Theories of consciousness

Nature reviews. Neuroscience, Год журнала: 2022, Номер 23(7), С. 439 - 452

Опубликована: Май 3, 2022

Язык: Английский

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Hallucinations and Strong Priors

Trends in Cognitive Sciences, Год журнала: 2018, Номер 23(2), С. 114 - 127

Опубликована: Дек. 21, 2018

Recent data establish a role for strong prior beliefs in the genesis of hallucinations. These are difficult to reconcile with aberrant inner-speech theories, which 'weaker' predictions about potential consequences one's own inner speech drive an inference that is emanating from agent external oneself. In predictive-coding view, this failure self-prediction renders surprising. The prediction errors induced explained away by higher-level priors identified recent work. presence and contents hallucinations can be understood terms learning, inference, reliability-based trade-off between internal information sources, biased toward high-level priors. If divorce perception sensation somewhat, distinction hallucination becomes less clear. We hope explanation more understandable stigmatizing. Hallucinations, perceptions absence objectively identifiable stimuli, illustrate constructive nature perception. Here, we highlight as critical elicitor empirical work independent laboratories shows strong, overly precise engender healthy subjects individuals who hallucinate real world susceptible these laboratory phenomena. consider observations light demonstrating apparently weak, or imprecise, psychosis. Appreciating interactions within hierarchies apparent disconnect. Data neural networks, human behavior, neuroimaging support contention. This underlines continuum normal perception, encouraging empathic approach clinical 'Instead saying false exterior percept, one should say percept true hallucination.' [1Taine H. De l'intelligence. Librairie Hachette et Cie, 1870Google Scholar] Hallucinations (see Glossary) percepts without corresponding stimuli [2Tracy D.K. Shergill S.S. Mechanisms underlying auditory hallucinations-understanding stimulus.Brain Sci. 2013; 3: 642-669Crossref PubMed Google Scholar]. They characterize many serious mental illnesses such schizophrenia post-traumatic stress disorder [3McCarthy-Jones S. Longden E. Auditory verbal disorder: common phenomenology, cause, interventions?.Front. Psychol. 2015; 6: 1071Crossref occur context Alzheimer's Parkinson's diseases epilepsy [4Hauf M. al.Common mechanisms hallucinations-perfusion studies epilepsy.Psychiatry Res. 211: 268-270Abstract Full Text PDF Scopus (0) Scholar], hearing loss [5Sommer I.E. al.Hearing loss; neglected risk factor psychosis.Schizophr. 2014; 158: 266-267Abstract eye disease [6Salakhutdinov R. Hinton G. An efficient learning procedure deep Boltzmann machines.Neural Comput. 2012; 24: 1967-2006Crossref (194) But they frequently any detectable illness, isolated experiences up 50% people (e.g., following bereavement) 2 10% population on daily basis [7Honig A. al.Auditory hallucinations: comparison patients nonpatients.J. Nerv. Ment. Dis. 1998; 186: 646-651Crossref (212) all sensory modalities, although visual most commonly reported. There has been long growing appreciation Scholar]: it than mere receipt information, instead involving synthetic process, based upon expectancies (henceforth priors) [8de Lange F.P. al.How do expectations shape perception?.Trends Cogn. 2018; 22: 764-779Abstract While regard processing [9Barlow Conditions versatile Helmholtz's unconscious task perception.Vision 1990; 30: 1561-1571Crossref prone error. particular, similar Scholar, 10Grush emulation theory representation: motor control, imagery, perception.Behav. Brain 2004; 27 (discussion 396–442): 377-396Crossref (628) concerned generating [11Powers III, A.R. al.Hallucinations top-down effects perception.Biol. Psychiatry Neurosci. Neuroimaging. 2016; 1: 393-400Abstract (66) focus (AVHs) voices, principles outline do, believe, extend beyond audition. AVHs around 80% diagnosed schizophrenia, but present us profound problems: since so common, their status diagnostic markers illness very uncertain [12Powers 3rd, al.Varieties voice-hearing: psychics psychosis continuum.Schizophr. Bull. 2017; 43: 84-98Crossref (19) Moreover, when treatment indicated, can, significant proportion people, prove persistent [13Shergill review psychological treatments.Schizophr. 32: 137-150Abstract (180) Thus, there need understand AVHs. using formal computational models [14Halligan P.W. David A.S. Cognitive neuropsychiatry: towards scientific psychopathology.Nat. Rev. 2001; 2: 209-215Crossref (83) suggest arise exert inordinate influence 15Friston K.J. perceptual inference.Behav. 2005; 28: 764-766Crossref (51) According predictive coding accounts, involves adopting hypothesis explains what causing our current [16Helmholtz H.v. Handbuch der physiologischen Optik. Voss, 1867Google optimized knowledge probable candidates Those compared incoming sensation, computed. inputs, them strong. will dominate ignored [17Feldman Friston Attention, uncertainty, free-energy.Front. Hum. 2010; 4: 215Crossref 18Friston K. Kiebel Predictive under free-energy principle.Philos. Trans. Soc. Lond. B Biol. 2009; 364: 1211-1221Crossref (341) 19Friston Stephan K.E. Free-energy brain.Synthese. 2007; 159: 417-458Crossref 20Teufel C. al.The Bayesian perception.Front. 7: 25Crossref By contrast, relatively belief updating (changing subsequent inference). powerful contribution expectation leads speculate might over inferences, creating no at That could volunteers was first reported Carl Seashore, working Yale Psychological Laboratory 1895. Seashore modality (touch, sight, taste) conditional learned cues another engendered suggestion [21Seashore, C.E. (1895) Measurements Illusions life. Reprinted Studies Laboratory, Vol. 3Google A example suggestion: were instructed would hear song White Christmas, played white noise, 5% experiencing Bing Crosby's voice [22Barber T.X. Calverley D.S. experimental study hypnotic (auditory visual) hallucinations.J. Abnorm. 1964; 68: 13-20Crossref People hallucinatory voices everyday lives effect [23Mintz Alpert Imagery vividness, reality testing, schizophrenic 1972; 79: 310-316Crossref (119) Pavlov's assertion inferences conditioned reflex [24Pavlov I.P. Natural science brain.Lectures Conditioned Reflexes: Twenty-five Years Objective Study Higher Nervous Activity (Behaviour) Animals. Liverwright Publishing Corporation, 1928Crossref garnered support. Ellson 1940s embraced conditioning paradigm used controlled procedures consistent stimulus delivery. illumination tungsten filament bulb presented predictor presentation near threshold 1-kHz tone. After association, tone omitted participants continue report tones, [25Ellson D.G. produced conditioning.J. Exp. 1941; 1-20Crossref underline predicting not auditory. Tones likewise established predictors dimly illuminated triangles), (of triangles never presented). even transfer out laboratory; seeing television screen, tone, [26Davies P. al.An effective subjects.Perception. 1982; 11: 663-669Crossref More recently, visual-auditory demonstrate voice-hearing significantly controls [27Kot T. Serper Increased susceptibility hallucinating patients: preliminary investigation.J. 2002; 190: 282-288Crossref (27) However, (and those preceded them) have driven demand characteristics perceived situation. contemporary research sought mitigate concern. Prior scene confers advantage recognizing degraded version image [28Teufel al.Shift early psychosis-prone individuals.Proc. Natl. Acad. U. 112: 13401-13406Crossref (48) Patients psychosis, and, extension, voice-hearing, particularly advantage, its magnitude correlated hallucination-like percepts. Similarly, audition; appear enhanced explicitly instructed. Indeed, able detect earlier control [29Alderson-Day B. al.Distinct ambiguous non-clinical hallucinations.Brain. 140: 2475-2489Crossref (6) It attribute findings characteristics. bias appears 'sensory conditioning' 30Ellson 31Warburton D.M. al.Scopolamine hallucinations.Neuropsychobiology. 1985; 14: 198-202Crossref 32Agathon Roussel M.A. Use test treated psychotropic drugs.Int. Pharmacopsychiatry. 1973; 8: 221-233PubMed 33Brogden W.J. Sensory pre-conditioning subjects.J. 1947; 37: 527-539Crossref (14) wherein modern psychophysics brought bear paradigm, establishing difficult-to-detect, near-threshold stimulus. Participants begin cue. amplified hallucinate. recently showed mediated beliefs, hallucinate, psychotic likely update new evidence [34Powers al.Pavlovian conditioning-induced result overweighting priors.Science. 357: 596-600Crossref (45) Critically, circuit phenomena, including superior temporal gyrus insula, largely overlapped engaged scanner 35Jardri al.Cortical activations during schizophrenia: coordinate-based meta-analysis.Am. J. Psychiatry. 2011; 168: 73-81Crossref (299) Individuals striatal dopamine (itself marker [36Jauhar al.A transdiagnostic positron emission tomographic imaging bipolar affective schizophrenia.JAMA 74: 1206-1213Crossref (7) Scholar]) impact perception: higher perceive target embedded stream rather actually [37Cassidy C.M. mechanism linked dopamine.Curr. 28 (e4): 503-514Abstract Visual related [38O'Callaghan al.Visual characterized impaired accumulation: insights hierarchical drift diffusion modeling disease.Biol. 680-688Abstract (4) may dopaminergic, cholinergic, serotonergic perturbations [39Factor S.A. neurotransmitters development disease-related psychosis.Eur. Neurol. 1244-1254Crossref here AVHs, underwrite other modalities illnesses. challenge characterizing discerning why each specific pathology culminates visions, delusions (Box 1).Box 1Hallucinations Delusions: Commonalities DifferencesDelusions tend co-occur, always. psychiatry literature, dissociated. Whereas consistently priors, associated priors.For instance, examining relation delusional motion reveals delusion-proneness well weaker statistical [111Schmack al.Delusions inference.J. 33: 13701-13712Crossref (54) 112Schmack al.Perceptual instability probing accounts stimuli.Schizophr. 72-77Crossref 113Stuke al.Delusion proneness reduced usage decisions.Schizophr. (Published online January 20, 2018)https://doi.org/10.1093/schbul/sbx189Crossref (2) high-precision cognitive manipulation (a 3D glasses shift 114Schmack al.Enhanced signalling schizophrenia.Hum. Mapp. 38: 1767-1779Crossref (3) Scholar]). contradictory reconciled assuming hierarchy brain's machinery: imprecise (or attenuate precision) lead formation beliefs. At same time, level sculpt subtending maintenance (by engendering delusion [115Corlett P.R. al.From drugs deprivation: framework understanding psychosis.Psychopharmacology (Berl.). 206: 515-530Crossref (131) Scholar]).This comorbidity structure symptoms, three-factor solution: negative disorganization, positive symptoms (comprising delusions) [116Liddle P.F. chronic schizophrenia. re-examination positive-negative dichotomy.Br. 1987; 151: 145-151Crossref symptom [117Minas I.H. al.Positive psychoses: multidimensional scaling SAPS SANS items.Schizophr. 1992; 143-156Abstract co-occur parasitosis (that insects skin) grandiose ideas, religious referential Might biological favor relationships symptoms?A probed gist versus details images varying degrees hallucination- [118Davies D.J. al.Anomalous shifts different types inference.Schizophr. 44: 1245-1253Crossref Hallucination-proneness stronger employment global (gist) local (detail) whereas reliance differential weighting levels drives [119Kwisthout al.To precise, don't matter: processing, precision, detail predictions.Brain 84-91Crossref (1) Scholar].The precision-weighting [18Friston Neurobiologically, density recurrent connections association cortices, primary regions, psychotogenic perturbation impacts excitatory/inhibitory (E/I) balance lower [120Yang G.J. al.Functional underlies preferential connectivity disturbances schizophrenia.Proc. 113: E219-E228Crossref (26) See [121Jardri Deneve Circular schizophrenia.Brain. 136: 3227-3241Crossref (60) detailed exposition E/I brief, implement exactly underlie coding. Blocking N-methyl-d-aspartic acid (NMDA) receptors (with ketamine example) profoundly alters [122Murray J.D. al.Linking microcircuit dysfunction impairment: disinhibition cortical memory model.Cereb. Cortex. 859-872Crossref 123Anticevic al.NMDA receptor function large-scale anticorrelated systems implications cognition 109: 16720-16725Crossref (120) thus altering perhaps differently NMDA blockade decrease precision facilitating delusion-like [124Corlett al.Prediction error, psychosis: updated model.J. Psychopharmacol. 1145-1155Crossref (22) does typically (except circumstances high environmental uncertainty [125Powers al.Ketamine-induced hallucinations.Psychopathology. 48: 376-385Crossref (24) sustained entail neuroplastic changes dopamine-driven pyramidal cells characteristic acute ketamine. administration amphetamine hallucinogens Comparing contrasting phenomenologies across key hypothesis. suspect case, where hallucinations, Delusions For

Язык: Английский

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Theories of Error Back-Propagation in the Brain

Trends in Cognitive Sciences, Год журнала: 2019, Номер 23(3), С. 235 - 250

Опубликована: Янв. 30, 2019

The error back-propagation algorithm can be approximated in networks of neurons, which plasticity only depends on the activity presynaptic and postsynaptic neurons. These biologically plausible deep learning models include both feedforward feedback connections, allowing errors made by network to propagate through layers. rules different implemented with types spike-time-dependent plasticity. dynamics described within a common framework energy minimisation. This review article summarises recently proposed theories how neural circuits brain could approximate used artificial networks. Computational implementing these achieve as efficient networks, but they use simple synaptic based have similarities, such including information about throughout network. Furthermore, incorporate experimental evidence connectivity, responses, provide insights might organised that modification weights multiple levels cortical hierarchy leads improved performance tasks. In past few years, computer programs using (see Glossary) achieved impressive results complex cognitive tasks were previously reach humans. processing natural images language [1LeCun Y. et al.Deep learning.Nature. 2015; 521: 436-444Crossref PubMed Scopus (42113) Google Scholar], or playing arcade board games [2Mnih V. al.Human-level control reinforcement 518: 529-533Crossref (13741) Scholar, 3Silver D. al.Mastering game Go tree search.Nature. 2016; 529: 484-489Crossref (8554) Scholar]. Since recent applications extended versions classic [4Rumelhart D.E. al.Learning representations back-propagating errors.Nature. 1986; 323: 533-536Crossref (15380) their success has inspired studies comparing brain. It been demonstrated when learn perform image classification navigation, neurons layers develop similar those seen areas involved tasks, receptive fields across visual grid cells entorhinal cortex [5Banino A. al.Vector-based navigation grid-like agents.Nature. 2018; 557: 429-433Crossref (289) 6Whittington, J.C.R. al. (2018) Generalisation structural knowledge hippocampal-entorhinal system. 31st Conference Neural Information Processing Systems (NIPS 2018), MontrealGoogle 7Yamins D.L. DiCarlo J.J. Using goal-driven understand sensory cortex.Nat. Neurosci. 19: 356-365Crossref (650) suggests may analogous algorithms. thanks current computational advances, now useful functions are [8Bowers J.S. Parallel distributed theory age networks.Trends Cogn. Sci. 2017; 21: 950-961Abstract Full Text PDF (22) A key question remains open is implement describes connections should modified during learning, its attractiveness, part, comes from prescribing weight changes reduce network, according theoretical analysis. Although originally brain, weights, appears unrealistic [9Crick F. excitement networks.Nature. 1989; 337: 129-132Crossref (353) 10Grossberg S. Competitive learning: interactive activation adaptive resonance.Cogn. 1987; 11: 23-63Crossref Nevertheless, [11Bengio al.STDP-Compatible approximation backpropagation an energy-based model.Neural Comput. 29: 555-577Crossref (47) 12Guerguiev J. al.Towards segregated dendrites.eLife. 6e22901Crossref (173) 13Sacramento, Dendritic microcircuits algorithm. 14Whittington Bogacz R. An predictive coding local Hebbian plasticity.Neural 1229-1262Crossref (91) theoretic important because overrule dogma, generally accepted for 30 too complicated Before discussing this new generation detail, we first brief overview train discuss why it was considered implausible. To effectively feedback, often need appropriately adjusted hierarchical simultaneously. For example, child learns name letters, incorrect pronunciation combined result speech, associative, areas. When multi-layer makes error, assigns credit individual synapses all prescribes much. How networks? trained set examples, each consisting input pattern target pattern. pair, generates prediction then minimise difference between predicted determine appropriate modification, term computed neuron change discrepancy (Box 1). Each amount determined product projects to.Box 1Artificial NetworksA conventional consists layer receiving weighted previous (Figure IA). propagating layers, Equation 1.1, where xl vector denoting l Wl−1 matrix − 1 l. function f applied allow nonlinear computations.During cost quantifying patterns (typically defined 1.2). particular, direction steepest decrease (or gradient) ID). Such 1.3, δl+1 terms associated xl+1. last L 1.4 t activity. Thus, output positive if higher than earlier 1.5 sum above strengths (and further scaled derivative function; · denotes element-wise multiplication). hidden unit sends excitatory projections units high terms, so increasing would output. Once computed, changed 1.3 proportion neuron. computations. During procedure steps take place case naming letters mentioned above, corresponds letter. After seeing image, guess at (predicted pattern) via speech On supervision his her parent correct (target pattern), along stream more likely sound will produced again. algorithmic process enough, there problems biology. Below, briefly three issues. Conventional compute forward direction, separately external Without representation, update computations downstream biological connection strength solely signals (e.g., connect), unclear afforded Historically, major criticism; thus main focus our article. back-propagated same prediction. symmetry identical exist directions connected bidirectional significantly expected chance, not always present [15Song al.Highly nonrandom features connectivity circuits.PLoS Biol. 2005; 3: 507-519Google even present, backwards forwards still correctly align themselves. Artificial send continuous (corresponding firing rate neurons), whereas real spikes. Generalising discrete spikes trivial, derivate found Away algorithm, description inside also simplified linear summation inputs. above-mentioned issues investigated studies. lack representation addressed early proposing instead driven global signal carried neuromodulators [16Mazzoni P. al.A rule networks.Proc. Natl. Acad. U. 1991; 88: 4433-4437Crossref (138) 17Williams R.J. Simple statistical gradient-following algorithms connectionist learning.Mach. Learn. 1992; 8: 229-256Crossref 18Unnikrishnan K.P. Venugopal Alopex: correlation-based recurrent networks.Neural 1994; 6: 469-490Crossref 19Seung H.S. Learning spiking stochastic transmission.Neuron. 2003; 40: 1063-1073Abstract (238) However, slow does scale size [20Werfel curves gradient descent 17: 2699-2718Crossref More promisingly, several do represent locally closely similarly standard benchmark handwritten digit classification) [12Guerguiev 21Lillicrap T.P. al.Random support learning.Nat. Commun. 713276Crossref (336) 22Scellier B. Bengio Equilibrium propagation: bridging gap backpropagation.Front. 24Crossref (146) summarise them detail following sections. criticism demonstrating random good [21Lillicrap 23Zenke Ganguli SuperSpike: supervised multilayer 30: 1514-1541Crossref (209) 24Mostafa, H. (2017) Deep errors. arXiv preprint arXiv:1711.06756Google 25Scellier, Generalization equilibrium propagation field dynamics. 1808.04873Google 26Liao, Q. (2016) backpropagation? AAAI Intelligence, pp. 1837–1844, AAAIGoogle 27Baldi Sadowski channel, optimality backpropagation.Neural Netw. 83: 51-74Crossref (39) being said, some concern regarding issue [28Bartunov, Assessing scalability biologically-motivated architectures. With regard realism shown generalised producing [29Sporea I. Grüning Supervised 2013; 25: 473-509Crossref (97) Scholar] calculating derivatives overcome [23Zenke realistic considered, themselves small dendritic structures [30Schiess M. al.Somato-dendritic error-backpropagation active dendrites.PLoS 12e1004638Crossref (43) There diversity ideas [31Balduzzi, (2015) Kickback cuts backprop's red-tape: assignment 485–491, 32Krotov, Hopfield, Unsupervised competing units. arXiv:1806.10181Google 33Kuśmierz Ł. factors: modulating errors.Curr. Opin. Neurobiol. 46: 170-177Crossref (52) 34Marblestone A.H. al.Toward integration neuroscience.Front. 10: 94Crossref (316) 35Bengio, (2014) auto-encoders propagation. arXiv:1407.7906Google 36Lee, D.-H. Difference Joint European Machine Knowledge Discovery Databases, 498–515, SpringerGoogle Scholar]; however, principles behind related 37O'Reilly R.C. Biologically error-driven differences: generalized recirculation algorithm.Neural 1996; 895-938Crossref (211) substantial data while paralleling operate minimal control, modifications depend biology, spike time-dependent plasticity, properties pyramidal microcircuits. We emphasise rely fundamentally principles. thereby without requiring program dynamics, well divide reviewed two classes differing represented, class model encodes differences time. contrastive [37O'Reilly relies observation proportional (difference decomposed into separate updates: one other provided [38Ackley D.H. Boltzmann machines.Cogn. 1985; 9: 147-169Crossref 2). twice: anti-Hebbian once converges (after propagated connections) role 'unlearn' existing association prediction, second target.Box 2Temporal-Error ModelsTemporal-error describe nodes given node summed inputs adjacent decay level IB). As recurrent, no longer possible write equation describing (such 1.1 Box 1); instead, differential 2.1 [72Pineda F.J. networks.Phys. Rev. Lett. 59: 2229-2232Crossref (594) x˙l over time (all equations figure ignore nonlinearities brevity).In model, occurring times. easiest consider connecting modified. Substituting see 2.2 required terms. O'Reilly presence backward propagates sequence approximates version Scholar].In gradually (x3|¬t) towards values (t), sample Figure ID. temporal (x˙3) (t −x3|¬t), is, (defined 1.4). Hence, simply equal (Equation 2.3). Temporal-error brevity). o

Язык: Английский

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Multimodal Language Processing in Human Communication

Trends in Cognitive Sciences, Год журнала: 2019, Номер 23(8), С. 639 - 652

Опубликована: Июнь 21, 2019

Язык: Английский

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Evaluating the neurophysiological evidence for predictive processing as a model of perception

Annals of the New York Academy of Sciences, Год журнала: 2020, Номер 1464(1), С. 242 - 268

Опубликована: Март 1, 2020

Abstract For many years, the dominant theoretical framework guiding research into neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series increasingly complex feature detectors. However, long‐standing orthodoxy these accounts recently challenged radically different set theories that contend perception arises from purely inferential process supported two distinct classes neurons: those transmit predictions about states and signal information deviates predictions. Although predictive processing (PP) models have become influential cognitive neuroscience, they also criticized for lacking empirical support to justify their status. This limited evidence base partly reflects considerable methodological challenges presented when trying test unique models. confluence technological advances prompted recent surge human nonhuman neurophysiological seeking fill this gap. Here, we will review new evaluate degree its findings key claims PP.

Язык: Английский

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Cellular Mechanisms of Conscious Processing

Trends in Cognitive Sciences, Год журнала: 2020, Номер 24(10), С. 814 - 825

Опубликована: Авг. 24, 2020

Recent breakthroughs in neurobiology indicate that the time is ripe to understand how cellular-level mechanisms are related conscious experience. Here, we highlight biophysical properties of pyramidal cells, which allow them act as gates control evolution global activation patterns. In states, this cellular mechanism enables complex sustained dynamics within thalamocortical system, whereas during unconscious such signal propagation prohibited. We suggest hallmark processing flexible integration bottom-up and top-down data streams at level. This provides foundation for Dendritic Information Theory, a novel neurobiological theory consciousness.

Язык: Английский

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Being a Beast Machine: The Somatic Basis of Selfhood

Trends in Cognitive Sciences, Год журнала: 2018, Номер 22(11), С. 969 - 981

Опубликована: Сен. 16, 2018

Язык: Английский

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Cognitive and Human Factors in Expert Decision Making: Six Fallacies and the Eight Sources of Bias

Analytical Chemistry, Год журнала: 2020, Номер 92(12), С. 7998 - 8004

Опубликована: Июнь 8, 2020

Fallacies about the nature of biases have shadowed a proper cognitive understanding and their sources, which in turn lead to ways that minimize impact. Six such fallacies are presented: it is an ethical issue, only applies "bad apples", experts impartial immune, technology eliminates bias, blind spot, illusion control. Then, eight sources bias discussed conceptualized within three categories: (A) factors relate specific case analysis, include data, reference materials, contextual information, (B) person doing past experience base rates, organizational factors, education training, personal lastly, (C) architecture human impacts all us. These can impact what data (e.g., how sampled collected, or considered as noise therefore disregarded), actual results decisions on testing strategies, analysis conducted, when stop testing), conclusions interpretation results). The paper concludes with measures these biases.

Язык: Английский

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The Perceptual Prediction Paradox

Trends in Cognitive Sciences, Год журнала: 2019, Номер 24(1), С. 13 - 24

Опубликована: Ноя. 28, 2019

Язык: Английский

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Prediction and memory: A predictive coding account

Progress in Neurobiology, Год журнала: 2020, Номер 192, С. 101821 - 101821

Опубликована: Май 21, 2020

The hippocampus is crucial for episodic memory, but it also involved in online prediction. Evidence suggests that a unitary hippocampal code underlies both memory and predictive processing, yet within coding framework the hippocampal-neocortical interactions accompany these two phenomena are distinct opposing. Namely, during recall, thought to exert an excitatory influence on neocortex, reinstate activity patterns across cortical circuits. This contrasts with empirical theoretical work where descending predictions suppress prediction errors 'explain away' ascending inputs via inhibition. In this hypothesis piece, we attempt dissolve previously overlooked dialectic. We consider how may facilitate respectively, by inhibiting neocortical or increasing their gain. propose processing modes depend upon neuromodulatory gain (or precision) ascribed error units. Within framework, recall cast as arising from fictive furnish training signals optimise generative models of world, absence sensory data.

Язык: Английский

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