Analysis and prediction of leucine-rich nuclear export signals.

By T. la Cour, Lars Kiemer, Anne Mølgaard, Ramneek Gupta, K. Skriver, Søren Brunak. Protein engineering, design & selection : PEDS, 2004

1,512 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1f84b3a01a5ad5c7d3c838fb040d4634e79bf32d

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Citations in the corpus

Causal Inference in Multisensory Perception (2007, background): “Unexpectedly, this only requires simple linear operations on neural activity [35].”
The benefits of noise in neural systems: bridging theory and experiment (2011, background): “By contrast, stochastic facilitation is not best associated with the algorithmic level of description, except when the randomness inherent in noise potentially has a necessary role, which could occur when the nature of a computation involves assessing probabilitie”
Correlations and Neuronal Population Information (2016, background): “Rather, it arises because correlations have a strong impact on the information encoded by a neuronal population, particularly on the portion of information that can be extracted with simple and biologically realizable algorithms.”
A Neural Implementation of the Kalman Filter (2009, background): “Probabilistic population coding [16, 4] is more closely related to model presented here.”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, methodology): “[7] presented another approach to Bayesian inference using probabilistic population codes, also without any learning result.”
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology (2009, background): “One particularly important idea is that neuronal variability can provide a ‘‘probabilistic population code’’ [9], which allows the brain to represent probability distributions, and perform Bayesian inference [10].”
The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics (2014, background): “More recent approaches do allow for the representation of uncertainty, including distributional [20], doubly distributed [21], and probabilistic population codes [22, 23, 24], or sampling-based network dynamics [25, 26, 11].”
Noise in the nervous system (2008, methodology): “and this concept has been incorporated into Bayesian models of neuronal population code”