D
Dmitry R. Lyamzin
Researcher at Ludwig Maximilian University of Munich
Publications - 23
Citations - 350
Dmitry R. Lyamzin is an academic researcher from Ludwig Maximilian University of Munich. The author has contributed to research in topics: Sensory system & Visual cortex. The author has an hindex of 6, co-authored 23 publications receiving 264 citations. Previous affiliations of Dmitry R. Lyamzin include Moscow Institute of Physics and Technology & Russian Academy of Sciences.
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Journal ArticleDOI
State-Dependent Population Coding in Primary Auditory Cortex
TL;DR: The findings demonstrate that the representational capacity of A1 depends strongly on cortical state, and suggest that cortical state should be considered as an explicit variable in all studies of sensory processing.
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The mouse posterior parietal cortex: Anatomy and functions.
Dmitry R. Lyamzin,Andrea Benucci +1 more
TL;DR: This area of the mouse PPC should rightfully be considered a convenient model system for a circuit-level understanding of the mammalian parietal cortex.
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The Neural Representation of Interaural Time Differences in Gerbils Is Transformed from Midbrain to Cortex
TL;DR: The results suggest that the neural representation of ITDs in gerbils is transformed from IC to A1 and have important implications for how spatial location may be combined with other acoustic features for the analysis of complex auditory scenes.
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Nonlinear transfer of signal and noise correlations in cortical networks.
Dmitry R. Lyamzin,Samuel J. Barnes,Roberta Donato,Jose A. Garcia-Lazaro,Tara Keck,Nicholas A. Lesica +5 more
TL;DR: It is shown that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs.
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Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlations
Dmitry R. Lyamzin,Dmitry R. Lyamzin,Jakob H. Macke,Jakob H. Macke,Nicholas A. Lesica,Nicholas A. Lesica +5 more
TL;DR: A model for the type of data commonly recorded in early sensory pathways: responses to repeated trials of a sensory stimulus in which each neuron has it own time-varying spike rate and the dependencies between cells are characterized by both signal and noise correlations.