Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Brian DePasquale"'
Autor:
Michele N. Insanally, Badr F. Albanna, Jade Toth, Brian DePasquale, Saba Shokat Fadaei, Trisha Gupta, Olivia Lombardi, Kishore Kuchibhotla, Kanaka Rajan, Robert C. Froemke
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-21 (2024)
Abstract Neuronal responses during behavior are diverse, ranging from highly reliable ‘classical’ responses to irregular ‘non-classically responsive’ firing. While a continuum of response properties is observed across neural systems, little i
Externí odkaz:
https://doaj.org/article/42bcfcbfbba74cab8b1cd12fb1c62752
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024)
Abstract Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, previous work has suggested that th
Externí odkaz:
https://doaj.org/article/7b66db28d3924570acc5fdd8d631f09f
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-11 (2019)
Neural representations in working memory are susceptible to internal noise, which scales with memory load. Here, the authors show that attractor dynamics mitigate the influence of internal noise by pulling memories towards a few stable representation
Externí odkaz:
https://doaj.org/article/7dc7a9bc12354148a2112c3668c27c12
Autor:
Michele N Insanally, Ioana Carcea, Rachel E Field, Chris C Rodgers, Brian DePasquale, Kanaka Rajan, Michael R DeWeese, Badr F Albanna, Robert C Froemke
Publikováno v:
eLife, Vol 8 (2019)
Neurons recorded in behaving animals often do not discernibly respond to sensory input and are not overtly task-modulated. These non-classically responsive neurons are difficult to interpret and are typically neglected from analysis, confounding atte
Externí odkaz:
https://doaj.org/article/4dde2c13912e49f18970454db5f36806
Publikováno v:
PLoS ONE, Vol 13, Iss 2, p e0191527 (2018)
Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input
Externí odkaz:
https://doaj.org/article/9ec1266678e84209bc8a0db668128814
Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, several hints in the literature suggest that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2fcd4f95ad54a936aa4bef49ff78eea8
https://doi.org/10.1101/2023.01.18.524599
https://doi.org/10.1101/2023.01.18.524599
Publikováno v:
Neuron. 111:631-649.e10
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8c4c81e57a9810e5bd4a4cb0d91f66f
https://doi.org/10.1016/j.neuron.2022.12.007
https://doi.org/10.1016/j.neuron.2022.12.007
Autor:
Michele N. Insanally, Badr F. Albanna, Jack Toth, Brian DePasquale, Saba Fadaei, Trisha Gupta, Kishore Kuchibhotla, Kanaka Rajan, Robert C. Froemke
Neuronal responses during behavior are diverse, ranging from highly reliable ‘classical’ responses to irregular or seemingly-random ‘non-classically responsive’ firing. While a continuum of response properties is frequently observed across ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcd6f3bf1939bc9e7b8ebd3c24c98ca2
https://doi.org/10.21203/rs.3.rs-1628084/v1
https://doi.org/10.21203/rs.3.rs-1628084/v1
Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here we develop a unified framework for modeling stimulus-driven behavior and mu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::06c718695302438915d5df791dd959cb
https://doi.org/10.1101/2021.10.28.465122
https://doi.org/10.1101/2021.10.28.465122
A key problem in systems neuroscience is to understand how neural populations integrate relevant sensory inputs during decision-making. Here, we address this problem by training a structured recurrent neural network to reproduce both psychophysical b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9f1e1df78f6e4aab50bb7e1e1e8b0d1
https://doi.org/10.1101/2020.11.27.401539
https://doi.org/10.1101/2020.11.27.401539