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Enregistrement W1870812038

Neural networking: Yale and Hebb at the 37th annual meeting of the Society for Neuroscience.

2008· other· en· W1870812038 sur OpenAlexaboutno aff
Kathleen A. Dave

Notice bibliographique

RevuePubMed · 2008
Typeother
Langueen
DomaineNeuroscience
ThématiqueNeurology and Historical Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNeuroscienceAttendanceDiversity (politics)PsychologyPhysiologyMedicineCognitive scienceSociology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

From November 3 to November 7, 2007, more than 31,000 neuroscientists convened in San Diego, California, for the 37th annual meeting of the Society for Neuroscience. Formed in 1969, the society has not celebrated its 40th anniversary, yet membership has ballooned from the 500 inaugural members to more than 38,000. The scientists and physicians who attended this meeting brought a diversity of ideas and techniques to the society’s overarching goal of understanding the brain — from the cells that comprise it to the behaviors it directs. The conference was organized into eight themes: Development; Neural Excitability, Synapses, and Cellular Mechanisms; Disorders of the Nervous System; Sensory and Motor Systems; Homeostatic and Neuroendocrine Systems; Cognition and Behavior; Techniques in Neuroscience; and History and Teaching of Neuroscience. Yale maintained its presence at the 2007 meeting with students, postdoctoral researchers, and faculty members giving 218 poster presentations, eight slide presentations, and four symposiums, spanning the breadth of all the above themes. The society’s overall range of membership was represented well by the diversity of Yale physicians and scientists in attendance. Our researchers came from 25 departments — Anesthesiology, Biomedical Engineering, Cell Biology, Comparative Medicine, Diagnostic Radiology, Endocrinology, Genetics, Immunobiology, Internal Medicine, Laboratory Medicine, Molecular Biophysics and Biochemistry (MB&B), Molecular, Cellular and Developmental Biology (MCDB), Neurobiology, Neurology, Neurosurgery, Obstetrics and Gynecology (OB/GYN), Ophthalmology, Pediatrics, Physiology, Pharmacology, Psychiatry, Psychology, Public Health, and Surgery — as well as The Child Study Center, The Center for Neuroscience and Neuroregenerative Research (CNNR), and the Kavli Institute for Neuroscience. Neuroscience is not simply an inclusive field, but an integrative one. Much of the research presented by Yale scientists was the result of fruitful, interdepartmental collaboration. The synthesis of datasets from the wide range of fields mentioned above, and the emergent synergies, are essential for meaningful advancements in a field with such broad goals as neuroscience. Such an interdisciplinary gathering did not exist when Canadian psychologist Donald Hebb began working on his seminal book Organization of Behavior. In this issue’s review, Bilal Haider chronicles the work of Yale physiologists and psychologists who informed and contributed to the “dual-trace mechanism” postulated by Hebb in his 1949 book. As the author states, the book was no Zeus’ Athena and “did not burst forth fully formed.” Rather, it synthesized a remarkable body of research drawn from the fields of anatomy, physiology, and psychology, much of which was done at Yale. These lines of “neural network” research, undertaken at Yale in the 1930s and crystallized by Hebb in the late 1940s, are far from passe. All four of the Society for Neuroscience Special Presidential Lecturers discussed how emerging technologies will increase scientists’ abilities to compare existing neural data sets and perform rigorous, direct experiments testing neural network theories. “We are rapidly approaching this horizon as neuroscientists make use of an increasingly powerful arsenal for obtaining data — from the level of molecules to nervous systems — and engage in the arduous and challenging process of adapting and assembling neuroscience data at all scales of resolution and across disciplines into computerized databases,” Dr. Mark Ellisman, UCSD, said of the University of California, San Diego. As director of the National Institutes of Health-sponsored Biomedical Informatics Research Network, Ellisman intimately is involved in this pursuit. Indeed, the four scientists selected for this year’s Presidential Lecture series represent the kind of cooperation and cross-talk that precedes great breakthroughs. Dr. Karl Svoboda, HHMI, demonstrated his laboratory’s capabilities to record images, at the sub-cellular level in mice, of synapses and their calcium dynamics from time spans of milliseconds to months. Studies of this type still involve invasive methods, but new technologies are making more detailed studies of the living human brain possible, as well. Pioneering applications of diffusion MRI, Dr. Heidi Johanssen-Berg of the University of Oxford, showed data of the anatomical connections between human brain regions, previously unparalleled in detail. “Defining these pathways is crucial to our understanding of how the brain works, as the communications network dictates how we receive and evaluate information about the world and how we produce co-coordinated responses,” Johansen-Berg says. Lastly, Dr. Sebastian Seung, MIT, proposed new neural network theories that may augment or partially refute those set forth by Hebb. Yet the aim of today’s neuroscientists remains largely the same as Hebb’s and was eloquently stated by Dr. Ellisman: “A grand goal in neuroscience research is to understand how the interplay of structural, chemical, and electrical signals in nervous tissue gives rise to behavior.” Haider’s following review reveals the intellectual climate at Yale that heavily informed one of the most tremendously influential neuroscientific theories of the 20th century.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,129
Score d'incertitude au seuil0,657

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,046
Tête enseignante GPT0,226
Écart entre enseignants0,180 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2008
Routes d'admission1
Résumé présentoui

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