Zobrazeno 1 - 10
of 280
pro vyhledávání: '"Peter E. Latham"'
Autor:
Anne K. Churchland, Eftychios A. Pnevmatikakis, John P. Cunningham, Farzaneh Najafi, Peter E. Latham, Gamaleldin F. Elsayed, Robin Cao
This package contains data, in NWB (Neurodata Without Borders) format, from the 4 mice included in "Farzaneh Najafi, Gamaleldin F Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P Cunningham, Anne K Churchland (bioRxiv, 2018); Exci
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5cb5d6af959d73cb229ecdedd7b5ac42
https://doi.org/10.14224/1.37693
https://doi.org/10.14224/1.37693
Autor:
Naoki Hiratani, Peter E. Latham
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-15 (2020)
How can rodents make sense of the olfactory environment without supervision? Here, the authors formulate olfactory learning as an integrated Bayesian inference problem, then derive a set of synaptic plasticity rules and neural dynamics that enables n
Externí odkaz:
https://doaj.org/article/f6df5da2e76146c792e6313cc3b56b26
Publikováno v:
PLoS Computational Biology, Vol 18, Iss 1, p e1009808 (2022)
Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in studies of sensory processing is to understand how the brain extracts those variables. Here we revisit a common enco
Externí odkaz:
https://doaj.org/article/673a2675b01944a0bbd8e32c6822f353
Autor:
C. Randy Gallistel, Peter E. Latham
Publikováno v:
Timing & Time Perception. 11:29-89
Bayesian parameter estimation and Shannon’s theory of information provide tools for analysing and understanding data from behavioural and neurobiological experiments on interval timing—and from experiments on Pavlovian and operant conditioning, b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::307fad66ca4229b2b5af70aead4b7a59
https://doi.org/10.1163/22134468-bja10069
https://doi.org/10.1163/22134468-bja10069
Autor:
Christopher B Currin, Phumlani N Khoza, Alexander D Antrobus, Peter E Latham, Tim P Vogels, Joseph V Raimondo
Publikováno v:
PLoS Computational Biology, Vol 15, Iss 7, p e1007049 (2019)
Externí odkaz:
https://doaj.org/article/9d5da0b32bd74b11beb155d5904765f3
Publikováno v:
PLoS Computational Biology, Vol 13, Iss 4, p e1005497 (2017)
Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these populati
Externí odkaz:
https://doaj.org/article/cbce68af3d6f448b8adb250f410f3ec2
Publikováno v:
PLoS Computational Biology, Vol 12, Iss 12, p e1005110 (2016)
Zipf's law, which states that the probability of an observation is inversely proportional to its rank, has been observed in many domains. While there are models that explain Zipf's law in each of them, those explanations are typically domain specific
Externí odkaz:
https://doaj.org/article/4c2d12ce18ee4da49aabd0cbe57df691
Autor:
Laurence Aitchison, Peter E. Latham, Jean-Pascal Pfister, Jannes Jegminat, Jorge Aurelio Menendez, Alexandre Pouget
Publikováno v:
Nature Neuroscience. 24:565-571
Learning, especially rapid learning, is critical for survival. However, learning is hard; a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab36b3917da9693a0567b2ad504c99bb
https://doi.org/10.1038/s41593-021-00809-5
https://doi.org/10.1038/s41593-021-00809-5
Publikováno v:
Entropy, Vol 15, Iss 8, Pp 3109-3129 (2013)
Maximum entropy models have become popular statistical models in neuroscience and other areas in biology and can be useful tools for obtaining estimates of mutual information in biological systems. However, maximum entropy models fit to small data se
Externí odkaz:
https://doaj.org/article/6edb23c86838413d97d5db1ef1ef10db
Autor:
Peter E. Latham, Naoki Hiratani
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-15 (2020)
Nature Communications
Nature Communications
Many experimental studies suggest that animals can rapidly learn to identify odors and predict the rewards associated with them. However, the underlying plasticity mechanism remains elusive. In particular, it is not clear how olfactory circuits achie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b8b09b07784f609b7ab43b4a228dd2b
http://link.springer.com/article/10.1038/s41467-020-17490-0
http://link.springer.com/article/10.1038/s41467-020-17490-0