Inés Samengo

发表

Bursting neurons in the hippocampal formation convey information about LFP features

Maria Constantinou, Daniel H Elijah, Daniel Squirrell, 2014, BMC Neuroscience.

Type I and Type II Neuron Models Are Selectively Driven by Differential Stimulus Features

Inés Samengo, Germán Mato, G. Mato, 2008, Neural Computation.

Bursts Generate a Non-Reducible Spike-Pattern Code

Inés Samengo, Ariel Rokem, Andreas V. M. Herz, 2009, Front. Neurosci..

Statistical Description of Associative Memory

Inés Samengo, 2003 .

The firing statistics of Poisson neuron models driven by slow stimuli

Inés Samengo, Eugenio Urdapilleta, I. Samengo, 2009, Biological Cybernetics.

Measuring Information Spatial Densities

Michele Bezzi, Inés Samengo, Stefan Leutgeb, 2002, Neural Computation.

Linking dynamical and functional properties of intrinsically bursting neurons

Marcelo A. Montemurro, Inés Samengo, Germán Mato, 2013, Journal of Computational Neuroscience.

Information-Theoretical Analysis of the Neural Code in the Rodent Temporal Lobe

Inés Samengo, Emilio Kropff, Melisa B. Maidana Capitán, 2018, Entropy.

A Subjective Distance Between Stimuli: Quantifying the Metric Structure of Representations

Inés Samengo, Stefan Leutgeb, Sheri J. Mizumori, 2004, Neural Computation.

Time and Category Information in Pattern-Based Codes

Inés Samengo, Hugo Gabriel Eyherabide, I. Samengo, 2010, Front. Comput. Neurosci..

Assessing the Relevance of Specific Response Features in the Neural Code

Inés Samengo, Hugo Gabriel Eyherabide, I. Samengo, 2018, Entropy.

The information transmitted by spike patterns in single neurons

Inés Samengo, Hugo G. Eyherabide, I. Samengo, 2009, Journal of Physiology-Paris.

Information-theoretical analysis of the statistical dependencies among three variables: Applications to written language

Inés Samengo, Damián H. Zanette, Damián G. Hernández, 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

Bursting Neurons in the Hippocampal Formation Encode Features of LFP Rhythms

Marcelo A. Montemurro, Maria Constantinou, Inés Samengo, 2016, Front. Comput. Neurosci..

Thalamic neuron models encode stimulus information by burst-size modulation

Marcelo A. Montemurro, Inés Samengo, Daniel H. Elijah, 2015, Front. Comput. Neurosci..

Time- and frequency-resolved covariance analysis for detection and characterization of seizures from intracraneal EEG recordings

Inés Samengo, Silvia Kochen, Melisa Maidana Capitán, 2020, Biological Cybernetics.

The subjective metric of remembered colors: A Fisher-information analysis of the geometry of human chromatic memory

Inés Samengo, Rodrigo Echeveste, María da Fonseca, 2019, PloS one.

Spike-timing precision underlies the coding efficiency of auditory receptor neurons.

Inés Samengo, Ariel Rokem, Martin Stemmler, 2006, Journal of neurophysiology.

Dynamics and Reliability of Bistable Neurons Driven with Time-Dependent Stimuli

Inés Samengo, Susanne Schreiber, Soledad Gonzalo Cogno, 2014, Neural Computation.

Efficiency of Auditory Receptor Neurons Spike-Timing Precision Underlies the Coding

Jan Clemens, Ariel Rokem, Bernhard Ronacher, 2015 .

When and Why Noise Correlations Are Important in Neural Decoding

Inés Samengo, Hugo Gabriel Eyherabide, I. Samengo, 2013, The Journal of Neuroscience.

Burst Firing is a Neural Code in an Insect Auditory System

Ariel Rokem, Inés Samengo, Andreas V. M. Herz, 2008, Frontiers Comput. Neurosci..

Estimating the Mutual Information between Two Discrete, Asymmetric Variables with Limited Samples

Inés Samengo, Damián G. Hernández, I. Samengo, 2019, Entropy.

Derivation of Human Chromatic Discrimination Ability from an Information-Theoretical Notion of Distance in Color Space

Inés Samengo, María da Fonseca, I. Samengo, 2016, Neural Computation.

Spike-Train Analysis

Marcelo A. Montemurro, Inés Samengo, Daniel H. Elijah, 2013 .

Novel Perceptually Uniform Chromatic Space

Inés Samengo, María da Fonseca, I. Samengo, 2018, Neural Computation.

Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli

Inés Samengo, Tim Gollisch, 2012, Journal of Computational Neuroscience.

Information Loss in an Optimal Maximum Likelihood Decoding

Inés Samengo, I. Samengo, 2001, Neural Computation.

Effects of spike-triggered negative feedback on receptive-field properties

Inés Samengo, Eugenio Urdapilleta, I. Samengo, 2015, Journal of Computational Neuroscience.

Inferring a Property of a Large System from a Small Number of Samples

Inés Samengo, Damián G Hernández, 2022, Entropy.

Statistical Properties of Color Matching Functions

Inés Samengo, María da Fonseca, I. Samengo, 2020, Neural Computation.

Two distinct mechanisms shape the reliability of neural responses.

Inés Samengo, Susanne Schreiber, Andreas V M Herz, 2009, Journal of neurophysiology.

Estimating probabilities from experimental frequencies.

Inés Samengo, 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

Quasistatic approximation of the interspike interval distribution of neurons driven by time-dependent inputs.

Inés Samengo, Eugenio Urdapilleta, I. Samengo, 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

Conversion of Phase Information into a Spike-Count Code by Bursting Neurons

Marcelo A. Montemurro, Inés Samengo, M. Montemurro, 2010, PloS one.

Independent neurons representing a finite set of stimuli: dependence of the mutual information on the number of units sampled.

Inés Samengo, 2001 .