cases of mpox, many of them among children, and at least 600 deaths potentially attributable to the disease have been reported this year in the DRC alone.

How does this emergency compare with one declared in 2022, when mpox cases spread around the globe? How is this virus behaving compared with the version that triggered that outbreak, a type called clade II? And will Africa be able to rein this one in? Nature talks to researchers about information they are rushing to gather.

Is clade Ib more deadly than the other virus types?

It’s hard to determine, says Jason Kindrachuk, a virologist at the University of Manitoba in Winnipeg, Canada. He says that the DRC is experiencing two outbreaks simultaneously. The clade I virus, which has been endemic in forested regions of the DRC for decades, circulates in rural regions, where people get it from animals. That clade was renamed Ia after the discovery of clade Ib. Studies in animals suggest that clade I is deadlier than clade II 1 — but Kindrachuk says that it’s hard to speculate on what that means for humans at this point.

Even when not fatal, mpox can trigger fevers, aches and painful fluid-filled skin lesions.

Growing mpox outbreak prompts WHO to declare global health emergency

Although many reports state that 10% of clade I infections in humans are fatal, infectious-disease researcher Laurens Liesenborghs at the Institute of Tropical Medicine in Antwerp, Belgium, doubts that this figure is accurate. Even the WHO’s latest estimate of a 3.5% fatality rate for people with mpox in the DRC might be high.

There are many reasons that fatality estimates might be unreliable, Liesenborghs says. For one, surveillance data capture only the most severe cases; many people who are less ill might not seek care at hospitals or through physicians, so their infections go unreported.

Another factor that can confound fatality rates is a secondary health condition. For example, people living with HIV — who can represent a large proportion of the population in many African countries — die from mpox at twice the rate of the general population 2 , especially if their HIV is untreated. And the relatively high death rate among children under age 5 could be partly because of malnutrition, which is common among kids in rural parts of the DRC, Liesenborghs says.

Is clade Ib more transmissible than other types?

The clade Ib virus has garnered particular attention because epidemiological data suggest that it transmits more readily between people than previous strains did, including through sexual activity, whereas clade Ia mostly comes from animals. An analysis posted ahead of peer review on the preprint server medRxiv 3 shows that clade Ib’s genome contains genetic mutations that seem to have been induced by the human immune system, suggesting that it has been in humans for some time. Clade Ia genomes have fewer of these mutations.

But Liesenborghs says that the mutations and clades might not be the most important factor in understanding how monkeypox virus spreads. Although distinguishing Ia from Ib is useful in tracking the disease, he says, the severity and transmissibility of the disease could be affected more by the region where the virus is circulating and the people there. Clade Ia, for instance, seems to be more common in sparsely populated rural regions where it is less likely to spread far. Clade Ib is cropping up in densely populated areas and spreading more readily.

Jean Nachega, an infectious-disease physician at the University of Pittsburgh in Pennsylvania, says that scientists don’t understand many aspects of mpox transmission — they haven’t even determined which animal serves as a reservoir for the virus in the wild, although rodents are able to carry it. “We have to be very humble,” Nachega says.

How effective are vaccines against the clade I virus?

Just as was the case during the COVID-19 pandemic, health experts are looking to vaccines to help curb this mpox outbreak. Although there are no vaccines designed specifically against the monkeypox virus, there are two vaccines proven to ward off a related poxvirus — the one that causes smallpox. Jynneos, made by biotechnology company Bavarian Nordic in Hellerup, Denmark, contains a type of poxvirus that can’t replicate but can trigger an immune response. LC16m8, made by pharmaceutical company KM Biologics in Kumamoto, Japan, contains a live — but weakened — version of a different poxvirus strain.

Hopes dashed for drug aimed at monkeypox virus spreading in Africa

Still, it’s unclear how effective these smallpox vaccines are against mpox generally. Dimie Ogoina, an infectious-disease specialist at Niger Delta University in Wilberforce Island, Nigeria, points out that vaccines have been tested only against clade II virus in European and US populations, because these shots were distributed by wealthy nations during the 2022 global outbreak . And those recipients were primarily young, healthy men who have sex with men, a population that was particularly susceptible during that outbreak. One study in the United States found that one dose of Jynneos was 80% effective at preventing the disease in at-risk people, whereas two doses were 82% effective 4 ; the WHO recommends getting both jabs.

People in Africa infected with either the clade Ia or Ib virus — especially children and those with compromised immune systems — might respond differently. However, one study in the DRC found that the Jynneos vaccine generally raised antibodies against mpox in about 1,000 health-care workers who received it 5 .

But researchers are trying to fill in some data gaps. A team in the DRC is about to launch a clinical trial of Jynneos in people who have come into close contact with the monkeypox virus — but have not shown symptoms — to see whether it can prevent future infection, or improve outcomes if an infection arises.

Will the vaccines help to rein in the latest outbreak?

Mpox vaccines have been largely unavailable in Africa, but several wealthy countries have pledged to donate doses to the DRC and other affected African nations. The United States has offered 50,000 Jynneos doses from its national stockpile, and the European Union has ordered 175,000, with individual member countries pledging extra doses. Bavarian Nordic has also added another 40,000. Japan has offered 3.5 million doses of LC16m8 — for which only one jab is recommended instead of two.

Monkeypox in Africa: the science the world ignored

None of them have arrived yet, though, says Espoir Bwenge Malembaka, an epidemiologist at the Catholic University of Bukavu in the DRC. Low- and middle-income nations cannot receive vaccines until the WHO has deemed the jabs safe and effective. And the WHO has not given its thumbs up yet. It is evaluating data from vaccine manufacturers, delaying donors’ ability to send the vaccines.

Even when the vaccines arrive, Bwenge Malembaka says, “it’s really a drop in the bucket”. The Africa Centres for Disease Control and Prevention in Addis Ababa, Ethiopia, estimates that 10 million doses are needed to rein in the outbreak.

Bwenge Malembaka says that the uncertainty over vaccine arrival has made it difficult for the government to form a distribution plan. “I don’t know how one can go about this kind of challenge,” he says. Bwenge Malembaka suspects that children are likely to receive doses first, because they are highly vulnerable to clade I, but officials haven’t decided which regions to target. It’s also unclear how the government would prioritize other vulnerable populations such as sex workers, who have been affected by clade Ib. Their profession is criminalized in the DRC, so they might not be able to come forward for treatment.

Researchers lament that public-health organizations didn’t provide vaccines and other resources as soon as the clade I outbreak was identified, especially given lessons learnt from the 2022 global mpox outbreak. “The opportunity was there a couple months ago to cut this transmission chain, but resources weren’t available,” Liesenborghs says. “Now, it will be more challenging to tackle this outbreak, and the population at risk is much broader.”

Nature 633 , 16-17 (2024)

doi: https://doi.org/10.1038/d41586-024-02793-9

Americo, J. L., Earl, P. L. & Moss, B. Proc. Natl Acad. Sci. USA 120 , e2220415120 (2023).

Article   PubMed   Google Scholar  

Yinka-Ogunleye, A. et al. BMJ Glob. Health 8 , e013126 (2023).

Kinganda-Lusamaki, E. et al. Preprint at medRxiv https://doi.org/10.1101/2024.08.13.24311951 (2024).

Yeganeh, N. et al. Vaccine 42 , 125987 (2024).

Priyamvada, L. et al. Vaccine 40 , 7321–7327 (2022).

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Title: brain-inspired artificial intelligence: a comprehensive review.

Abstract: Current artificial intelligence (AI) models often focus on enhancing performance through meticulous parameter tuning and optimization techniques. However, the fundamental design principles behind these models receive comparatively less attention, which can limit our understanding of their potential and constraints. This comprehensive review explores the diverse design inspirations that have shaped modern AI models, i.e., brain-inspired artificial intelligence (BIAI). We present a classification framework that categorizes BIAI approaches into physical structure-inspired and human behavior-inspired models. We also examine the real-world applications where different BIAI models excel, highlighting their practical benefits and deployment challenges. By delving into these areas, we provide new insights and propose future research directions to drive innovation and address current gaps in the field. This review offers researchers and practitioners a comprehensive overview of the BIAI landscape, helping them harness its potential and expedite advancements in AI development.

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Prioritizing the unexpected: New brain mechanism uncovered

Neuroscientists show how the brain implements responses to unexpected events.

Researchers have discovered how two brain areas, neocortex and thalamus, work together to detect discrepancies between what animals expect from their environment and actual events. These prediction errors are implemented by selective boosting of unexpected sensory information. These findings enhance our understanding of predictive processing in the brain and could offer insights into how brain circuits are altered in autism spectrum disorders (ASDs) and schizophrenia spectrum disorders (SSDs).

The research, published today in Nature, outlines how scientists at the Sainsbury Wellcome Centre at UCL studied mice in a virtual reality environment to take us a step closer to understanding both the nature of prediction error signals in the brain as well as the mechanisms by which they arise.

"Our brains constantly predict what to expect in the world around us and the consequences of our actions. When these predictions turn out wrong, this causes strong activation of different brain areas, and such prediction error signals are important for helping us learn from our mistakes and update our predictions. But despite their importance, surprisingly little is known about the neural circuit mechanisms responsible for their implementation in the brain," explained Professor Sonja Hofer, Group Leader at SWC and corresponding author on the paper.

To study how the brain processes expected and unexpected events, the researchers placed mice in a virtual reality environment where they could navigate along a familiar corridor to get to a reward. The virtual environment enabled the team to precisely control visual input and introduce unexpected images on the walls. By using a technique called two-photon calcium imaging, the researchers were able to record the neural activity from many individual neurons in primary visual cortex, the first area in our neocortex to receive visual information from the eyes.

"Previous theories proposed that prediction error signals encode how the actual visual input is different from expectations, but surprisingly we found no experimental evidence for this. Instead, we discovered that the brain boosts the responses of neurons that have the strongest preference for the unexpected visual input. The error signal we observe is a consequence of this selective amplification of visual information. This implies that our brain detects discrepancies between predictions and actual inputs to make unexpected events more salient" explained Dr Shohei Furutachi, Senior Research Fellow in the Hofer and Mrsic-Flogel labs at SWC and first author on the study.

To understand how the brain generates this amplification of the unexpected sensory input in the visual cortex, the team used a technique called optogenetics to inactivate or activate different groups of neurons. They found two groups of neurons that were important for causing the prediction error signal in the visual cortex: vasoactive intestinal polypeptide (VIP)-expressing inhibitory interneurons in V1 and a thalamic brain region called the pulvinar, which integrates information from many neocortical and subcortical areas and is strongly connected to V1. But the researchers found that these two groups of neurons interact in a surprising way.

"Often in neuroscience we focus on studying one brain region or pathway at a time. But coming from a molecular biology background, I was fascinated by how different molecular pathways synergistically interact to enable flexible and contextual regulation. I decided to test the possibility that cooperation could be occurring at the level of neural circuits, between VIP neurons and the pulvinar," explained Dr Furutachi.

And indeed, Dr Furutachi's work revealed that VIP neurons and pulvinar act synergistically together. VIP neurons act like a switch board: when they are off, the pulvinar suppresses activity in the neocortex, but when VIP neurons are on, the pulvinar can strongly and selectively boost sensory responses in the neocortex. The cooperative interaction of these two pathways thus mediates the sensory prediction error signals in visual cortex.

The next steps for the team are to explore how and where in the brain the animals' predictions are compared with the actual sensory input to compute sensory prediction errors and how prediction error signals drive learning. They are also exploring how their findings could help contribute to understanding ASDs and SSDs.

"It has been proposed that ASDs and SSDs both can be explained by an imbalance in the prediction error system. We are now trying to apply our discovery to ASDs and SSDs model animals to study the mechanistic neural circuit underpinnings of these disorders," explained Dr Furutachi.

This research was funded by the Sainsbury Wellcome Centre Core Grant from the Gatsby Charity Foundation and Wellcome (219627/Z/19/Z and 090843/F/09/Z); a Wellcome Investigator Award (219561/Z/19/Z); the Gatsby Charitable Foundation (GAT3212 and GAT3361); the Wellcome Trust (090843/E/09/Z and 217211/Z/19/Z); European Research Council (HigherVision 337797; NeuroV1sion 616509); the SNSF (31003A 169525); Biozentrum core funds (University of Basel).

• Nervous System
• Psychology Research
• Brain Tumor
• Neuroscience
• Brain-Computer Interfaces
• Animal Learning and Intelligence
• Encephalopathy
• Memory-prediction framework
• Autistic spectrum
• Neocortex (brain)
• Sensory system
• Social cognition

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Materials provided by Sainsbury Wellcome Centre . Note: Content may be edited for style and length.

Journal Reference :

• Shohei Furutachi, Alexis D. Franklin, Andreea M. Aldea, Thomas D. Mrsic-Flogel, Sonja B. Hofer. Cooperative thalamocortical circuit mechanism for sensory prediction errors . Nature , 2024; DOI: 10.1038/s41586-024-07851-w

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