The following outline is provided as an overview of and topical guide to brain mapping: Brain mapping – set of neuroscience techniques predicated on the mapping of (biological) quantities or properties onto spatial representations of the (human or non-human) brain resulting in maps. Brain mapping is further defined as the study of the anatomy and function of the brain and spinal cord through the use of imaging (including intra-operative, microscopic, endoscopic and multi-modality imaging), immunohistochemistry, molecular and optogenetics, stem cell and cellular biology, engineering (material, electrical and biomedical), neurophysiology and nanotechnology. == Broad scope == History of neuroscience History of neurology Brain mapping Human brain Neuroscience Nervous system. === The neuron doctrine === Neuron doctrine – A set of carefully constructed elementary set of observations regarding neurons. For more granularity, more current, and more advanced topics, see the cellular level section Asserts that neurons fall under the broader cell theory, which postulates: All living organisms are composed of one or more cells. The cell is the basic unit of structure, function, and organization in all organisms. All cells come from preexisting, living cells. The Neuron doctrine postulates several elementary aspects of neurons: The brain is made up of individual cells (neurons) that contain specialized features such as dendrites, a cell body, and an axon. Neurons are cells differentiable from other tissues in the body. Neurons differ in size, shape, and structure according to their location or functional specialization. Every neuron has a nucleus, which is the trophic center of the cell (The part which must have access to nutrition). If the cell is divided, only the portion containing the nucleus will survive. Nerve fibers are the result of cell processes and the outgrowths of nerve cells. (Several axons are bound together to form one nerve fibril. See also: Neurofilament. Several nerve fibrils then form one large nerve fiber. Myelin, an electrical insulator, forms around selected axons. Neurons are generated by cell division. Neurons are connected by sites of contact and not via cytoplasmic continuity. (A cell membrane isolates the inside of the cell from its environment. Neurons do not communicate via direct cytoplasm to cytoplasm contact.) Law of dynamic polarization. Although the axon can conduct in both directions, in tissue there is a preferred direction of transmission from cell to cell. Elements added later to the initial Neuron doctrine A barrier to transmission exists at the site of contact between two neurons that may permit transmission. (Synapse) Unity of transmission. If a contact is made between two cells, then that contact can be either excitatory or inhibitory, but will always be of the same type. Dale's law, each nerve terminal releases a single type of neurotransmitter. Some of the basic postulates in the Neuron doctrine have been subsequently questioned, refuted, or updated. See the cellular level section topics for additional information. === Map, atlas, and database projects === Brain Activity Map Project – 2013 NIH $3 billion project to map every neuron in the human brain in ten years, based upon the Human Genome Project. NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative [1] Community outreach site for above where the public may comment [2] Human Brain Project (EU) – 1 billion euro, 10-year project to simulate the human brain with supercomputers. BigBrain A high-resolution 3D atlas of the human brain created as part of the HBP. Human Connectome Project – 2009 NIH $30 million project to build a network map of the human brain, including structural (anatomical) and functional elements. Emphasis included research into dyslexia, autism, Alzheimer's disease, and schizophrenia. See also Connectome a, comprehensive map of neural connections in the brain. Allen Brain Atlas – 2003 $100 million project funded by Paul Allen (Microsoft) BrainMaps – National Institute of Health (NIH) database including 60 terabytes of image scans of primate and non-primates, integrated with information covering structure and function. NeuroNames – Defines the brain in terms of about 550 primary structures (about 850 unique structures) to which all other structures, names, and synonyms are related. About 15,000 neuroanatomical terms are cross indexed, including many synonyms in seven languages. Coverage includes the brain and spinal cord of the four species most frequently studied by neuroscientists: human, macaque (monkey), rat and mouse. The controlled, standardized vocabulary for each structure is located in an unambiguous, strict physical hierarchy, and these terms are selected based on ease of pronunciation, mnemonic value, and frequency of use in recent neuroscientific publications. Relation of each structure to its superstructures and substructures is included. The controlled vocabulary is suitable for uniquely indexing neuroanatomical information in digital databases. Decade of the Brain 1990–1999 promotion by NIH and the Library of Congress "to enhance public awareness of the benefits to be derived from brain research". Communications targeted Members of Congress, staffs, and the general public to promote funding. Talairach Atlas see Jean Talairach Harvard Whole Brain Atlas see Human brain MNI Template see Medical image computing Blue Brain Project and Artificial brain International Consortium for Brain Mapping see Brain Mapping List of neuroscience databases NIH Toolbox National Institute of Health (USA) toolbox for the assessment of neurological and behavioral function Organization for Human Brain Mapping The Organization for Human Brain Mapping (OHBM) is an international society dedicated to using neuroimaging to discover the organization of the human brain. == Imaging and recording systems == This section covers imaging and recording systems. The general section covers history, neuroimaging, and techniques for mapping specific neural connections. The specific systems section covers the various specific technologies, including experimental and widely deployed imaging and recording systems. === General === Most imaging work to date on individual neurons has been conducted outside the brain, typically on large neurons, and has been most frequently destructive. New techniques are however rapidly emerging. Search on "Single neuron imaging" and see related topics: Biological neuron model, Single-unit recording, Neural oscillation, Computational neuroscience. dMRI (above) is also promising in non-destructive imaging of single neurons inside the brain. History of neuroimaging (redirects from Brain scanner) Neuroimaging (redirects from Brain function map) Connectomics – mapping technique showing neural connections in a nervous system. === Specific systems === Cortical stimulation mapping Diffusion MRI (dMRI) – includes diffusion tensor imaging (DTI) and diffusion functional MRI (DfMRI). dMRI is a recent breakthrough in brain mapping allowing the visualization of cross connections between different anatomical parts of the brain. It allows noninvasive imaging of white matter fiber structure and in addition to mapping can be useful in clinical observations of abnormalities, including damage from stroke. Electroencephalography (EEG) – uses electrodes on the scalp and other techniques to detect the electrical flow of currents. Electrocorticography – intracranial EEG, the practice of using electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. Electrophysiological techniques for clinical diagnosis Functional magnetic resonance imaging (fMRI) Medical image computing (brain research of leads medical and surgical uses of mapping technology) Neurostimulation (in research stimulation is frequently used in conjunction with imaging) Positron emission tomography (PET) – a nuclear medical imaging technique that produces a three-dimensional image or picture of functional processes in the body. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. In modern scanners, three dimensional imaging is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine. === Imaging and recording componentry === ==== Electrochemical ==== Haemodynamic response – the rapid delivery of blood to active neuronal tissues. Blood Oxygenation Level Dependent signal (BOLD), corresponds to the concentration of deoxyhemoglobin. The BOLD effect is based on the fact that when neuronal activity is increased in one part of the brain, there is also an increased amount of cerebral blood flow to that area. Functional m
Double descent
Double descent in statistics and machine learning is the phenomenon where a model's error rate on the test set initially decreases with the number of parameters, then peaks, then decreases again. This phenomenon has been considered surprising, as it contradicts assumptions about overfitting in classical machine learning. The increase usually occurs near the interpolation threshold, where the number of parameters is the same as the number of training data points (the model is just large enough to fit the training data). Or, more precisely, it is the maximum number of samples on which the model/training procedure achieves approximately on average 0 training error. == History == Early observations of what would later be called double descent in specific models date back to 1989. The term "double descent" was coined by Belkin et. al. in 2019, when the phenomenon gained popularity as a broader concept exhibited by many models. The latter development was prompted by a perceived contradiction between the conventional wisdom that too many parameters in the model result in a significant overfitting error (an extrapolation of the bias–variance tradeoff), and the empirical observations in the 2010s that some modern machine learning techniques tend to perform better with larger models. == Theoretical models == Double descent occurs in linear regression with isotropic Gaussian covariates and isotropic Gaussian noise. A model of double descent at the thermodynamic limit has been analyzed using the replica trick, and the result has been confirmed numerically. A number of works have suggested that double descent can be explained using the concept of effective dimension: While a network may have a large number of parameters, in practice only a subset of those parameters are relevant for generalization performance, as measured by the local Hessian curvature. This explanation is formalized through PAC-Bayes compression-based generalization bounds, which show that less complex models are expected to generalize better under a Solomonoff prior.
Dan Hendrycks
Dan Hendrycks (born 1994 or 1995) is an American machine learning researcher. He serves as the director of the Center for AI Safety, a nonprofit research organization based in San Francisco, California. == Early life and education == Hendrycks was raised in a Christian evangelical household in Marshfield, Missouri. He received a B.S. from the University of Chicago in 2018 and a Ph.D. from the University of California, Berkeley in Computer Science in 2022. == Career and research == Hendrycks' research focuses on topics that include machine learning safety, machine ethics, and robustness. He credits his participation in the effective altruism (EA) movement-linked 80,000 Hours program for his career focus towards AI safety, though denies being an advocate for EA. Hendrycks is the main author of the research paper that introduced the activation function GELU in 2016, and of the paper that introduced the language model benchmark MMLU (Massive Multitask Language Understanding) in 2020. In February 2022, Hendrycks co-authored recommendations for the US National Institute of Standards and Technology (NIST) to inform the management of risks from artificial intelligence. In September 2022, Hendrycks wrote a paper providing a framework for analyzing the impact of AI research on societal risks. He later published a paper in March 2023 examining how natural selection and competitive pressures could shape the goals of artificial agents. This was followed by "An Overview of Catastrophic AI Risks", which discusses four categories of risks: malicious use, AI race dynamics, organizational risks, and rogue AI agents. Hendrycks is the safety adviser of xAI, an AI startup company founded by Elon Musk in 2023. To avoid any potential conflicts of interest, he receives a symbolic one-dollar salary and holds no company equity. In November 2024, he also joined Scale AI as an advisor collecting a one-dollar salary. Hendrycks is the creator of Humanity's Last Exam, a benchmark for evaluating the capabilities of large language models, which he developed in collaboration with Scale AI. In 2024, Hendrycks published the textbook Introduction to AI Safety, Ethics, and Society, based on courseware he had previously developed. == Selected publications == Hendrycks, Dan; Gimpel, Kevin (2020-07-08). "Gaussian Error Linear Units (GELUs)". arXiv:1606.08415 [cs.LG]. Hendrycks, Dan; Gimpel, Kevin (2018-10-03). "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks". International Conference on Learning Representations 2017. arXiv:1610.02136. Hendrycks, Dan; Mazeika, Mantas; Dietterich, Thomas (2019-01-28). "Deep Anomaly Detection with Outlier Exposure". International Conference on Learning Representations 2019. arXiv:1812.04606. Hendrycks, Dan; Mazeika, Mantas; Zou, Andy (2021-10-25). "What Would Jiminy Cricket Do? Towards Agents That Behave Morally". Conference on Neural Information Processing Systems 2021. arXiv:2110.13136.
The Best Free AI Pair Programmer for Beginners
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Multiline optical-character reader
A multiline optical-character reader, or MLOCR, is a type of mail sorting machine that uses optical character recognition (OCR) technology to determine how to route mail through the postal system. MLOCRs work by capturing images of the front of letter-sized mailpieces, and extracting the entire address from each piece. It looks up the postal code within each address in a master database, prints a barcode representing this information on the mailpiece, and performs an initial sort. All of this occurs in a fraction of a second as the mailpiece passes through the machine. After this point, mail is further sorted by barcode sorters that read this barcode to determine its destination throughout its journey all the way down to the walk sequence of the mail carrier. The United States Postal Service has used remote bar coding since 1992. In the United States, if the MLOCR is not able to decode the address, then the mailpiece is placed on "hold" by printing a unique fluorescent barcode on the back of the mailpiece, and the mailpiece is then set aside for further processing by the Remote Bar Coding System (formerly called Remote Video Encoding). An image of the mailpiece is sent to a Remote Encoding Center where a human data conversion operator manually inspects the image. The operator converts the information on the mailpiece into abbreviated codes and enters the data into the computer. This data is sent back to the MLOCR site where it is matched with the unique barcode on the back of the un-coded mailpiece, and a barcode is then printed on the mailpiece like the rest of the mail. All this effort is invested up front into deciphering the destination of each mailpiece and printing the correct barcode, so that the mailpiece will never need to be manually examined again until it reaches the hands of the letter carrier who will carry it to the final delivery point. A Delivery Bar Code Sorter is repeatedly used at each point in the USPS system to read the barcode and sort the mailpiece to a tray corresponding to the next leg of its journey towards its final destination. The United States Postal Service is the largest user of these machines; however, large volume mailers and mail consolidators also have their own MLOCR systems to barcode outgoing mail in order to receive significant postage discounts. An option called FASTforward can be added to an MLOCR that allows it to automatically forward mail to a new address. This additional computer hardware/software combination looks up decoded addresses in the National Change of Address database to see if the recipient has recently moved. If so, a POSTNET barcode representing the new address is sprayed on the mailpiece thus routing it to new address although the old address is still visible—a testament to the degree at which mail can be mechanically sorted. Generally, all OCR-equipped letter sorting machines ordered since the late 1980s have been equipped with OCR systems capable of reading multiple lines of address.
BioBIKE
BioBike(nee. BioLingua ) is a cloud-based, through-the-web programmable (Paas) symbolic biocomputing and bioinformatics platform that aims to make computational biology, and especially intelligent biocomputing (that is, the application of Artificial Intelligence to computational biology) accessible to research scientists who are not expert programmers. == Unique capabilities == BioBIKE is an integrated symbolic biocomputing and bioinformatics platform, built from the start as an entirely (what is now called) cloud-based architecture where all computing is done in remote servers, and all user access is accomplished through web browsers. BioBIKE has a built-in frame system in which all objects, data, and knowledge are represented. This enables code written either in the native Lisp, in the visual programming language, or systems of rules expressed in the SNARK theorem prover to access the whole of biological knowledge in an integrated manner. For its time (released in 2002) it was unique in permitting users to create fully functional biocomputing programs that run on the back-end servers entirely through the web browser UI. (In modern terms it was one of the first PaaS (Platform as a Service) systems, predating even Salesforce in this capability.) Initially this programming was carried out in raw Lisp, but Jeff Elhai's team at VCU, with NSF funding, created an entirely graphical programming environment on top of BioBIKE based upon the Boxer-style programming environments. Being a multi-headed, multi-threaded, multi-user, multi-tenancy cloud-based system, BioBIKE users were able to directly work together through their web browsers, remotely sharing the same listener and memory space. This permitted a unique sort of collaboration, discussed in Shrager (2007). A specialized offshoot of BioBIKE called "BioDeducta" includes SRI's SNARK theorem prover, offering unique "deductive biocomputing" capabilities. == Implementation == BioBIKE is open-source software implemented using the Lisp programming language. Continuing development takes place by the BioBIKE team centered at Virginia Commonwealth University . == History == BioBIKE was originally called "BioLingua", and was developed by Jeff Shrager at The Carnegie Inst. of Washington Dept. of Plant Biology, and JP Massar with funding from NASA's Astrobiology Division. Shrager and Massar wanted to create a web-based, multi-user Lisp Machine, specialized for bioinformatics. Other early contributors to the project included Mike Travers, and Jeff Elhai of VCU. Elhai obtained continuing funding from the National Science Foundation for the project, which was renamed BioBIKE. Elhai and colleagues added BioBIKE's unique visual programming language. Shrager, meanwhile, collaborated with Richard Waldinger at SRI to build SRI's (SNARK) theorem prover into BioBIKE, creating a deductive biocomputing system, called BioDeducta. == Instances == There used to be a number of BioBIKE verticals in different biological domains, including viral pathogens, cyanobacteria and other bacteria, Arabidopsis thaliana, and several others described in the references.
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