{"technology":{"slug":"neuroscience","name":"Neuroscience","description":"Brain science and neural research. From neural circuits and connectomics to brain-computer interfaces, neuroplasticity, and neurodegenerative disease research.","discipline":"Neuroscience / Medicine","icon":"🧠"},"lastUpdated":"2026-04-11T06:29:30.389Z","articleCount":15,"articles":[{"id":"oa-W2147502381","title":"The Synaptic Organization of the Brain","authors":"","journal":"Oxford University Press eBooks","pubDate":"2004-01-08","doi":"10.1093/acprof:oso/9780195159561.001.1","abstract":"Abstract Synapses are the contact sites that enable neurons to form connections between each other in order to transmit and process neural information. Synaptic organization is concerned with the principles by which neurons form circuits that mediate the specific functional operations of different brain regions. One of the aims of this book is to show that the study of synaptic organization—in its full multidisciplinary, multilevel, and theoretical dimension—is a powerful means of integrating brain information to give clear insights into the neural basis of behavior. This book, which has been revised in this the fifth edition, details local circuits in the different regions of the brain. The results of the mouse and human genome projects are incorporated. Also the book contains support from neuroscience databases. Among the new advances covered are 2-photon confocal laser microscopy of dendrites and dendritic spines, biochemical analyses, and dual patch and multielectrode recordings, applied together with an increasing range of behavioral and gene-targeting methods.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2147502381","citationCount":3577,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2144895779","title":"Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework","authors":"Stanislas Dehaene","journal":"Cognition","pubDate":"2001-04-01","doi":"10.1016/s0010-0277(00)00123-2","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2144895779","citationCount":2434,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2586907386","title":"Neuroscience Needs Behavior: Correcting a Reductionist Bias","authors":"John W. Krakauer, Asif A. Ghazanfar, Àlex Gómez-Marín, Malcolm A. MacIver, David Poeppel","journal":"Neuron","pubDate":"2017-02-01","doi":"10.1016/j.neuron.2016.12.041","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2586907386","citationCount":1448,"isOpenAccess":true,"pdfUrl":"http://www.cell.com/article/S0896627316310406/pdf"},{"id":"oa-W4214775604","title":"The NEURON Book","authors":"Nicholas T. Carnevale, Michael L. Hines","journal":"Cambridge University Press eBooks","pubDate":"2006-01-12","doi":"10.1017/cbo9780511541612","abstract":"The authoritative reference on NEURON, the simulation environment for modeling biological neurons and neural networks that enjoys wide use in the experimental and computational neuroscience communities. This book shows how to use NEURON to construct and apply empirically based models. Written primarily for neuroscience investigators, teachers, and students, it assumes no previous knowledge of computer programming or numerical methods. Readers with a background in the physical sciences or mathematics, who have some knowledge about brain cells and circuits and are interested in computational modeling, will also find it helpful. The NEURON Book covers material that ranges from the inner workings of this program, to practical considerations involved in specifying the anatomical and biophysical properties that are to be represented in models. It uses a problem-solving approach, with many working examples that readers can try for themselves.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W4214775604","citationCount":1167,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2949808130","title":"A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster","authors":"Zhihao Zheng, J. Scott Lauritzen, Eric Perlman, Camenzind G. Robinson, Matthew Nichols, Daniel E. Milkie, Omar Torrens, J. W. Price, Corey B. Fisher, Nadiya Sharifi, Steven A. Calle-Schuler, Lucia Kmecová, Iqbal J. Ali, Bill Karsh, Eric T. Trautman, John Bogovic, Philipp Hanslovsky, Gregory S.X.E. Jefferis, Michael Kazhdan, Khaled Khairy, Stephan Saalfeld, Richard D. Fetter, Davi D. Bock","journal":"Cell","pubDate":"2018-07-01","doi":"10.1016/j.cell.2018.06.019","abstract":"Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience. VIDEO ABSTRACT.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2949808130","citationCount":1138,"isOpenAccess":true,"pdfUrl":"http://www.cell.com/article/S0092867418307876/pdf"},{"id":"oa-W2113773025","title":"Radical embodiment: neural dynamics and consciousness","authors":"Evan Thompson, Francisco J. Varela","journal":"Trends in Cognitive Sciences","pubDate":"2001-10-01","doi":"10.1016/s1364-6613(00)01750-2","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2113773025","citationCount":1137,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2966081953","title":"Towards artificial general intelligence with hybrid Tianjic chip architecture","authors":"Jing Pei, Lei Deng, Sen Song, Mingguo Zhao, Youhui Zhang, Shuang Wu, Guanrui Wang, Zhe Zou, Zhenzhi Wu, Wei He, Feng Chen, Ning Deng, Si Wu, Yu Wang, Yujie Wu, Zheyu Yang, Cheng Ma, Guoqi Li, Wentao Han, Huanglong Li, Huaqiang Wu, Rong Zhao, Yuan Xie, Luping Shi","journal":"Nature","pubDate":"2019-07-31","doi":"10.1038/s41586-019-1424-8","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2966081953","citationCount":1015,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2139570025","title":"Emotion and the motivational brain","authors":"Peter J. Lang, Margaret M. Bradley","journal":"Biological Psychology","pubDate":"2009-10-31","doi":"10.1016/j.biopsycho.2009.10.007","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2139570025","citationCount":989,"isOpenAccess":true,"pdfUrl":"https://www.ncbi.nlm.nih.gov/pmc/articles/3612949"},{"id":"oa-W2036299899","title":"The cognitive neuroscience of creativity","authors":"Arne Dietrich","journal":"Psychonomic Bulletin & Review","pubDate":"2004-12-01","doi":"10.3758/bf03196731","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2036299899","citationCount":825,"isOpenAccess":true,"pdfUrl":"https://link.springer.com/content/pdf/10.3758/BF03196731.pdf"},{"id":"oa-W2601369343","title":"Where Does EEG Come From and What Does It Mean?","authors":"Michael X Cohen","journal":"Trends in Neurosciences","pubDate":"2017-03-15","doi":"10.1016/j.tins.2017.02.004","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2601369343","citationCount":698,"isOpenAccess":true,"pdfUrl":"https://zenodo.org/record/3452760"},{"id":"oa-W2115859705","title":"Optogenetic interrogation of neural circuits: technology for probing mammalian brain structures","authors":"Feng Zhang, Viviana Gradinaru, Antoine Adamantidis, Remy Durand, Raag D. Airan, Luı́s de Lecea, Karl Deisseroth","journal":"Nature Protocols","pubDate":"2010-02-18","doi":"10.1038/nprot.2009.226","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2115859705","citationCount":694,"isOpenAccess":true,"pdfUrl":"https://www.ncbi.nlm.nih.gov/pmc/articles/4503465"},{"id":"oa-W2951065015","title":"Toward an Integration of Deep Learning and Neuroscience","authors":"Adam Marblestone, Greg Wayne, Konrad P. Körding","journal":"arXiv (Cornell University)","pubDate":"2016-06-01","doi":"10.3389/fncom.2016.00094","abstract":"Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2951065015","citationCount":687,"isOpenAccess":true,"pdfUrl":"https://arxiv.org/pdf/1606.03813"},{"id":"oa-W1975398991","title":"A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses","authors":"Ning Qiao, Hesham Mostafa, Federico Corradi, Marc Osswald, Fabio Stefanini, Dora Sumislawska, Giacomo Indiveri","journal":"Frontiers in Neuroscience","pubDate":"2015-04-29","doi":"10.3389/fnins.2015.00141","abstract":"Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm(2), and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W1975398991","citationCount":684,"isOpenAccess":true,"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fnins.2015.00141/pdf"},{"id":"oa-W2513986326","title":"Genetically encoded indicators of neuronal activity","authors":"Michael Z. Lin, Mark J. Schnitzer","journal":"Nature Neuroscience","pubDate":"2016-08-26","doi":"10.1038/nn.4359","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2513986326","citationCount":661,"isOpenAccess":true,"pdfUrl":"https://www.ncbi.nlm.nih.gov/pmc/articles/5557009"},{"id":"oa-W2036962817","title":"Targeted optogenetic stimulation and recording of neurons in vivo using cell-type-specific expression of Channelrhodopsin-2","authors":"Jessica A. Cardin, Marie Carlén, Konstantinos Meletis, Ulf Knoblich, Feng Zhang, Karl Deisseroth, Li‐Huei Tsai, Christopher I. Moore","journal":"Nature Protocols","pubDate":"2010-01-21","doi":"10.1038/nprot.2009.228","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2036962817","citationCount":559,"isOpenAccess":true,"pdfUrl":"https://www.nature.com/articles/nprot.2009.228.pdf"}],"links":{"web":"https://science-database.com/technology/neuroscience","llms_txt":"https://science-database.com/technology/neuroscience/llms.txt","api":"https://science-database.com/api/v1/technology/neuroscience"}}