Active inference on discrete state-spaces: A synthesis

Costa, Lancelot Da; Parr, Thomas; Sajid, Noor; Veselič, Sebastijan; Neacsu, Victorita; Friston, Karl J.

doi:10.1016/j.jmp.2020.102447

Cited by 200 publications

(227 citation statements)

References 145 publications

(186 reference statements)

Supporting

Mentioning

225

Contrasting

Unclassified

Order By: Relevance

“…If the subject repeats the word correctly, they are given a positive evaluation (and negative otherwise). The word repetition task was stimulated using a (Markov decision process) generative model of discrete outcomes ( Friston et al , 2017 a ; Sajid et al , 2019 ; Da Costa et al , 2020 ), previously introduced in ( Sajid et al , 2020 ). The model considers a left-lateralized neuronal circuitry involved in word repetition, but this can be extended—via additional state factors or hierarchies—to include the right hemisphere.…”

Section: Methodsmentioning

confidence: 99%

Paradoxical lesions, plasticity and active inference

Sajid

Parr

Gajardo-Vidal

et al. 2020

Brain Communications

Self Cite

View full text Add to dashboard Cite

Paradoxical lesions are secondary brain lesions that ameliorate functional deficits caused by the initial insult. This effect has been explained in several ways; particularly by the reduction of functional inhibition, or by increases in the excitatory-to-inhibitory synaptic balance within perilesional tissue. In this article, we simulate how and when a modification of the excitatory-inhibitory balance triggers the reversal of a functional deficit caused by a primary lesion. For this, we introduce in-silico lesions to an active inference model of auditory word repetition. The first in-silico lesion simulated damage to the extrinsic (between region) connectivity causing a functional deficit that did not fully resolve over 100 trials of a word repetition task. The second lesion was implemented in the intrinsic (within region) connectivity, compromising the model’s ability to rebalance excitatory-inhibitory connections during learning. We found that when the second lesion was mild, there was an increase in experience-dependent plasticity that enhanced performance relative to a single lesion. This paradoxical lesion effect disappeared when the second lesion was more severe because plasticity-related changes were disproportionately amplified in the intrinsic connectivity, relative to lesioned extrinsic connections. Finally, this framework was used to predict the physiological correlates of paradoxical lesions. This formal approach provides new insights into the computational and neurophysiological mechanisms that allow some patients to recover after large or multiple lesions.

show abstract

Section: Methodsmentioning

confidence: 99%

Paradoxical lesions, plasticity and active inference

Sajid

Parr

Gajardo-Vidal

et al. 2020

Brain Communications

Self Cite

View full text Add to dashboard Cite

show abstract

“…Definitions of the constructs in this paper can be found in Table 1 of Friston et al (2016) , Table 2 of Da Costa et al (2020) , and in the Supplementary Information of Hesp et al (2019) . A conceptual description of the technical notions employed in this paper can be found in Box 2 of Veissière et al (2020) – reproduced here for convenience.…”

Section: Representational Pathways In Active Inferencementioning

confidence: 99%

Representation Wars: Enacting an Armistice Through Active Inference

2021

View full text Add to dashboard Cite

Over the last 30 years, representationalist and dynamicist positions in the philosophy of cognitive science have argued over whether neurocognitive processes should be viewed as representational or not. Major scientific and technological developments over the years have furnished both parties with ever more sophisticated conceptual weaponry. In recent years, an enactive generalization of predictive processing – known as active inference – has been proposed as a unifying theory of brain functions. Since then, active inference has fueled both representationalist and dynamicist campaigns. However, we believe that when diving into the formal details of active inference, one should be able to find a solution to the war; if not a peace treaty, surely an armistice of a sort. Based on an analysis of these formal details, this paper shows how both representationalist and dynamicist sensibilities can peacefully coexist within the new territory of active inference.

show abstract

“…Furthermore, we have just shown neuronal populations encoding beliefs under two possible policies over three time points. For an introduction to the associated mathematics describing neuronal interactions in this theory, see Friston et al ., 2, 16 and Da Costa et al 17 …”

Section: Figmentioning

confidence: 99%

“…In decision‐making and action selection, a related class of biologically plausible algorithms comes from the ‘active inference’ framework 2,16,17,19 . Active inference models go a step beyond predictive coding to emphasize that the brain does not simply predict sensory input passively.…”

Section: Figmentioning

confidence: 99%

Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Smith

Badcock

Friston

2020

Psychiatry Clin Neurosci

121

View full text Add to dashboard Cite

Research in clinical neuroscience is founded on the idea that a better understanding of brain (dys)function will improve our ability to diagnose and treat neurological and psychiatric disorders. In recent years, neuroscience has converged on the notion that the brain is a 'prediction machine,' in that it actively predicts the sensory input that it will receive if one or another course of action is chosen. These predictions are used to select actions that will (most often, and in the long run) maintain the body within the narrow range of physiological states consistent with survival. This insight has given rise to an area of clinical computational neuroscience research that focuses on characterizing neural circuit architectures that can accomplish these predictive functions, and on how the associated processes may break down or become aberrant within clinical conditions. Here, we provide a brief review of examples of recent work on the application of predictive processing models of brain function to study clinical (psychiatric) disorders, with the aim of highlighting current directions and their potential clinical utility. We offer examples of recent conceptual models, formal mathematical models, and applications of such models in empirical research in clinical populations, with a focus on making this material accessible to clinicians without expertise in computational neuroscience. In doing so, we aim to highlight the potential insights and opportunities that understanding the brain as a prediction machine may offer to clinical research and practice.

show abstract

Active inference on discrete state-spaces: A synthesis

Cited by 200 publications

References 145 publications

Paradoxical lesions, plasticity and active inference

Paradoxical lesions, plasticity and active inference

Representation Wars: Enacting an Armistice Through Active Inference

Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Contact Info

Product

Resources

About