The neuroconnectionist research programme

Nature reviews. Neuroscience, Journal Year: 2023, Volume and Issue: 24(7), P. 431 - 450

Published: May 30, 2023

Language: Английский

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Prediction during language comprehension: what is next?

Trends in Cognitive Sciences, Journal Year: 2023, Volume and Issue: 27(11), P. 1032 - 1052

Published: Sept. 11, 2023

Prediction is often regarded as an integral aspect of incremental language comprehension, but little known about the cognitive architectures and mechanisms that support it. We review studies showing listeners readers use all manner contextual information to generate multifaceted predictions upcoming input. The nature these may vary between individuals owing differences in experience, among other factors. then turn unresolved questions which guide search for underlying mechanisms. (i) Is prediction essential processing or optional strategy? (ii) Are generated from within system by domain-general processes? (iii) What relationship memory? (iv) Does comprehension require simulation via production system? discuss promising directions making progress answering developing a mechanistic understanding language.

Language: Английский

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Collective behavior from surprise minimization

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(17)

Published: April 17, 2024

Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move a whole, exhibiting rich behavioral repertoire that ranges from directed movement milling disordered swarming. Typically, macroscopic patterns arise decentralized, local interactions among constituent components (e.g., individual fish school). Preeminent models this process describe individuals self-propelled particles, subject self-generated “social forces” short-range repulsion long-range attraction or alignment. However, organisms are not particles; they probabilistic decision-makers. Here, we introduce an approach modeling collective behavior based on active inference. This cognitive framework casts the consequence single imperative: minimize surprise. We demonstrate many empirically observed phenomena, including cohesion, milling, motion, emerge naturally when considering driven by Bayesian inference—without explicitly building rules goals into agents. Furthermore, show inference can recover generalize classical notion social forces agents attempt suppress prediction errors conflict with their expectations. By exploring parameter space belief-based model, reveal nontrivial relationships between beliefs group properties like polarization tendency visit different states. also explore how about uncertainty determine decision-making accuracy. Finally, update generative model over time, resulting collectively more sensitive external fluctuations encode information robustly.

Language: Английский

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Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Nature Machine Intelligence, Journal Year: 2023, Volume and Issue: 5(12), P. 1369 - 1381

Published: Nov. 20, 2023

Abstract Brain networks exist within the confines of resource limitations. As a result, brain network must overcome metabolic costs growing and sustaining its physical space, while simultaneously implementing required information processing. Here, to observe effect these processes, we introduce spatially embedded recurrent neural (seRNN). seRNNs learn basic task-related inferences existing three-dimensional Euclidean where communication constituent neurons is constrained by sparse connectome. We find that converge on structural functional features are also commonly found in primate cerebral cortices. Specifically, they solving using modular small-world networks, which functionally similar units configure themselves utilize an energetically efficient mixed-selective code. Because emerge unison, reveal how many common motifs strongly intertwined can be attributed biological optimization processes. incorporate biophysical constraints fully artificial system serve as bridge between research communities move neuroscientific understanding forwards.

Language: Английский

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Efficient mechanisms

Philosophical Psychology, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 24

Published: March 21, 2023

A distinguishing feature of neural computation and information processing is that it fits models describe the most efficient strategies for performing different cognitive tasks. Efficiency determines a distinctive sense teleology involving optimal performance resource management through specific strategy. I articulate this kind call teleological function. argue function compatible with mechanistic explanation and, likely, computational mechanisms are efficiently functional in sense. They members class whose efficiency intertwined their functionality. This illustrated by widely discussed approaches to mind, such as Barlow's coding hypothesis or ones associated so-called "predictive mind", which propose brain employs highly save energy resources critical organism's survival.

Language: Английский

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Hierarchical predictive coding in distributed pain circuits

Frontiers in Neural Circuits, Journal Year: 2023, Volume and Issue: 17

Published: March 3, 2023

Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing motor control. Nociceptive pain involves large distributed network of circuits. However, it still unknown whether this completely decentralized or requires networkwide coordination. Multiple lines evidence from human animal studies have suggested that cingulate cortex insula (cingulate-insula network) are two major hubs mediating information afferents spinothalamic inputs, whereas subregions cortices distinct projections functional roles. In mini-review, we propose an updated hierarchical predictive framework for perception discuss its related computational, algorithmic, implementation issues. We suggest active inference as generalized algorithm, hierarchically organized traveling waves independent neural oscillations plausible mechanism to integrate bottom-up top-down across

Language: Английский

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An active inference perspective for the amygdala complex

Trends in Cognitive Sciences, Journal Year: 2023, Volume and Issue: 28(3), P. 223 - 236

Published: Dec. 15, 2023

We outline how a predictive processing framework based on active inference can help overcome the limitations of fragmented feed-forward theories and more comprehensively explain role amygdala in anxiety, fear, danger detection.This integrates theoretical predictions with empirical findings, suggesting that central subnucleus acts as Bayesian regulator an interoceptive self-model, sending top-down to basolateral for efficient perception.Fear conditioning is discussed example proactive homeostatic regulation, where exteroceptive cues are used anticipate negative experiences.By extension, other functions (e.g., its approach/avoidance behavior or attention) be explained using same computational principles within hierarchical model. The heterogeneous network subcortical nuclei importance cognitive clinical neuroscience. Various experimental designs human psychology animal model research have mapped multiple conceptual frameworks valence/salience decision making) ever refined circuitry. However, these predominantly bottom up-driven accounts often rely interpretations tailored specific phenomenon, thus preventing comprehensive integrative theories. argue here function could unify fractionated approaches into overarching clearer mechanistic interpretations. This embeds models, informed by prior knowledge belief updating, dynamical system distributed across circuits which self-regulation implemented continuously tracking environmental demands. subnuclei quintessential social, cognitive, affective, neuroscience [1.Adolphs R. What does contribute social cognition?.Ann. N. Y. Acad. Sci. 2010; 1191: 42-61Crossref PubMed Scopus (613) Google Scholar, 2.Bickart K.C. et al.The hub brain networks support life.Neuropsychologia. 2014; 63: 235-248Crossref (246) 3.Rosenberger L.A. indispensable experiential learning.Curr. Biol. 2019; 29: 3532-3537Abstract Full Text PDF (22) 4.Terburg D. essential rapid escape: rodent study.Cell. 2018; 175: 723-735Abstract (89) Scholar]. With neuroimaging circuit charting functional heterogeneity [5.Sladky al.Unsmoothed MRI bed nucleus stria terminalis during emotional faces.Neuroimage. 168: 383-391Crossref (21) 6.Sladky al.Basolateral orchestrate we learn whom trust.Commun. 2021; 4: 1-9Crossref (3) 7.Torrisi S. al.Extended connectivity changes sustained shock anticipation.Transl. Psychiatry. 8: 1-12Crossref (0) 8.Tillman R.M. al.Intrinsic extended amygdala.Hum. Brain Mapp. 39: 1291-1312Crossref (51) Scholar], our understanding has become detailed, yet fractionated. Moreover, some key such fear conditioning, do not easily replicate paradigms organisms [9.Visser al.Robust BOLD responses faces but conditioned threat: challenging amygdala's reputation extinction learning.J. Neurosci. 41: 10278-10292Crossref (15) problem lies data absence theory would allow unifying less situational ad hoc interpretation function(s). comprises several smaller [10.Janak P.H. Tye K.M. From behaviour amygdala.Nature. 2015; 517: 284-292Crossref (1205) (BLA) considered mainly stimulus sensory further divided two parts: lateral (LA) sensitive unimodal sensations [11.Uwano T. al.Neuronal responsiveness various stimuli, associative learning rat amygdala.Neuroscience. 1995; 68: 339-361Abstract (143) Scholar] connections cortices thalamus, whereas basal (BA) encodes multimodal contextual features [12.Yaniv al.A gradient plasticity revealed cortical stimulation, vivo.Neuroscience. 2001; 106: 613-620Abstract (32) behavioral [13.Kyriazi P. al.Multi-dimensional coding neurons.Neuron. 99: 1315-1328Abstract (64) hippocampus entorhinal cortex [14.Davis Reijmers L.G. dynamic nature engrams amygdala.Brain Res. Bull. 141: 44-49Crossref (18) allowing abstract encodings [15.Duvarci Pare Amygdala microcircuits controlling learned fear.Neuron. 82: 966-980Abstract (523) (CeA) reflect general motivational aspects [16.Balleine B.W. Killcross Parallel incentive processing: integrated view function.Trends 2006; 272-279Abstract (452) Scholar,17.Shackman A.J. al.Dispositional negativity: psychological neurobiological perspective.Psychol. 2016; 142: 1275-1314Crossref (109) and, together distal (BST), might encode features, threat (Table 1, Key table). Although cell populations marked differences types, neurotransmitter binding sites, gene expression, [18.Babaev O. al.Inhibition anxiety circuitry.Exp. Mol. Med. 50: 1-16Crossref (168) Scholar,19.Hur J. al.Anxiety neurobiology temporally uncertain anticipation.J. 2020; 40: 7949-7964Crossref (48) inhabit small space, differentiation found noninvasive fMRI, between BLA CeA [6.Sladky BST [7.Torrisi given high fMRI sensitivity ultra-high field fMRI) and/or well-powered tasks providing sufficient specificity.Table 1Key table. Feed-forward versus selected topicsAnatomical diagramFeed-forward processingActive inferenceLA connected well BA. connects direct indirect pathways (via insula forebrain)Theory integrate stimuli gate behaviorCeA self-model passes valence BLA, tune predictionsTopic 1: detection BLASensory signaled CeA. How filter relevant features?Sensations actively filtered (suppressed enhanced) thalamus areas, sensingTopic 2: CeASensations trigger feature makes particular sensation fear-inducing?CeA form self-regulatory process. Fear deviations homeo-/allostatic set-pointsTopic 3: conditioningNeutral associated feared stimulus. affective value assigned previously neutral stimuli?(Harmless) predict from priors adaptive behaviorTopic approach avoidance causes somatic changes. actions gated regulated?Actions parallel processes levels (BLA, attention; CeA, stereotypical behavior; cortex, flexible behavior) Open table new tab Commonly, presented following principles: after operates relay output region motivated [20.Davis M. Whalen P.J. amygdala: vigilance emotion.Mol. 6: 13-34Crossref (2347) Scholar,21.LeDoux J.E. Emotion brain.Annu. Rev. 2000; 23: 155-184Crossref (6385) Modern views appreciate complexity executive control information integration [22.Fadok J.P. al.New perspectives function.Curr. Opin. Neurobiol. 49: 141-147Crossref (148) still face substantial limitations. Foremost, at what stage mere transformed meaning organism danger, safety, reward)? different topics 1), typically studied isolation, unified under one biological/computational principle? Empirical aimed relating microcircuit dynamics basic primitives affectivity positive/negative valence; see Glossary) active/passive responding) Scholar,23.Fadok competitive inhibitory selection passive responses.Nature. 2017; 542: 96-100Crossref (267) 24.Gozzi A. neural switch 67: 656-666Abstract (150) 25.Pliota al.Stress peptides sensitize circuitry promote coping.Mol. 25: 428-441Crossref (11) limited success attribute traditional perspective. First, even carefully planned experiments, humans animals exhibit striking variability their behavior, pointing toward considerable intersubjective state dependencies responses. handled treating uninformative noise, essentially means discarding part observed data; alternatively, additional confounding variables obscure apparent neutrality behavioristic research. Then, no longer describes naturally observable relationship response: it contaminated artifactual assumptions. Second, mounting evidence resting-state reveals intrinsic activity independent external perturbations condition fundamental [26.Coste C.P. al.Ongoing fluctuations directly account intertrial indirectly intersubject Stroop task performance.Cereb. Cortex. 2011; 21: 2612-2619Crossref Scholar,27.Sadaghiani large-scale perception.Proc. Natl. U. 112: 8463-8468Crossref (163) Third, level subjective, first-person experience, assumption cognition only triggered violates validity. All necessary corollary steps imply that, sake operationalization, important usually accounted for, neither nor practice. Stimulus-driven, models replaced action-oriented self-regulation, driven higher hierarchy states lower (Box 1). If input diverges predictions, prediction error signal passed back level. In this way, novel surprising relayed [28.Clark Whatever next? Predictive brains, situated agents, future science.Behav. 2013; 36: 181-204Crossref (2893) resulting computationally [29.Ali al.Predictive consequence energy efficiency recurrent networks.Patterns. 2022; 3100639Abstract (8) Scholar,30.Rao R.P.N. Ballard D.H. visual cortex: extra-classical receptive-field effects.Nat. 1999; 79-87Crossref (3074) [31.Friston K.J. Stephan K.E. Free-energy brain.Synthese. 2007; 159: 417-458Crossref (409) 32.Friston K. free-energy principle: rough guide brain?.Trends Cogn. 2009; 13: 293-301Abstract (1084) 33.Parr al.Active Inference: Free Energy Principle Mind, Brain, Behavior. MIT Press, 2022Crossref regulation 2). Prediction errors known reinforcement rewards [34.Hollerman J.R. Schultz W. Dopamine neurons report temporal reward learning.Nat. 1998; 304-309Crossref Scholar,35.Mirenowicz Preferential activation midbrain dopamine appetitive rather than aversive stimuli.Nature. 1996; 379: 449-451Crossref (661) generalized perception action [32.Friston Scholar,36.Friston epistemic value.Cogn. 187-214Crossref (432) emotions applying concepts self-models [37.Seth A.K. Friston Active brain.Philos. Trans. Soc. B. 37120160007Crossref (448) constructed [38.Barrett L.F. emotion: interoception categorization.Soc. Affect. 12: 1-23Crossref (142) variational free [39.Joffily Coricelli G. Emotional principle.PLoS Comput. 9e1003094Crossref (171) 3). Expanding ideas, propose inference, illustrate four 1).Box 1Free energy, processing, brainFoundational principle assumes all nervous follow minimizing (variational) Fμαy, internal μ, α, observations y. To infer statistical regularities world, agent creates generative (m; i.e., thoughts, fantasies), tested against available (y; input, data, sensations, evidence). neurocomputational implementation hierarchical, is, send down hierarchy, while up (predictive processing). continuous bidirectional process refines reduces discrepancies expectations reality (prediction minimization). Higher slower environment (temporal depth). Going beyond passively updating coding), also act upon world make conform (i.e., change inputs receives interacting environment).Minimizing minimizes surprise Fμαy≥−lnpym,α, reduction, making special formulation processing. inverse evidence, optimization comparison, tractable biologically plausible method implementing building blocks brain. Given term reformulated Fμαy=Complexity–Accuracy minimized ways. creating sparser, efficient, generalizable Complexity. Accuracy related achieved developing accurate (m) reliably predicts (y). Actions (α) affect accuracy ways: (i) sampling way modulation attention moving eyes away stimulus) sensed selectively enhancing suppressing distinguish noise; (ii) come true prefers going dark forest turns flashlight). themselves constantly guided goals perceptual (to improve predictions) instrumental verify predictable sensation) [68.Seth Tsakiris Being beast machine: basis selfhood.Trends 22: 969-981Abstract (see Box 2 main text).Box 2Active self-regulationActive described 1 text, brain, understood finding approximate optimal solution (integrating beliefs) choice policy beliefs. Computationally, precision-weighted [49.Mathys C.D. al.Uncertainty Gaussian filter. Front. Hum, Neurosci2014: 8Google posits weight depends ratio precision (or variance, π=σ−2) [e.g., px∼Nμxπx−1] likelihood pyx∼Nμyπy−1]. posterior distribution pxy∼Nμx∣yπx∣y−1 then calculated πx∣y=πx+πy mean μx∣y=μx+πyπx∣yy−μx [i.e., = + rate) × error] Figure text).On level, weighting thought neuromodulatory mechanisms example, acetylcholine tuning synaptic gain consequently, adjust rate. Updating called when occurs short timescales single observation), parameter updates trials). For perception, rate should highest if low situations) originates precise seeing object very clearly). lowest knowing one's living room) unreliable dark). distinct 'active' inference:First, sample features. Early eye-tracking studies had already showed participants images differently depending [112.Yarbus A.L. Eye Movements Vision. Springer, 1967Crossref famous basketball gorilla experiment [113.Simons D.J. Chabris C.F. Gorillas midst: inattentional blindness events.Perception. 28: 1059-1074Crossref focused leads attentional highly unusual events (low priori probability context). Even though useful they price introducing well-known biases, illusions exploit (seeing you seeing), confirmation bias want knowledge), overgeneralizations (not there). Our dependence why false beliefs about feel were real [114.Dijkstra Fleming S.M. Subjective strength distinguishes imagination.Nat. Commun. 2023; 14: 1627Crossref (7) implications biased disorders text).Second, regulation. high, parameters simply updated match case epistemically coding. These flexible, preference correspond metabolic, allostatic, autopoietic needs organism, body state, preferable being safe space vs. exposed predation). depend objective (how is), subjective preferences be)Box 3Emotions: descriptive naturalisticThe concept construction due success. perspective provides alternative action-/goal-/outcome-oriented embodied linked especially Scholar]). enables us reformulate affectivity.Salience results relevance, mediated weighting. Valence estimating current expected Formally, divergence proportional inspire testable, [103.Hesp C. al.Deeply felt affect: emergence deep inference.Neural 33: 398-446Crossref (71) been suggested (expected) increase disappointment, unhappiness, decrease relief, hope, happiness Altogether, emotions, reward, emergent consequences principles. Ideally, seeks out experiences predicted avoids those result feeling too cold hot, Consequently, (subjectively) rewarding preferably detected [115.Banerjee al.Value-guided remapping orbitofrontal cortex.Nature. 585: 245-250Crossref (70) Scholar,116.Schaffner al.Sensory relies fitness-maximizing codes.Nat. Hum. Behav. 7: 1135-1151Crossref automatic harm elicited extremely simple [117.De Franceschi al.Vision guides freeze flight defense strategies mice.Curr. 26: 2150-2154Abstract (156) preferred states, shifts focus function. There genetically defined modality population (Figure 3 [69.Kim behaviors.Neuron. 93: 1464-1479Abstract (256) Scholar]), encoding currently unclear.Thevalue drives [118.Sharpe M.J. al.Dopamine transients acquisition model-based associations.Nat. 20: 735-742Crossref reward-sensitive [89.Warlow recruits mesocorticolimbic pursuit pain.Nat. 11: 2716Crossref (31) pleasure. Instead, rodents engage optogenetic self-stimulation [100.Warlow Berridge Incentive motivation: 'wanting' roles circuitry.Behav. 411113376Crossref (29) require concurrent supports links inference.Arousal via neuromodulation, controlled slight hunger critical compared discomfort pain, death). implement asymmetric p

Language: Английский

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Dynamics of specialization in neural modules under resource constraints

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 2, 2025

Language: Английский

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Predictive Coding algorithms induce brain-like responses in Artificial Neural Networks

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

Abstract This study explores whether predictive coding (PC) inspired Deep Neural Networks can serve as biologically plausible neural network models of the brain. We compared two PC-inspired training objectives, a and contrastive approach, to supervised baseline in simple Recurrent Network (RNN) architecture. evaluated on key signatures PC, including mismatch responses, formation priors, learning semantic information. Our results show that models, especially locally trained model, exhibited these PC-like behaviors better than Supervised or an Untrained RNN. Further, we found activity regularization evokes response-like effects across all suggesting it may proxy for energy-saving principles PC. Finally, find Gain Control (an important mechanism PC framework) be implemented using weight regularization. Overall, our findings indicate are able capture computational processing brain, promising foundation building artificial networks. work contributes understanding relationship between biological networks, highlights potential algorithms advancing brain modelling well brain-inspired machine learning.

Language: Английский

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Coordinating multiple mental faculties during learning

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 13, 2025

Abstract Complex behavior is supported by the coordination of multiple brain regions. How do regions coordinate absent a homunculus? We propose achieved controller-peripheral architecture in which peripherals (e.g., ventral visual stream) aim to supply needed inputs their controllers hippocampus and prefrontal cortex) while expending minimal resources. developed formal model within this framework address how support rapid learning from few example images. The captured higher-level activity controller shaped lower-level representations, affecting precision sparsity manner that paralleled measures. In particular, peripheral encoded information extent smooth operation controller. Alternative models optimized gradient descent irrespective architectural constraints could not account for human or responses, and, typical standard deep approaches, were unstable trial-by-trial learners. While previous work offered accounts specific faculties, such as perception, attention, learning, approach step toward addressing next generation questions concerning faculties coordinate.

Language: Английский

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