The DOI matches its Crossref record; 83 of the 84 references it cites were found in a registry or an index.
A Dynamical Systems Hypothesis of Schizophrenia
Open accessAbstract
We propose a top-down approach to the symptoms of schizophrenia based on a statistical dynamical framework. We show that a reduced depth in the basins of attraction of cortical attractor states destabilizes the activity at the network level due to the constant statistical fluctuations caused by the stochastic spiking of neurons. In integrate-and-fire network simulations, a decrease in the NMDA receptor conductances, which reduces the depth of the attractor basins, decreases the stability of short-term memory states and increases distractibility. The cognitive symptoms of schizophrenia such as distractibility, working memory deficits, or poor attention could be caused by this instability of attractor states in prefrontal cortical networks. Lower firing rates are also produced, and in the orbitofrontal and anterior cingulate cortex could account for the negative symptoms, including a reduction of emotions. Decreasing the GABA as well as the NMDA conductances produces not only switches between the attractor states, but also jumps from spontaneous activity into one of the attractors. We relate this to the positive symptoms of schizophrenia, including delusions, paranoia, and hallucinations, which may arise because the basins of attraction are shallow and there is instability in temporal lobe semantic memory networks, leading thoughts to move too freely round the attractor energy landscape.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In addition, the discrete-pool model used in Loh et al. (2007) cannot capture features that emerge naturally from a continuous model described here: Broadening of WM representations, smooth random drift of memory, and sensitivity to target-distractor similarity.”
From Linking microcircuit dysfunction to cognitive impairment: Effects of disinhibition associated with schizophrenia in a cortical working memory model (2014) · cites this paper as Loh et al. 2007
In the citing paper
“Increased miss rate would be expected if the persistent activity pattern was completely lost in the target cells (Loh et al. 2007), or a bump spontaneously appeared in nontarget cells, interfering with target encoding (Cano-Colino and Compte 2012).”
From Linking microcircuit dysfunction to cognitive impairment: Effects of disinhibition associated with schizophrenia in a cortical working memory model (2014) · cites this paper as Loh et al. 2007
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Introduction to the theory of neural computation
Hertz, J., Krogh, A., & Palmer, R. G. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90014-0 · Cited by 6,410
What are the functional consequences of neurocognitive deficits in schizophrenia?
(1996) · American Journal of Psychiatry · doi:10.1176/ajp.153.3.321 · Cited by 3,516
Cortical inhibitory neurons and schizophrenia
Lewis, D. A., Hashimoto, T., & Volk, D. W. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1648 · Cited by 2,297
Amit, D. J. (1989) · Cambridge University Press eBooks · doi:10.1017/cbo9780511623257 · Cited by 2,261
The principal features and mechanisms of dopamine modulation in the prefrontal cortex
Seamans, J. K., & Yang, C. R. (2004) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2004.05.006 · Cited by 1,592
Context, cortex, and dopamine: A connectionist approach to behavior and biology in schizophrenia.
Cohen, J. D., & Servan‐Schreiber, D. (1992) · Psychological Review · doi:10.1037/0033-295x.99.1.45 · Cited by 1,513
The Symptoms of Chronic Schizophrenia
Liddle, P. F. (1987) · The British Journal of Psychiatry · doi:10.1192/bjp.151.2.145 · Cited by 1,461
D1 Dopamine Receptors in Prefrontal Cortex: Involvement in Working Memory
Sawaguchi, T., & Goldman-Rakic, P. S. (1991) · Science · doi:10.1126/science.1825731 · Cited by 1,364
Probabilistic decision making by slow reverberation in cortical circuits
Wang, X.-J. (2002) · Neuron · doi:10.1016/s0896-6273(02)01092-9 · Cited by 1,242
Flagged first, then by citations
1 of the 84 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- RollsET 2005 Emotion explained Oxford Oxford University Press 606
- AbelesM 1991 Corticonics New York Cambridge University Press 280
- BraitenbergVSchützA 1991 Anatomy of the cortex Berlin Springer Verlag 249
Cited by
Sorted by citations
Dynamic models of large-scale brain activity
Breakspear, M. (2017) · Nature Neuroscience · doi:10.1038/nn.4497 · Cited by 1,239
The orbitofrontal cortex and beyond: From affect to decision-making
Rolls, E. T., & Fabian Grabenhorst, B. (2008) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2008.09.001 · Cited by 851
Self-citationValue, pleasure and choice in the ventral prefrontal cortex
Grabenhorst, F., & Rolls, E. T. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.12.004 · Cited by 766
Self-citationDurstewitz, D., & Seamans, J. K. (2008) · Biological Psychiatry · doi:10.1016/j.biopsych.2008.05.015 · Cited by 586
Criticality in the brain: A synthesis of neurobiology, models and cognition
Cocchi, L., Gollo, L. L., Zalesky, A., & Breakspear, M. J. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2017.07.002 · Cited by 555
Mental training enhances attentional stability: Neural and behavioral evidence
Lutz, A., Slagter, H. A., Rawlings, N. B., Francis, A. D., Greischar, L. L., & Davidson, R. J. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.1614-09.2009 · Cited by 489
Characterizing Thalamo-Cortical Disturbances in Schizophrenia and Bipolar Illness
Anticevic, A., Cole, M. W., Repovš, G., Murray, J. D., Brumbaugh, M. S., Winkler, A. M., Savić, A., Krystal, J. H., Pearlson, G. D., & Glahn, D. C. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht165 · Cited by 509
An anatomical substrate for integration among functional networks in human cortex
van den Heuvel, M. P., & Sporns, O. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.2128-13.2013 · Cited by 404
Toward a neurobiology of delusions
Corlett, P. R., Taylor, J. R., Wang, X.-J., Fletcher, P. C., & Krystal, J. H. (2010) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2010.06.007 · Cited by 394
Computational models of schizophrenia and dopamine modulation in the prefrontal cortex
Rolls, E. T., Loh, M., Deco, G., & Winterer, G. (2008) · Nature reviews. Neuroscience · doi:10.1038/nrn2462 · Cited by 396
Self-citationStochastic dynamics as a principle of brain function
Deco, G., Rolls, E. T., & Romo, R. (2009) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2009.01.006 · Cited by 392
Self-citationAnticevic, A., Haut, K. M., Murray, J. D., Repovš, G., Yang, G., Diehl, C., McEwen, S. C., Bearden, C. E., Addington, J., Goodyear, B. G., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.0566 · Cited by 387
Moving From Static to Dynamic Models of the Onset of Mental Disorder
Nelson, B., McGorry, P. D., Wichers, M., Wigman, J. T. W., & Hartmann, J. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0001 · Cited by 378
Knowing how much you don't know: a neural organization of uncertainty estimates
Bach, D. R., & Dolan, R. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3289 · Cited by 339
Erbb4 Deletion from Fast-Spiking Interneurons Causes Schizophrenia-like Phenotypes
del Pino, I., García-Frigola, C., Dehorter, N., Brotons-Mas, J. R., Alvarez-Salvado, E., Martínez de Lagrán, M., Ciceri, G., Gabaldón, M. V., Moratal, D., Dierssen, M., et al. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.010 · Cited by 314
The orbitofrontal cortex and emotion in health and disease, including depression
Rolls, E. T. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.09.021 · Cited by 343
Self-citationThe neuronal encoding of information in the brain
Rolls, E. T., & Treves, A. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.08.002 · Cited by 300
Self-citationMurray, J. D., Anticevic, A., Gancsos, M., Ichinose, M., Corlett, P. R., Krystal, J. H., & Wang, X.-J. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs370 · Cited by 255
Neuromodulatory Systems and Their Interactions: A Review of Models, Theories, and Experiments
Avery, M. C., & Krichmar, J. L. (2017) · Frontiers in Neural Circuits · doi:10.3389/fncir.2017.00108 · Cited by 250
Armbruster, D. J. N., Ueltzhöffer, K., Basten, U., & Fiebach, C. J. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00286 · Cited by 202
Brain mechanisms for perceptual and reward-related decision-making
Deco, G., Rolls, E. T., Albantakis, L., & Romo, R. (2013) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.01.010 · Cited by 196
Self-citationA non-reward attractor theory of depression
Rolls, E. T. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.05.007 · Cited by 180
Self-citationThe storage and recall of memories in the hippocampo-cortical system
Rolls, E. T. (2017) · Cell and Tissue Research · doi:10.1007/s00441-017-2744-3 · Cited by 195
Self-citationA quantitative theory of the functions of the hippocampal CA3 network in memory
Rolls, E. T. (2013) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2013.00098 · Cited by 124
Self-citationAre computational models of any use to psychiatry?
Huys, Q. J. M., Moutoussis, M., & Williams, J. H. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.03.001 · Cited by 106
Coupling of respiration and attention via the locus coeruleus: effects of meditation and pranayama.
Melnychuk, M. C., Dockree, P. M., O'Connell, R. G., Murphy, P. R., Balsters, J. H., & Robertson, I. H. (2018) · Psychophysiology · doi:10.1111/psyp.13091 · Cited by 101
An attractor hypothesis of obsessive–compulsive disorder
Rolls, E. T., Loh, M., & Deco, G. (2008) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2008.06379.x · Cited by 91
Self-citationAn optimal strategy for epilepsy surgery: Disruption of the rich-club?
Lopes, M. A., Richardson, M. P., Abela, E., Rummel, C., Schindler, K. A., Goodfellow, M., & Terry, J. R. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005637 · Cited by 88
Altered Global Signal Topography in Schizophrenia
Yang, G., Murray, J. D., Glasser, M. F., Pearlson, G. D., Krystal, J. H., Schleifer, C., Repovš, G., & Anticevic, A. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw297 · Cited by 88
Exploration of miRNA families for hypotheses generation
Kamanu, T., Radovanović, A., Archer, J. A. C., & Bajić, V. B. (2013) · Scientific Reports · doi:10.1038/srep02940 · Cited by 83
Rolls, E. T. (2009) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1 · Cited by 83
Self-citationModel‐based detection and analysis of introgressed Neanderthal ancestry in modern humans
Steinrücken, M., Spence, J. P., Kamm, J. A., Wieczorek, E., & Song, Y. S. (2018) · Molecular Ecology · doi:10.1111/mec.14565 · Cited by 73
Anticevic, A., Cole, M. W., Repovš, G., Savić, A., Driesen, N. R., Yang, G., Cho, Y., Murray, J. D., Glahn, D. C., Wang, X., et al. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00169 · Cited by 80
Comprehensive review: Computational modelling of schizophrenia
Valton, V., Romaniuk, L., Steele, J. D., Lawrie, S. M., & Seriès, P. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.08.022 · Cited by 82
Phillips, W. A., & Silverstein, S. M. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00307 · Cited by 75
Effort awareness and sense of volition in schizophrenia
Lafargue, G., & Franck, N. (2008) · Consciousness and Cognition · doi:10.1016/j.concog.2008.05.004 · Cited by 77
Genetic and cognitive windows into circuit mechanisms of psychiatric disease
Arguello, P. A., & Gogos, J. A. (2011) · Trends in Neurosciences · doi:10.1016/j.tins.2011.11.007 · Cited by 70
Age‐related changes in the ease of dynamical transitions in human brain activity
Ezaki, T., Sakaki, M., Watanabe, T., & Masuda, N. (2018) · Human Brain Mapping · doi:10.1002/hbm.24033 · Cited by 55
Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry
Murray, J. D., Demirtaş, M., & Anticevic, A. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.07.004 · Cited by 67
Brain dynamics: the temporal variability of connectivity, and differences in schizophrenia and ADHD
Rolls, E. T., Cheng, W., & Feng, J. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01197-x · Cited by 65
Self-citationA computational neuroscience approach to schizophrenia and its onset
Rolls, E. T., & Deco, G. (2010) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2010.09.001 · Cited by 67
Self-citationIt’s about time: Linking dynamical systems with human neuroimaging to understand the brain
John, Y. J., Sawyer, K. S., Srinivasan, K., Müller, E. J., Munn, B., & Shine, J. M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00230 · Cited by 71
Multi-level and hybrid modelling approaches for systems biology
Bardini, R., Politano, G. M. M., Benso, A., & Carlo, S. D. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2017.07.005 · Cited by 63
Cognition-Emotion Dysinteraction in Schizophrenia
Anticevic, A., & Corlett, P. R. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00392 · Cited by 66
Serotonin Regulates Performance Nonmonotonically in a Spatial Working Memory Network
Cano-Colino, M., Almeida, R., Gómez-Cabrero, D., Artigas, F., & Compte, A. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht096 · Cited by 58
Ramírez-Mahaluf, J. P., Roxin, A., Mayberg, H. S., & Compte, A. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv249 · Cited by 60
Attractor cortical neurodynamics, schizophrenia, and depression
Rolls, E. T. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01333-7 · Cited by 63
Self-citationThe influence of emotion clarity on emotional prosody identification in paranoid schizophrenia
Bach, D. R., Buxtorf, K., Grandjean, D., & Strik, W. K. (2008) · Psychological Medicine · doi:10.1017/s0033291708004704 · Cited by 50
Bridging Levels of Understanding in Schizophrenia Through Computational Modeling
Anticevic, A., Murray, J. D., & Barch, D. M. (2015) · Clinical Psychological Science · doi:10.1177/2167702614562041 · Cited by 51
The Brain Connectivity Workshops: Moving the frontiers of computational systems neuroscience
Stephan, K. E., Riera, J. J., Deco, G., & Horwitz, B. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.167 · Cited by 51
Self-citationAsl, M. M., Vahabie, A., & Valizadeh, A. (2018) · Basic and Clinical Neuroscience Journal · doi:10.32598/bcn.9.10.125 · Cited by 52
Beyond the disconnectivity hypothesis of schizophrenia
Rolls, E. T., Cheng, W., Gilson, M., Gong, W., Deco, G., Lo, C. Z., Yang, A. C., Tsai, S., Liu, M., Lin, C., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz161 · Cited by 48
Self-citationModulating the granularity of category formation by global cortical states
Kim, Y. (2008) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.001.2008 · Cited by 47
Manifold learning for parameter reduction
Holiday, A., Kooshkbaghi, M., Bello-Rivas, J., Gear, C., Zagaris, A., & Kevrekidis, I. (2018) · Journal of Computational Physics · doi:10.1016/j.jcp.2019.04.015 · Cited by 50
Dopaminergic Control of Long-Term Depression/Long-Term Potentiation Threshold in Prefrontal Cortex
Sheynikhovich, D., Otani, S., & Arleo, A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0466-13.2013 · Cited by 45
Hippocampal storage and recall of neocortical “What”–“Where” representations
Rolls, E. T., Zhang, C., & Feng, J. (2024) · Hippocampus · doi:10.1002/hipo.23636 · Cited by 43
Self-citationClinical Applications of Stochastic Dynamic Models of the Brain, Part I: A Primer
Roberts, J. A., Friston, K. J., & Breakspear, M. J. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.01.010 · Cited by 41
Rolls, E. T. (2012) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2011.06.017 · Cited by 39
Self-citationMoustafa, A. A., & Gluck, M. A. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.02.006 · Cited by 41
Schleifer, C. H., Lin, A., Kushan, L., Ji, J. L., Yang, G., Bearden, C. E., & Anticevic, A. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.3470-17.2018 · Cited by 41
Schizophrenia, dissociation, and consciousness
Bob, P., & Mashour, G. A. (2011) · Consciousness and Cognition · doi:10.1016/j.concog.2011.04.013 · Cited by 43
The Role of Serotonin in Orbitofrontal Function and Obsessive-Compulsive Disorder
Maia, T. V., & Cano-Colino, M. (2015) · Clinical Psychological Science · doi:10.1177/2167702614566809 · Cited by 42
Improper activation of D1 and D2 receptors leads to excess noise in prefrontal cortex
Avery, M. C., & Krichmar, J. L. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00031 · Cited by 37
Oullier, O., Kirman, A. P., & Kelso, J. A. S. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.2009960 · Cited by 34
Rösler, L., Rolfs, M., van der Stigchel, S., Neggers, S. F. W., Cahn, W., Kahn, R. S., & Thakkar, K. N. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00155.2015 · Cited by 34
Kang, J., Pae, C., & Park, H. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222161 · Cited by 33
Stochastic cortical neurodynamics underlying the memory and cognitive changes in aging
Rolls, E. T., & Deco, G. (2014) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2014.12.003 · Cited by 34
Self-citationAnticevic, A., Schleifer, C., & Cho, Y. (2015) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2015.17.4/aanticevic · Cited by 34
Attractor Network Dynamics, Transmitters, and Memory and Cognitive Changes in Aging
Rolls, E. T. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108554350.014 · Cited by 32
Self-citationRevisiting the role of computational neuroimaging in the era of integrative neuroscience
Loosen, A. M., Kato, A., & Gu, X. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01946-8 · Cited by 38
Radical embodied cognitive neuroscience: addressing “grand challenges” of the mind sciences
Favela, L. H. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00796 · Cited by 33
Prediction of Decisions from Noise in the Brain before the Evidence is Provided
Rolls, E. T., & Deco, G. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00033 · Cited by 30
Self-citationHumpston, C. S., Adams, R. A., Benrimoh, D. A., Broome, M. R., Corlett, P. R., Gerrans, P., Horga, G., Parr, T., Pienkos, E., Powers, A. R., et al. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby073 · Cited by 30
Neural and neuronal discoordination in schizophrenia: from ensembles through networks to symptoms
Krajcovic, B., Fajnerova, I., Horacek, J., Kelemen, E., Kubik, S., Svoboda, J., & Stuchlik, A. (2019) · Acta Physiologica · doi:10.1111/apha.13282 · Cited by 29
Cortical attractor network dynamics with diluted connectivity
Rolls, E. T., & Webb, T. J. (2011) · Brain Research · doi:10.1016/j.brainres.2011.08.002 · Cited by 31
Self-citationKang, J., Jeong, S., Pae, C., & Park, H. (2021) · Human Brain Mapping · doi:10.1002/hbm.25442 · Cited by 24
Face processing in different brain areas, and critical band masking
Rolls, E. T. (2008) · Journal of Neuropsychology · doi:10.1348/174866407x258903 · Cited by 28
Self-citationTowards biologically constrained attractor models of schizophrenia
Stein, H., Barbosa, J., & Compte, A. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.10.013 · Cited by 27
A Computational Model for Spatial Working Memory Deficits in Schizophrenia
Cano-Colino, M., & Compte, A. (2012) · Pharmacopsychiatry · doi:10.1055/s-0032-1306314 · Cited by 26
Modeling neuropathologies as disruption of normal sequence generation in working memory networks
Verduzco-Flores, S. O., Ermentrout, B., & Bodner, M. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.09.007 · Cited by 25
Communication before coherence
Rolls, E. T., Webb, T. J., & Deco, G. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08188.x · Cited by 25
Self-citationModeling Neurotransmission: Computational Tools to Investigate Neurological Disorders
Gandolfi, D., Boiani, G. M., Bigiani, A., & Mapelli, J. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094565 · Cited by 28
Ramírez-Mahaluf, J. P., Tepper, Á., Alliende, L. M., Mena, C., Castañeda, C. P., Iruretagoyena, B., Nachar, R., Reyes-Madrigal, F., León-Ortíz, P., Mora-Durán, R., et al. (2022) · Schizophrenia Bulletin · doi:10.1093/schbul/sbac187 · Cited by 22
Okimura, T., Tanaka, S., Maeda, T., Kato, M., & Mimura, M. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.03.039 · Cited by 21
Clinical Applications of Stochastic Dynamic Models of the Brain, Part II: A Review
Roberts, J. A., Friston, K. J., & Breakspear, M. J. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.12.009 · Cited by 21
A functional theory of bistable perception based on dynamical circular inference
Leptourgos, P., Bouttier, V., Jardri, R., & Denève, S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008480 · Cited by 22
The shallow cognitive map hypothesis: A hippocampal framework for thought disorder in schizophrenia
Musa, A., Khan, S., Mujahid, M., & El-Gaby, M. (2022) · Schizophrenia · doi:10.1038/s41537-022-00247-7 · Cited by 28
Excessive Attractor Instability Accounts for Semantic Priming in Schizophrenia
Lerner, I., Bentin, S., & Shriki, O. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040663 · Cited by 22
Robust sequential working memory recall in heterogeneous cognitive networks
Rabinovich, M. I., Sokolov, Y., & Kozma, R. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00220 · Cited by 20
Noise in Attractor Networks in the Brain Produced by Graded Firing Rate Representations
Webb, T. J., Rolls, E. T., Deco, G., & Feng, J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023630 · Cited by 20
Self-citationPatient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia
Alamian, G., Pascarella, A., Lajnef, T., Knight, L., Walters, J., Singh, K. D., & Jerbi, K. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102485 · Cited by 16
Computational Models of Hallucinations
Jardri, R., & Denève, S. (2013) · The Neuroscience of Hallucinations · doi:10.1007/978-1-4614-4121-2_16 · Cited by 16
Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics
Jeong, S., Kang, J., Pae, C., Eo, J., Park, S., Son, J., & Park, H. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118618 · Cited by 14
Complex Systems Science and Brain Dynamics: A Frontiers in Computational Neuroscience Special Topic
Siegelmann, H. T. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00007 · Cited by 15
How Memory Conforms to Brain Development
Millán, A. P., Torres, J. J., & Marro, J. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00022 · Cited by 14
The Role of Infrastructure in the Transformation of Child–Adolescent Mental Health Systems
Stelk, W. J., & Slaton, E. (2010) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-010-0282-3 · Cited by 15
Sakamoto, K., Katori, Y., Saito, N., Yoshida, S., Aihara, K., & Mushiake, H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080906 · Cited by 13
Yin, L., Han, F., & Wang, Q. (2023) · Cognitive Neurodynamics · doi:10.1007/s11571-023-09999-z · Cited by 14
From reward value to decision-making
Rolls, E. T. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374620-7.00005-4 · Cited by 12
Self-citationComputational Approach to Schizophrenia: Disconnection Syndrome and Dynamical Pharmacology
Érdi, P., Flaugher, B., Jones, T., Ujfalussy, B. B., Zalányi, L., Diwadkar, V. A., Ricciardi, L. M., Buonocore, A., & Pirozzi, E. (2008) · AIP conference proceedings · doi:10.1063/1.2965102 · Cited by 12
Metadata
- Authors
- Loh, M., Rolls, E. T., & Deco, G.
- Affiliations
- Universitat Pompeu Fabra (ES)
- University of Oxford (GB)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Corresponding author
- Rolls, Edmund T
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2007-11-09
- Volume
- 3
- Issue
- 11
- Pages
- e228
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2065887
- OpenAlex
- W2113693127
- Cited by
- 164
- References
- 84
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 6 · 2023: 9 · 2022: 5 · 2021: 7 · 2020: 3 · 2019: 8 · 2018: 11 · 2017: 13 · 2016: 5 · 2015: 12 · 2014: 7 · 2013: 12 · 2012: 11
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Boehringer Ingelheim Fonds
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- schizophrenia, dynamical systems, basins of attraction, short-term memory, distractibility, working memory deficits, negative symptoms, positive symptoms, orbitofrontal cortex, anterior cingulate cortex, statistical fluctuations, network stability
- MeSH terms
- Action Potentials, Animals, Brain, Computer Simulation, Humans, Models, Neurological, Nerve Net, Synaptic Transmission, Schizophrenia, Receptors, N-Methyl-D-Aspartate, Receptors, GABA
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
