C
Christian K. Machens
Researcher at Champalimaud Foundation
Publications - 78
Citations - 5156
Christian K. Machens is an academic researcher from Champalimaud Foundation. The author has contributed to research in topics: Population & Artificial neural network. The author has an hindex of 27, co-authored 72 publications receiving 4211 citations. Previous affiliations of Christian K. Machens include Cold Spring Harbor Laboratory & École Normale Supérieure.
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Journal ArticleDOI
Flexible Control of Mutual Inhibition: A Neural Model of Two-Interval Discrimination
Christian K. Machens,Christian K. Machens,Ranulfo Romo,Ranulfo Romo,Carlos D. Brody,Carlos D. Brody +5 more
TL;DR: A simple mutual-inhibition network model is presented that captures all three task phases within a single framework and integrates both working memory and decision making because its dynamical properties are easily controlled without changing its connectivity.
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Modeling single-neuron dynamics and computations: a balance of detail and abstraction.
TL;DR: How single-cell models on five levels of complexity, from black-box approaches to detailed compartmental simulations, address key questions about neural dynamics and signal processing are examined.
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Demixed principal component analysis of neural population data
Dmitry Kobak,Wieland Brendel,Wieland Brendel,Christos Constantinidis,Claudia E. Feierstein,Adam Kepecs,Zachary F. Mainen,Xue-Lian Qi,Ranulfo Romo,Naoshige Uchida,Christian K. Machens +10 more
TL;DR: A new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components and exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards is demonstrated.
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Efficient codes and balanced networks
TL;DR: A possible revision of the dominant view that neurons represent information with firing rates corrupted by Poisson noise is suggested, suggesting that tight excitatory/inhibitory balance may be a signature of a highly cooperative code, orders of magnitude more precise than a Poisson rate code.
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Linearity of Cortical Receptive Fields Measured with Natural Sounds
TL;DR: In vivo whole-cell methods in the rat auditory cortex are used to record subthreshold membrane potential fluctuations elicited by complex acoustic stimuli, including animal vocalizations, and find that the STRF has a rich dynamical structure, including excitatory regions positioned in general accord with the prediction of the classical tuning curve.