HFirst: A Temporal Approach to Object Recognition DOI
Garrick Orchard,

Cédric Meyer,

Ralph Etienne‐Cummings

и другие.

IEEE Transactions on Pattern Analysis and Machine Intelligence, Год журнала: 2015, Номер 37(10), С. 2028 - 2040

Опубликована: Янв. 15, 2015

This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in output of biologically inspired asynchronous Address Event Representation (AER) vision sensors. The nature these systems frees computation and communication from rigid predetermined enforced by system clocks conventional systems. Freedom constraints opens possibility using true to our advantage computation. We show not only how can be used recognition, but also it fact simplify Specifically, we rely on simple temporal-winner-take-all rather than more computationally intensive synchronous operations typically neural networks recognition. approach visual represents major paradigm shift clocked find application other sensory modalities computational tasks. showcase effectiveness achieving highest reported accuracy date (97.5\%$\pm$3.5\%) previously published four class card pip task an 84.9\%$\pm$1.9\% new difficult 36 character task.

Язык: Английский

Fully integrated silicon probes for high-density recording of neural activity DOI

James J. Jun,

Nicholas A. Steinmetz, Joshua H. Siegle

и другие.

Nature, Год журнала: 2017, Номер 551(7679), С. 232 - 236

Опубликована: Ноя. 7, 2017

Язык: Английский

Процитировано

1993

Integrated information theory: from consciousness to its physical substrate DOI
Giulio Tononi, Mélanie Boly, Marcello Massimini

и другие.

Nature reviews. Neuroscience, Год журнала: 2016, Номер 17(7), С. 450 - 461

Опубликована: Май 26, 2016

Язык: Английский

Процитировано

1403

A Toolbox for Representational Similarity Analysis DOI Creative Commons
Hamed Nili, Cai Wingfield, Alexander Walther

и другие.

PLoS Computational Biology, Год журнала: 2014, Номер 10(4), С. e1003553 - e1003553

Опубликована: Апрель 17, 2014

Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns test computational models of brain information processing. One approach this problem representational similarity analysis (RSA), which characterizes a representation in or model by the distance matrix response elicited set stimuli. The encapsulates what distinctions between stimuli emphasized and de-emphasized representation. tested comparing it predicts that region. RSA also enables us compare representations stages processing within given model, behavioral data, individuals species. Here, we introduce Matlab toolbox for RSA. supports an simultaneously data- hypothesis-driven. It designed help integrate wide range into multichannel measurements as provided modern functional imaging neuronal recording techniques. Tools visualization inference enable user relate sets regions statistically using nonparametric methods. searchlight-based RSA, continuously map volume search code specific geometry. Finally, linear-discriminant t value measure discriminability bridges gap linear decoding analyses In order demonstrate capabilities toolbox, apply both simulated real fMRI data. functions equally applicable other modalities measurement. freely available community under open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).

Язык: Английский

Процитировано

929

Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy DOI
Robert Prevedel, Young‐Gyu Yoon, Maximilian Hoffmann

и другие.

Nature Methods, Год журнала: 2014, Номер 11(7), С. 727 - 730

Опубликована: Май 18, 2014

Язык: Английский

Процитировано

761

Graph analysis of the human connectome: Promise, progress, and pitfalls DOI
Alex Fornito, Andrew Zalesky, Michael Breakspear

и другие.

NeuroImage, Год журнала: 2013, Номер 80, С. 426 - 444

Опубликована: Апрель 30, 2013

Язык: Английский

Процитировано

758

From the neuron doctrine to neural networks DOI
Rafael Yuste

Nature reviews. Neuroscience, Год журнала: 2015, Номер 16(8), С. 487 - 497

Опубликована: Июль 8, 2015

Язык: Английский

Процитировано

712

Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective DOI
Xi‐Nian Zuo, Xiu-Xia Xing

Neuroscience & Biobehavioral Reviews, Год журнала: 2014, Номер 45, С. 100 - 118

Опубликована: Май 27, 2014

Язык: Английский

Процитировано

649

Decoding Wakefulness Levels from Typical fMRI Resting-State Data Reveals Reliable Drifts between Wakefulness and Sleep DOI Creative Commons
Enzo Tagliazucchi, Helmut Laufs

Neuron, Год журнала: 2014, Номер 82(3), С. 695 - 708

Опубликована: Май 1, 2014

Язык: Английский

Процитировано

639

The Metastable Brain DOI Creative Commons
Emmanuelle Tognoli, J. A. Scott Kelso

Neuron, Год журнала: 2014, Номер 81(1), С. 35 - 48

Опубликована: Янв. 1, 2014

Язык: Английский

Процитировано

622

Nano-Bioelectronics DOI
Anqi Zhang, Charles M. Lieber

Chemical Reviews, Год журнала: 2015, Номер 116(1), С. 215 - 257

Опубликована: Дек. 21, 2015

Nano-bioelectronics represents a rapidly expanding interdisciplinary field that combines nanomaterials with biology and electronics and, in so doing, offers the potential to overcome existing challenges bioelectronics. In particular, shrinking electronic transducer dimensions nanoscale making their properties appear more biological can yield significant improvements sensitivity biocompatibility thereby open up opportunities fundamental healthcare. This review emphasizes recent advances nano-bioelectronics enabled semiconductor nanostructures, including silicon nanowires, carbon nanotubes, graphene. First, synthesis electrical of these are discussed context Second, affinity-based nano-bioelectronic sensors for highly sensitive analysis biomolecules reviewed. studies, nanostructures as transistor-based biosensors from device behavior through sensing applications future challenges. Third, complex interface between nanoelectronics living systems, single cells live animals, is discussion focuses on representative electrophysiology using nanoelectronic devices cellular measurements emerging work where arrays incorporated within three-dimensional cell networks define synthetic natural tissues. Last, some exciting discussed.

Язык: Английский

Процитировано

583