UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Active inference on discrete state-spaces: A synthesis

Da Costa, L; Parr, T; Sajid, N; Veselic, S; Neacsu, V; Friston, K; (2020) Active inference on discrete state-spaces: A synthesis. Journal of Mathematical Psychology , 99 , Article 102447. 10.1016/j.jmp.2020.102447. (In press). Green open access

[img]
Preview
Text
1-s2.0-S0022249620300857-main.pdf - Published version

Download (2MB) | Preview

Abstract

Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents. From its inception, its associated process theory has grown to incorporate complex generative models, enabling simulation of a wide range of complex behaviours. Due to successive developments in active inference, it is often difficult to see how its underlying principle relates to process theories and practical implementation. In this paper, we try to bridge this gap by providing a complete mathematical synthesis of active inference on discrete state-space models. This technical summary provides an overview of the theory, derives neuronal dynamics from first principles and relates this dynamics to biological processes. Furthermore, this paper provides a fundamental building block needed to understand active inference for mixed generative models; allowing continuous sensations to inform discrete representations. This paper may be used as follows: to guide research towards outstanding challenges, a practical guide on how to implement active inference to simulate experimental behaviour, or a pointer towards various in-silico neurophysiological responses that may be used to make empirical predictions.

Type: Article
Title: Active inference on discrete state-spaces: A synthesis
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jmp.2020.102447
Publisher version: http://dx.doi.org/10.1016/j.jmp.2020.102447
Language: English
Additional information: © 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Keywords: Active inference, Free energy principle, Process theory, Variational Bayesian inference, Markov decision process, Mathematical review
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10115398
Downloads since deposit
7Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item