Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Kristofor D. Carlson"'
Publikováno v:
PLoS Computational Biology, Vol 15, Iss 6, p e1006908 (2019)
Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionalit
Externí odkaz:
https://doaj.org/article/e1b08e98ae374b3dab1cd9ff3372cc28
Publikováno v:
2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d90c11680d9824c91c2e93b9fb12d1e
https://doi.org/10.1109/dicta56598.2022.10034619
https://doi.org/10.1109/dicta56598.2022.10034619
Autor:
Matthew J. Marinella, James B. Aimone, John H. Naegle, Fredrick Rothganger, Samuel A. Mulder, Nadine E. Miner, Craig M. Vineyard, Kristofor D. Carlson, Steven J. Plimpton, Conrad D. James, Aleksandra Faust, Timothy J. Draelos
Publikováno v:
Biologically Inspired Cognitive Architectures. 19:49-64
Biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here, we survey the history of neural-inspired and neuromorphic computing in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd3005d630b8d5f3aea5049c0b1e0ada
https://doi.org/10.1016/j.bica.2016.11.002
https://doi.org/10.1016/j.bica.2016.11.002
Supported by recent computational studies, sparse coding and dimensionality reduction are emerging as a ubiquitous coding strategy across brain regions and modalities, allowing neurons to achieve nonnegative sparse coding (NSC) by efficiently encodin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6c78deb579365a1ee99098fdee09ee4
Autor:
James B. Aimone, Pamela L. Follett, Conrad D. James, Jonathon W. Donaldson, Craig M. Vineyard, Aaron J. Hill, John H. Naegle, Michael R. Smith, David R. Follett, Kristofor D. Carlson
Publikováno v:
IJCNN
Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f4cc2a29bdac79d69b1188cad69d08e
https://doi.org/10.1109/ijcnn.2017.7966150
https://doi.org/10.1109/ijcnn.2017.7966150
Autor:
Craig M. Vineyard, James B. Aimone, Nadine E. Miner, Jonathan A. Cox, Timothy J. Draelos, Kristofor D. Carlson, Conrad D. James, William Severa, Christopher C. Lamb
Publikováno v:
IJCNN
Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing —
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07c7f688564b1e9182b4a57856bf91a7
https://doi.org/10.1109/ijcnn.2017.7965898
https://doi.org/10.1109/ijcnn.2017.7965898
The continuous integration of young neurons into the adult brain represents a novel form of structural plasticity and has inspired the creation of numerous computational models to understand the functional role of adult neurogenesis. These computatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ef23aa0e05c3547c0ebb8e69bc484f21
https://doi.org/10.1016/b978-0-12-803784-3.00020-2
https://doi.org/10.1016/b978-0-12-803784-3.00020-2
Autor:
James B. Aimone, Alison L. Althaus, Rosanna C. Barnard, Suzanna Becker, Luc Berthouze, Panagiotis Bozelos, Craig E. Brown, Markus Butz-Ostendorf, Kristofor D. Carlson, Hermann Cuntz, Will DeBello, Gustavo Deco, Moritz Deger, Thomas Deller, Simon F. Farmer, Michael Fauth, Henrique M. Fernandes, Rory Finnegan, Tomoki Fukai, Shaoyu Ge, Kimberly Gerrow, Keren Grafen, Naoki Hiratani, Felix Z. Hoffmann, Anthony Holtmaat, Frances Hutchings, Peter Jedlicka, Marcus Kaiser, Vassilis Kehayas, Gregory W. Kirschen, Istvan Z. Kiss, Florence Kleberg, Andreas Knoblauch, Morten L. Kringelbach, Sol Lim, Paul Miller, Daniel Miner, Geoffrey G. Murphy, Mikaël Naveau, Steffen Platschek, Mark Plitt, Panayiota Poirazi, Ivan Raikov, Sebastian Rinke, James Roach, Fred Rothganger, Kurt A. Sailor, Leonard Sander, Vittorio Sanguineti, Mark Shaw, Quinton Skilling, Ivan Soltesz, Angus B.A. Stevner, Christian Tetzlaff, Gertraud Teuchert-Noodt, Jochen Triesch, Tim J. van Hartevelt, Arjen van Ooyen, Felix Wolf, Florentin Wörgötter, Karen Zito, Michal Zochowski
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::571692cf3c7fb0005226e2b43af3e7c5
https://doi.org/10.1016/b978-0-12-803784-3.00034-2
https://doi.org/10.1016/b978-0-12-803784-3.00034-2
Publikováno v:
ICRC
For decades, neural networks have shown promise for next-generation computing, and recent breakthroughs in machine learning techniques, such as deep neural networks, have provided state-of-the-art solutions for inference problems. However, these netw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6f331d16801d03fd110a31b615fdb2b
https://doi.org/10.1109/icrc.2016.7738681
https://doi.org/10.1109/icrc.2016.7738681
Autor:
Kristofor D. Carlson, N. Giordano
Publikováno v:
Journal of Computational Neuroscience. 30:747-758
Synaptic strength can be modified by the relative timing of pre- and postsynaptic activity, a phenomenon termed spike timing-dependent plasticity (STDP). Studies of neurons in the hippocampus and in other regions have found that when presynaptic acti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc2d0ef3741f70735432898697ecae9f
https://doi.org/10.1007/s10827-010-0290-z
https://doi.org/10.1007/s10827-010-0290-z