Depth and hierarchies in the predictive brain: From reaction to action DOI
Otto Muzik, Vaibhav A. Diwadkar

Wiley Interdisciplinary Reviews Cognitive Science, Journal Year: 2023, Volume and Issue: 14(6)

Published: July 30, 2023

The human brain is a prediction device, view widely accepted in neuroscience. Prediction rational and efficient response that relies on the brain's ability to create employ generative models optimize actions over unpredictable time horizons. We argue extant predictive frameworks while compelling, have not explicitly accounted for following: (a) must incorporate depth (i.e., rely degrees of abstraction enable predictions different horizons); (b) implementation scheme account varying dynamic hierarchies formed using functional networks. show these ascending processes (driven by reaction), descending (related prediction), eventually driving action. Because they are dynamically formed, allow address challenges virtually any domain. By way application, we explain how this framework can be applied heretofore poorly understood behavioral thermoregulation. Although mammalian thermoregulation has been closely tied deep structures engaged autonomic control such as hypothalamus, narrow conception does translate well humans. In addition profound differences evolutionary history, bestowed with substantially increased complexity (that itself emerged from differences). humans possible because, signals shaped homeostatic sub-networks, interject related (implemented interoceptive executive sub-networks) action sub-networks). These sub-networks cumulatively form hierarchy thermoregulation, potentiating range viable responses known unknown thermoregulatory challenges. suggest our proposed extensions provide set generalizable principles further illuminate many facets brain. This article categorized under: Neuroscience > Behavior Philosophy Action Psychology Prediction.

Language: Английский

A step-by-step tutorial on active inference and its application to empirical data DOI Creative Commons
Ryan Smith, Karl Friston, Christopher J. Whyte

et al.

Journal of Mathematical Psychology, Journal Year: 2022, Volume and Issue: 107, P. 102632 - 102632

Published: Feb. 4, 2022

The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity years useful approach for modeling neurocognitive processes. This framework is highly general flexible ability to be customized model any cognitive process, well simulate predicted neuronal responses based on accompanying neural theory. It also affords both simulation experiments proof of principle behavioral empirical studies. However, there are limited resources that explain how build run these models practice, which limits their widespread use. Most introductions assume technical background programming, mathematics, machine learning. In this paper we offer step-by-step tutorial POMDPs, simulations using standard MATLAB routines, fit data. We minimal programming thoroughly all equations, provide exemplar scripts can theoretical Our goal the reader with requisite knowledge practical tools apply own research. optional sections multiple appendices, interested additional details. should necessary use follow emerging advances

Language: Английский

Citations

162
The free energy principle made simpler but not too simple DOI Creative Commons
Karl Friston, Lancelot Da Costa, Noor Sajid

et al.

Physics Reports, Journal Year: 2023, Volume and Issue: 1024, P. 1 - 29

Published: June 1, 2023

This paper provides a concise description of the free energy principle, starting from formulation random dynamical systems in terms Langevin equation and ending with Bayesian mechanics that can be read as physics sentience. It rehearses key steps using standard results statistical physics. These entail (i) establishing particular partition states based upon conditional independencies inherit sparsely coupled dynamics, (ii) unpacking implications this inference (iii) describing paths variational principle least action. Teleologically, offers normative account self-organisation optimal design decision-making, sense maximising marginal likelihood or model evidence. In summary, world systems, we end up sentient behaviour interpreted self-evidencing; namely, self-assembly, autopoiesis active inference.

Language: Английский

Citations

86
Computational psychiatry: from synapses to sentience DOI Creative Commons
Karl Friston

Molecular Psychiatry, Journal Year: 2022, Volume and Issue: 28(1), P. 256 - 268

Published: Sept. 2, 2022

Abstract This review considers computational psychiatry from a particular viewpoint: namely, commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion generative model as underwriting (i) sentient processing brain, and (ii) scientific process psychiatry. The story starts with view brain—from cognitive neuroscience—as an organ inference prediction. offers formal description neuronal message passing, distributed belief propagation networks; how certain kinds dysconnection lead aberrant updating false inference. dysconnections question can be read pernicious synaptopathy that fits comfortably notions we—or our brains—encode uncertainty or its complement, precision . then ensuing theories are tested empirically, emphasis modelling circuits synaptic gain control mediates attentional set, active inference, learning planning. opportunities afforded by this sort considered light silico experiments; neuropsychology, phenotyping promises nosology for resulting survey approaches is not scholarly exhaustive. Rather, aim theoretical narrative emerging across subdisciplines within empirical scales investigation. These range epilepsy research neurodegenerative disorders; post-traumatic stress disorder management chronic pain, schizophrenia functional medical symptoms.

Language: Английский

Citations

81
Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception DOI Creative Commons
Achim Schilling, William Sedley, Richard Gerum

et al.

Brain, Journal Year: 2023, Volume and Issue: 146(12), P. 4809 - 4825

Published: July 27, 2023

Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy lesion studies, phantom perception may serve as a vehicle understand the fundamental processing principles underlying healthy auditory perception. With special focus on tinnitus-as prime example of perception-we review recent work at intersection artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not loss tinnitus. We argue that intrinsic neural noise generated amplified along pathway compensatory mechanism restore normal based adaptive stochastic resonance. The increase can then be misinterpreted input perceived This formalized Bayesian brain framework, where percept (posterior) assimilates prior prediction (brain's expectations) likelihood (bottom-up signal). A higher mean lower variance (i.e. enhanced precision) shifts posterior, evincing misinterpretation sensory evidence, which further confounded by plastic changes underwrite predictions. Hence, two provide most explanatory power for emergence perceptions: predictive coding top-down resonance complementary bottom-up mechanism. conclude both also play crucial role Finally, context neuroscience-inspired improve contemporary machine learning techniques.

Language: Английский

Citations

46
How particular is the physics of the free energy principle? DOI Creative Commons
Miguel Aguilera, Beren Millidge, Alexander Tschantz

et al.

Physics of Life Reviews, Journal Year: 2021, Volume and Issue: 40, P. 24 - 50

Published: Nov. 23, 2021

The free energy principle (FEP) states that any dynamical system can be interpreted as performing Bayesian inference upon its surrounding environment. Although, in theory, the FEP applies to a wide variety of systems, there has been almost no direct exploration or demonstration concrete systems. In this work, we examine depth assumptions required derive simplest possible set systems – weakly-coupled non-equilibrium linear stochastic Specifically, explore (i) how general requirements imposed on statistical structure are and (ii) informative is about behaviour such We discover two Markov blanket condition (i.e. boundary precluding coupling between internal external states) stringent restrictions solenoidal flows tendencies driving out equilibrium) only valid for very narrow space parameters. Suitable require an absence perception-action asymmetries highly unusual living interacting with More importantly, observe mathematically central step argument, connecting variational inference, relies implicit equivalence dynamics average those states. This does not hold even since it requires effective decoupling from system's history interactions. These observations critical evaluating generality applicability indicate existence significant problems theory current form. issues make FEP, stands, straightforwardly applicable simple studied here suggest more development needed before could applied kind complex describe cognitive processes.

Language: Английский

Citations

71
Predictive coding is a consequence of energy efficiency in recurrent neural networks DOI
Abdullahi Ali, Nasir Ahmad,

E. de Groot

et al.

Patterns, Journal Year: 2022, Volume and Issue: 3(12), P. 100639 - 100639

Published: Nov. 23, 2022

Language: Английский

Citations

43
The empirical status of predictive coding and active inference DOI
Rowan Hodson, Marishka Mehta, Ryan Smith

et al.

Neuroscience & Biobehavioral Reviews, Journal Year: 2023, Volume and Issue: 157, P. 105473 - 105473

Published: Nov. 28, 2023

Language: Английский

Citations

30
Natural language syntax complies with the free-energy principle DOI Creative Commons
Elliot Murphy, Emma Holmes, Karl Friston

et al.

Synthese, Journal Year: 2024, Volume and Issue: 203(5)

Published: May 3, 2024

Abstract Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service active inference accord with free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation linguistic communication FEP, we extend this program underlying computations responsible for generating syntactic objects. argue recently proposed principles economy design—such as “minimal search” criteria from theoretical syntax—adhere FEP. This affords a greater degree explanatory power FEP—with respect higher functions—and offers linguistics grounding first computability. mostly focus on building new principled relations between also show through sample preliminary examples how both tree-geometric depth Kolmogorov complexity estimate (recruiting Lempel–Ziv compression algorithm) can be accurately predict legal operations workspaces, directly line formulations variational free energy minimization. is motivate general design term Turing–Chomsky Compression (TCC). use TCC align concerns linguists normative account self-organization furnished by marshalling evidence psycholinguistics ground core efficient computation within inference.

Language: Английский

Citations

8
A Step-by-Step Tutorial on Active Inference and its Application to Empirical Data DOI Open Access
Ryan Smith, Karl Friston, Christopher J. Whyte

et al.

Published: Jan. 2, 2021

The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity years useful approach for modelling neurocognitive processes. This framework is highly general flexible ability to be customized model any cognitive process, well simulate predicted neuronal responses based on accompanying neural theory. It also affords both simulation experiments proof of principle behavioral empirical studies. However, there are limited resources that explain how build run these models practice, which limits their widespread use. Most introductions assume technical background programming, mathematics, machine learning. In this paper we offer step-by-step tutorial POMDPs, simulations using standard MATLAB routines, fit data. We minimal programming thoroughly all equations, provide exemplar scripts can theoretical Our goal the reader with requisite knowledge practical tools apply own research. optional sections several appendices, interested additional details. should necessary use follow emerging advances

Language: Английский

Citations

37
Supervised structure learning DOI Creative Commons
Karl Friston, Lancelot Da Costa, Alexander Tschantz

et al.

Biological Psychology, Journal Year: 2024, Volume and Issue: 193, P. 108891 - 108891

Published: Oct. 19, 2024

Language: Английский

Citations

6