Daniel Tranchina

According to our database1, Daniel Tranchina authored at least 14 papers between 2000 and 2019.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.



In proceedings 
PhD thesis 


On csauthors.net:


A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory.
PLoS Comput. Biol., 2019

An alternating renewal process describes the buildup of perceptual segregation.
Frontiers Comput. Neurosci., 2014

Recovery of Sparse Translation-Invariant Signals With Continuous Basis Pursuit.
IEEE Trans. Signal Process., 2011

A blind sparse deconvolution method for neural spike identification.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Sparse decomposition of transformation-invariant signals with continuous basis pursuit.
Proceedings of the IEEE International Conference on Acoustics, 2011

A Systems Approach Uncovers Restrictions for Signal Interactions Regulating Genome-wide Responses to Nutritional Cues in Arabidopsis.
PLoS Comput. Biol., 2009

Spike Train Statistics and Dynamics with Synaptic Input from any Renewal Process: A Population Density Approach.
Neural Comput., 2009

A system biology approach highlights a hormonal enhancer effect on regulation of genes in a nitrate responsive "biomodule".
BMC Syst. Biol., 2009

<i>In Silico </i>Evaluation of Predicted Regulatory Interactions in <i>Arabidopsis thaliana</i>.
BMC Bioinform., 2009

Critical Analysis of Dimension Reduction by a Moment Closure Method in a Population Density Approach to Neural Network Modeling.
Neural Comput., 2007

A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Extension to Slow Inhibitory Synapses.
Neural Comput., 2001

A population density method for large-scale modeling of neuronal networks with realistic synaptic kinetics.
Neurocomputing, 2001

A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Analysis and an Application to Orientation Tuning.
J. Comput. Neurosci., 2000

Fast neural network simulations with population density methods.
Neurocomputing, 2000