Reference health
Statistical models for neural encoding, decoding, and optimal stimulus design
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24/24 checkable references clean · checked 2026-07-24
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
8 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 24 checked references that resolve
resolves10.1523/JNEUROSCI.18-18-07411.1998
A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells
resolves10.7551/mitpress/7503.003.0112
Real-time adaptive information-theoretic optimization of neurophysiology experiments
resolves10.1121/1.404284
A statistical study of cochlear nerve discharge patterns in response to complex speech stimuli
resolves10.1016/0378-5955(94)90222-4
In search of the best stimulus: An optimization procedure for finding efficient stimuli in the cat auditory cortex
resolves10.1088/0954-898X_14_3_304
Convergence properties of three spike-triggered analysis techniques
resolves10.1088/0954-898X_15_4_002
Maximum likelihood estimation of cascade point-process neural encoding models
resolves10.1523/JNEUROSCI.0919-04.2004
Superlinear Population Encoding of Dynamic Hand Trajectory in Primary Motor Cortex
resolves10.1162/0899766042321797
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
resolves10.1523/JNEUROSCI.3305-05.2005
Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model
resolves10.1523/JNEUROSCI.23-11-04746.2003
The Receptive-Field Organization of Simple Cells in Primary Visual Cortex of Ferrets under Natural Scene Stimulation
resolves10.1152/jn.00697.2004
A Point Process Framework for Relating Neural Spiking Activity to Spiking History, Neural Ensemble, and Extrinsic Covariate Effects
resolves10.1146/annurev.neuro.29.051605.113024
COMPLETE FUNCTIONAL CHARACTERIZATION OF SENSORY NEURONS BY SYSTEM IDENTIFICATION
The 8 references without a DOI — listed, not checked
no DOI — not checkedAhrens, M., Paninski, L., Petersen, R. and Sahani, M. (2006) Input nonlinearity models of barrel cortex responses. Computational Neuroscience Meeting, Edinburgh.
no DOI — not checked10.1016/S0079-6123(06)65031-0_bib7
no DOI — not checkedKulkarni, J. and Paninski, L. (2006) Common-input models for multiple neural spike-train data. COSYNE’06.
no DOI — not checkedPillow, J. and Paninski, L. (2007) Model-based decoding, information estimation, and change-point detection in multi-neuron spike trains. Under review, Neural Computation.
no DOI — not checkedPillow, J., Paninski, L., Shlens, J., Simoncelli, E. and Chichilnisky, E. (2005a) Modeling multi-neuronal responses in primate retinal ganglion cells. Comp. Sys. Neur. ’05.
no DOI — not checked10.1016/S0079-6123(06)65031-0_bib24
no DOI — not checked10.1016/S0079-6123(06)65031-0_bib25
no DOI — not checkedEvidence optimization techniques for estimating stimulus-response functions
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work were clean at the dated check: each resolved to a known work in a public registry, and none
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