New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Humans retrieve the memory of an event in reverse to how they saw it, a report published today has discovered. Instead of constructing a past memory by building a picture from details of the event, the brain forms an overall'gist' of what happened first. It then fills out the story by retrieving more detail. This process seems to be the opposite of how the brain works when first encountering an event. The latest findings may give scientists greater insight into the reliability and accuracy of memory and witness accounts of incidents such as crime.
A research collaboration headed up at the National University of Singapore (NUS) has successfully employed machine learning to investigate the cellular architecture of the human brain. The approach uses functional MRI (fMRI) data to automatically estimate brain parameters, enabling neuroscientists to infer the cellular properties of different brain regions without having to surgically probe the brain. The researchers say that their technique could potentially be used to assess treatment of neurological disorders or develop new therapies (Science Advances 10.1126/sciadv.aat7854). "The underlying pathways of many diseases occur at the cellular level, and many pharmaceuticals operate at the microscale level," explains team leader Thomas Yeo. "To know what really happens at the innermost levels of the human brain, it is crucial for us to develop methods that can delve into the depths of the brain non-invasively." Currently, most human brain studies employ non-invasive approaches such as MRI, which limits examination of the brain at a cellular level.
Brain development is a remarkable self-organization process in which cells proliferate, differentiate, migrate, and wire to form functional neural circuits. In humans, this process takes place over a long fetal phase and continues into the postnatal period, but it is largely inaccessible for direct, functional investigation at a cellular level. Therefore, the features that make the human central nervous system unique and the sequence of molecular and cellular events underlying brain disorders remain largely uncharted. Human pluripotent stem (hPS) cells, including those obtained by reprogramming somatic cells, have the ability to self-organize and differentiate when grown in three-dimensional (3D) aggregates rather than in direct contact with a flat plastic surface (1). Such 3D neural cultures, also known as organoids and organ spheroids, recapitulate many aspects of human brain development in vitro (1) and have the potential to accelerate progress in human neurobiology.
Before you start reading this article, there will be few perceptions that your brain(you) will be considering, why do we need to read this? What can we learn from this? Or by reading this, can we understand how the brains react and solve problems in different situations? This article is divided into three parts. In the first part of the article, we introduce Computational NeuroScience in brief which includes how neurons survive, the anatomy of the neurons and the models bound to the brain.
For all their fleshly failings, human brains are the model that computer engineers have always sought to emulate: huge processing power that's both surprisingly energy efficient, and available in a tiny form factor. But late last year, in an unprepossessing former metal works in Manchester, one machine became the closest thing to an artificial human brain there is. The one-million core SpiNNaker -- short for Spiking Neural Network Architecture -- is the culmination of decades of work and millions of pounds of investment. The result: a massively parallel supercomputer designed to mimic the workings of the human brain, which it's hoped will give neuroscientists a new understanding of how the mind works and open up new avenues of medical research. The genesis of the project lies in the late 1990s, with the work of Steve Furber, now professor of computer engineering at the University of Manchester.
Bee brains have evolved to be so energy efficient that they may be able to count using only four nerve cells, scientists have found. Simulations with a brain model used just four nerve cells and found this simplistic organ would be able to count up to, and beyond, five. The small number of nerve cells needed to count indicates that brain size is not as important as brain organisation, scientists claim. Bee brains have evolved to be so energy efficient that they can count using only four brain cells, scientists have found. Simulations showed the simple brain was capable of counting small quantities by closely studying one item at a time.
For all its faults, the human brain is pretty incredible. So incredible, in fact, that for more than 60 years, scientists, entrepreneurs, and sci-fi enthusiasts have done everything they can to replicate it in the form of artificial intelligence. While many people condemn such technology as the harbinger of the apocalypse, it has made countless tasks easier and even obsolete. But, will AI ever be able to replace the human brain? Some of the brightest minds in the world are working on the advancement of artificial intelligence.
MIT chemical engineers and neuroscientists have devised a new way to preserve biological tissue, allowing them to visualize proteins, DNA, and other molecules within cells, and to map the connections between neurons. The researchers showed that they could use this method, known as SHIELD, to trace the connections between neurons in a part of the brain that helps control movement and other neurons throughout the brain. "Using our technique, for the first time, we were able to map the connectivity of these neurons at single-cell resolution," says Kwanghun Chung, an assistant professor of chemical engineering and a member of MIT's Institute for Medical Engineering and Science and Picower Institute for Learning and Memory. "We can get all this multiscale, multidimensional information from the same tissue in a fully integrated manner because with SHIELD we can protect all this information." Chung is the senior author of the paper, which appears in the Dec. 17 issue of Nature Biotechnology.
Improved understanding of how the developing human nervous system differs from that of closely related nonhuman primates is fundamental for teasing out human-specific aspects of behavior, cognition, and disorders. The shared and unique functional properties of the human nervous system are rooted in the complex transcriptional programs governing the development of distinct cell types, neural circuits, and regions. However, the precise molecular mechanisms underlying shared and unique features of the developing human nervous system have been only minimally characterized. We generated complementary tissue-level and single-cell transcriptomic datasets from up to 16 brain regions covering prenatal and postnatal development in humans and rhesus macaques (Macaca mulatta), a closely related species and the most commonly studied nonhuman primate. We created and applied TranscriptomeAge and TempShift algorithms to age-match developing specimens between the species and to more rigorously ...
The brain is responsible for cognition, behavior, and much of what makes us uniquely human. The development of the brain is a highly complex process, and this process is reliant on precise regulation of molecular and cellular events grounded in the spatiotemporal regulation of the transcriptome. Disruption of this regulation can lead to neuropsychiatric disorders. The regulatory, epigenomic, and transcriptomic features of the human brain have not been comprehensively compiled across time, regions, or cell types. Understanding the etiology of neuropsychiatric disorders requires knowledge not just of endpoint differences between healthy and diseased brains but also of the developmental and cellular contexts in which these differences arise. Moreover, an emerging body of research indicates that many aspects of the development and physiology of the human brain are not well recapitulated in model organisms, and therefore it is necessary that neuropsychiatric disorders be understood in the broader context of the developing and adult human brain. Here we describe the generation and analysis of a variety of genomic data modalities at the tissue and single-cell levels, including transcriptome, DNA methylation, and histone modifications across multiple brain regions ranging in age from embryonic development through adulthood. We observed a widespread transcriptomic transition beginning during late fetal development and consisting of sharply decreased regional differences. This reduction coincided with increases in the transcriptional signatures of mature neurons and the expression of genes associated with dendrite development, synapse development, and neuronal activity, all of which were temporally synchronous across neocortical areas, as well as myelination and oligodendrocytes, which were asynchronous. Moreover, genes including MEF2C, SATB2, and TCF4, with genetic associations to multiple brain-related traits and disorders, converged in a small number of modules exhibiting spatial or spatiotemporal specificity. We generated and applied our dataset to document transcriptomic and epigenetic changes across human development and then related those changes to major neuropsychiatric disorders. These data allowed us to identify genes, cell types, gene coexpression modules, and spatiotemporal loci where disease risk might converge, demonstrating the utility of the dataset and providing new insights into human development and disease.