Searcharxiv⌕ Search

arXiv · 2610.27235

Does a Flux-Defined Markov Blanket Screen Self-Replicating Gray--Scott Spots? A Computational Test of a Blanket Condition from the Free Energy Principle

Abstract

Friston's free energy principle holds that a system separated from its environment by a Markov blanket---a boundary whose states statistically screen its interior from its exterior---can be treated as performing approximate Bayesian inference, and that such blankets arise wherever coupling is short-range. We test this blanket condition in a concrete nonlinear pattern-forming system: self-replicating spots in a reaction-diffusion model that persist, compete, and reproduce. Directed boundary exchange and timescale separation hold up reasonably well, with qualifications. A flux-defined blanket does not screen interior from exterior. Flux removes only part of the linear dependence, and a nonlinear test shows that conditioning on flux \emph{increases} the dependence, consistent with conditioning on a collider, since flux is a joint function of concentrations on both sides of the boundary. The dependence is concentrated within about one organism radius of the boundary. A band of the surrounding field that thick screens the interior from the exterior beyond it, as any short-range field would, which is not evidence of inference.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

John E Pearson. 2026-10-19. Does a Flux-Defined Markov Blanket Screen Self-Replicating Gray--Scott Spots? A Computational Test of a Blanket Condition from the Free Energy Principle. https://arxiv.org/abs/2610.27235

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

On sources to variabilities of simple cells in the primary visual cortex: A principled theory for the interaction between geometric image transformations and receptive field responses

This paper gives an overview of a theory for modelling the interaction between geometric image transformations and receptive field responses for a visual observer that views objects and spatio-temporal events in the environment. This treatment is developed over combinations of (i) uniform spatial scaling transformations, (ii) spatial affine transformations, (iii) Galilean transformations and (iv) temporal scaling transformations. By postulating that the family of receptive fields should be covariant under these classes of geometric image transformations, it follows that the receptive field shapes should be expanded over the degrees of freedom of the corresponding image transformations, to enable a formal matching between the receptive field responses computed under different viewing conditions for the same scene or for a structurally similar spatio-temporal event. We conclude the treatment by discussing and providing partial support for a working hypothesis that the receptive fields of simple cells in the primary visual cortex ought to be covariant under these classes of geometric image transformations, and thus have the shapes of their receptive fields expanded over the degrees of freedom of the corresponding geometric image transformations.

q-bio.NC↗

DRIADA: an open-source Python toolkit for linking single-neuron selectivity and population activity to behavior

Understanding how individual neurons contribute to population activity during behavior is central to understanding brain functions across biological scales. We developed DRIADA, an open-source Python toolkit that connects analyses of individual neurons, population activity, and neuronal networks to the same behavioral parameters. We evaluated it on synthetic populations with known ground truth, calcium-imaging recordings from dorsal hippocampal CA1 in 13 mice across 39 sessions, and a simulated continuous attractor neural network (CANN) with known toroidal organization. Across most hippocampal sessions, embeddings of spatially selective neurons predicted held-out position better than equally sized random subsets and all recorded neurons. The same workflow related population dimensions back to individual neurons, including cells without detected selectivity for the measured behavioral variables. In the simulated network, complementary analyses captured features consistent with its known organization. Thus, DRIADA provides a common framework for relating neuronal selectivity, population structure, and network activity to behavioral dynamics.

q-bio.NC↗

Alignment of visual brain encoding models with individual perceptual similarity judgments

Encoding models that predict brain activation responses to images have traditionally been trained to maximize accuracy when predicting observed brain data. However, their internal representations may not reflect individual human perception. In this work, we investigate whether we can fine-tune brain encoding models to better align their representations with individuals' similarity judgments. Using similarity judgments of 100 images from 8 participants in the Natural Scenes Dataset (NSD), we adapt a generic human similarity judgment model (DreamSim) separately for each participant to obtain personalized perceptual similarity models. These models are then used to obtain subject-specific similarity structure across images in their encoding model training sets. We fine-tune each participant's baseline encoder in two conditions, aligning its representations with either generic or personalized similarity judgments, with the same initialization and objective. We find that baseline brain encoders reflect within-subject perceptual similarity more strongly than measured fMRI responses and preserve intersubject correlations present in measured fMRI, but fail to reflect those of human perceptual similarity judgments. Fine-tuning with generic, population-level perceptual similarity information results in better alignment with human judgments than baseline encoders, but these models still fail to capture intersubject correlations of human judgments. However, fine-tuning with personalized perceptual similarity information results in models that are the most aligned with human perceptual similarity judgments and also reflect intersubject correlations of human judgments. Our framework provides an approach for bootstrapping human similarity judgment data into more individualized, human-aligned brain encoding models of vision.

q-bio.NC↗