Reference health
The free-energy principle: a unified brain theory?
113/113 checkable references clean · checked 2026-10-18
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.
17 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 113 checked references that resolve
resolves10.1145/168304.168306
Keeping the neural networks simple by minimizing the description length of the weights
resolves10.1007/978-94-011-5014-9_12
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
resolves10.1016/j.tins.2004.10.007
The Bayesian brain: the role of uncertainty in neural coding and computation
resolves10.1088/0954-898X_4_4_001
A forward-inverse optics model of reciprocal connections between visual cortical areas
resolves10.1080/01621459.1989.10478825
Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models)
resolves10.1152/jn.1996.76.4.2790
Probability density estimation for the interpretation of neural population codes
resolves10.1088/1741-2560/2/3/S06
Evolution of the cerebellum as a neuronal machine for Bayesian state estimation
resolves10.1038/4580
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
resolves10.1073/pnas.95.12.7121
On the actions that one nerve cell can have on another: Distinguishing “drivers” from “modulators”
resolves10.1016/S0079-6123(06)54005-1
Contribution of feedforward, lateral and feedback connections to the classical receptive field center and extra-classical receptive field surround of primate V1 neurons
resolves10.1016/S0079-6123(06)65006-1
Towards a unified theory of neocortex: laminar cortical circuits for vision and cognition
resolves10.1016/j.brainres.2008.04.024
Spikes, synchrony, and attentive learning by laminar thalamocortical circuits
resolves10.1146/annurev.ne.13.030190.001353
Perceptual Neural Organization: Some Approaches Based on Network Models and Information Theory
resolves10.1162/neco.1995.7.6.1129
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
resolves10.1152/jn.1987.57.1.162
Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. III. Information theoretic analysis
resolves10.1038/381607a0
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
resolves10.1098/rstb.2000.0562
The labile brain. III. Transients and spatio–temporal receptive fields
resolves10.1126/science.1166673
Widespread Changes in Synaptic Markers as a Function of Sleep and Wakefulness in
<i>Drosophila</i>
resolves10.1146/annurev.ne.18.030195.003011
Visual Feature Integration and the Temporal Correlation Hypothesis
resolves10.1523/JNEUROSCI.02-01-00032.1982
Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex
resolves10.1016/j.neuroscience.2005.06.094
Beyond bistability: Biophysics and temporal dynamics of working memory
resolves10.1016/j.jphysparis.2007.01.004
Autoassociative memory retrieval and spontaneous activity bumps in small-world networks of integrate-and-fire neurons
resolves10.1162/089976699300016287
The Relationship Between Synchronization Among Neuronal Populations and Their Mean Activity Levels
resolves10.1523/JNEUROSCI.4499-07.2008
The Effects of Visual Stimulation and Selective Visual Attention on Rhythmic Neuronal Synchronization in Macaque Area V4
resolves10.1016/j.jphysparis.2007.01.005
Neuronal coherence during selective attentional processing and sensory–motor integration
resolves10.1016/j.visres.2008.03.009
Predictive coding as a model of biased competition in visual attention
resolves10.1016/j.neunet.2004.06.006
Bayesian representation learning in the cortex regulated by acetylcholine
resolves10.1016/0896-6273(93)90304-A
Neural Darwinism: Selection and reentrant signaling in higher brain function
resolves10.1016/0306-4522(94)90592-4
Value-dependent selection in the brain: Simulation in a synthetic neural model
resolves10.1037/0033-295X.88.2.135
Toward a modern theory of adaptive networks: Expectation and prediction.
resolves10.1007/s00213-006-0578-x
The debate over dopamine’s role in reward: the case for incentive salience
resolves10.1152/jn.1998.80.2.696
Smoothness Maximization Along a Predefined Path Accurately Predicts the Speed Profiles of Complex Arm Movements
resolves10.1152/jn.00266.2007
Sensory Prediction Errors Drive Cerebellum-Dependent Adaptation of Reaching
resolves10.1113/jphysiol.2006.120121
Computational principles of sensorimotor control that minimize uncertainty and variability
resolves10.1038/nature02024
Environmentally mediated synergy between perception and behaviour in mobile robots
resolves10.1098/rstb.2007.2098
Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration
resolves10.1016/S0893-6080(02)00056-4
Control of exploitation–exploration meta-parameter in reinforcement learning
resolves10.1126/science.283.5401.549
The Role of Locus Coeruleus in the Regulation of Cognitive Performance
resolves10.1016/S0065-3233(01)55003-2
Rational evolutionary design: The theory of in vitro protein evolution
resolves10.1007/BF02691333
Characterization of state transitions in spatially distributed, chaotic, nonlinear, dynamical systems in cerebral cortex
resolves10.1017/S0140525X01000097
Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems
resolves10.1016/j.neuroscience.2008.03.050
Self-organization and neuronal avalanches in networks of dissociated cortical neurons
resolves10.1016/j.newideapsych.2006.09.002
Intentionality in non-equilibrium systems? The functional aspects of self-organized pattern formation
resolves10.1126/science.1077349
Discrete Coding of Reward Probability and Uncertainty by Dopamine Neurons
resolves10.1038/nrn2536
Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia
resolves10.1017/S0140525X03000025
Convergence of biological and psychological perspectives on cognitive coordination in schizophrenia
The 17 references without a DOI — listed, not checked
no DOI — not checkedNicolis, G. & Prigogine, I. Self-Organisation in Non-Equilibrium Systems (Wiley, New York, 1977).
no DOI — not checkedBernard, C. Lectures on the Phenomena Common to Animals and Plants (Thomas, Springfield, 1974).
no DOI — not checkedFeynman, R. P. Statistical Mechanics: a Set of Lectures (Benjamin, Reading, Massachusetts, 1972).
no DOI — not checkedvon Helmholtz, H. in Treatise on Physiological Optics Vol. III 3rd edn (Voss, Hamburg, 1909).
no DOI — not checkedMacKay, D. M. in Automata Studies (eds Shannon, C. E. & McCarthy, J.) 235–251 (Princeton Univ. Press, Princeton, 1956).
no DOI — not checkedNeisser, U. Cognitive Psychology (Appleton-Century-Crofts, New York, 1967).
no DOI — not checkedGregory, R. L. Perceptual illusions and brain models. Proc. R. Soc. Lond. B Biol. Sci. 171, 179–196 (1968).
no DOI — not checkedBeal, M. J. Variational Algorithms for Approximate Bayesian Inference. Thesis, University College London (2003).
no DOI — not checkedEfron, B. & Morris, C. Stein's estimation rule and its competitors – an empirical Bayes approach. J. Am. Stats. Assoc. 68, 117–130 (1973).
no DOI — not checkedBarlow, H. in Sensory Communication (ed. Rosenblith, W.) 217–234 (MIT Press, Cambridge, Massachusetts, 1961).
no DOI — not checkedTipping, M. E. Sparse Bayesian learning and the Relevance Vector Machine. J. Machine Learn. Res. 1, 211–244 (2001).
no DOI — not checkedHebb, D. O. The Organization of Behaviour (Wiley, New York, 1949).
no DOI — not checkedvon der Malsburg, C. The Correlation Theory of Brain Function. Internal Report 81–82, Dept. Neurobiology, Max-Planck-Institute for Biophysical Chemistry (1981).
no DOI — not checkedRescorla, R. A. & Wagner, A. R. in Classical Conditioning II: Current Research and Theory (eds Black, A. H. & Prokasy, W. F.) 64–99 (Appleton Century Crofts, New York, 1972).
no DOI — not checkedWatkins, C. J. C. H. & Dayan, P. Q-learning. Mach. Learn. 8, 279–292 (1992).
no DOI — not checkedTodorov, E. in Advances in Neural Information Processing Systems (eds Scholkopf, B., Platt, J. & Hofmann T.) 19, 1369–1376 (MIT Press, 2006).
no DOI — not checkedMaturana, H. R. & Varela, F. De máquinas y seres vivos (Editorial Universitaria, Santiago, 1972). English translation available in Maturana, H. R. & Varela, F. in Autopoiesis and Cognition (Reidel, Dordrecht, 1980).
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. If Crossref’s record marks the work itself
retracted, withdrawn, or removed, the badge says so instead of showing clean. It says nothing
about the quality, findings, or importance of the work itself, and nothing about references
deposited without a DOI.
checked 2026-10-18 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.1038/nrn2787"><img src="https://citestamp.com/citestamped/10.1038/nrn2787/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.1038/nrn2787)