Modularity, segregation, and interactions

This commentary considers how far one can go in making inferences about functional modularity or segregation, based on the sorts of analyses used by Caplan & Waters in relation to the underlying neuronal infrastructure. Specifically an attempt is made to relate the "functionalist" approach adopted in the target article to "neuroreductionist" perspectives on the same issue

Hallucinations and perceptual inference

This commentary takes a closer look at how constructive models of subjective perception," referred to by Collerton et al. (sect. 2), might contribute to the Perception and Attention Deficit (PAD) model. It focuses oil the neuronal mechanisms that could mediate hallucinations, or false inference - in particular, the role of cholinergic systems in encoding uncertainty in the context of hierarchical Bayesian models of perceptual inference Friston 20021); Yu & Dayan 2002)

On the modelling of seizure dynamics.

This scientific commentary refers to ‘On the nature of seizure dynamics’, by V. Jirsa et al. (doi:10.1093/brain/awu133)

Waves of prediction

Predictive processing (e.g., predictive coding) is a predominant paradigm in cognitive neuroscience. This Primer considers the various levels of commitment neuroscientists have to the neuronal process theories that accompany the principles of predictive processing. Specifically, it reviews and contextualises a recent PLOS Biology study of alpha oscillations and travelling waves. We will see that alpha oscillations emerge naturally under the computational architectures implied by predictive coding-and may tell us something profound about recurrent message passing in brain hierarchies. Specifically, the bidirectional nature of forward and backward waves speaks to opportunities to understand attention and how it nuances bottom-up and top-down influences

Deep Active Inference for Partially Observable MDPs

Deep active inference has been proposed as a scalable approach to perception and action that deals with large policy and state spaces. However, current models are limited to fully observable domains. In this paper, we describe a deep active inference model that can learn successful policies directly from high-dimensional sensory inputs. The deep learning architecture optimizes a variant of the expected free energy and encodes the continuous state representation by means of a variational autoencoder. We show, in the OpenAI benchmark, that our approach has comparable or better performance than deep Q-learning, a state-of-the-art deep reinforcement learning algorithm.Comment: 1st International Workshop on Active inference, European Conference on Machine Learning (ECML/PCKDD 2020

Active inference and oculomotor pursuit: the dynamic causal modelling of eye movements.

This paper introduces a new paradigm that allows one to quantify the Bayesian beliefs evidenced by subjects during oculomotor pursuit. Subjects' eye tracking responses to a partially occluded sinusoidal target were recorded non-invasively and averaged. These response averages were then analysed using dynamic causal modelling (DCM). In DCM, observed responses are modelled using biologically plausible generative or forward models - usually biophysical models of neuronal activity

Active inference and the anatomy of oculomotion

Given that eye movement control can be framed as an inferential process, how are the requisite forces generated to produce anticipated or desired fixation? Starting from a generative model based on simple Newtonian equations of motion, we derive a variational solution to this problem and illustrate the plausibility of its implementation in the oculomotor brainstem. We show, through simulation, that the Bayesian filtering equations that implement ‘planning as inference’ can generate both saccadic and smooth pursuit eye movements. Crucially, the associated message passing maps well onto the known connectivity and neuroanatomy of the brainstem – and the changes in these messages over time are strikingly similar to single unit recordings of neurons in the corresponding nuclei. Furthermore, we show that simulated lesions to axonal pathways reproduce eye movement patterns of neurological patients with damage to these tracts

The Computational Anatomy of Visual Neglect

Visual neglect is a debilitating neuropsychological phenomenon that has many clinical implications and—in cognitive neuroscience—offers an important lesion deficit model. In this article, we describe a computational model of visual neglect based upon active inference. Our objective is to establish a computational and neurophysiological process theory that can be used to disambiguate among the various causes of this important syndrome; namely, a computational neuropsychology of visual neglect. We introduce a Bayes optimal model based upon Markov decision processes that reproduces the visual searches induced by the line cancellation task (used to characterize visual neglect at the bedside). We then consider 3 distinct ways in which the model could be lesioned to reproduce neuropsychological (visual search) deficits. Crucially, these 3 levels of pathology map nicely onto the neuroanatomy of saccadic eye movements and the systems implicated in visual neglect

Analysis of family-wise error rates in statistical parametric mapping using random field theory.

This technical report revisits the analysis of family-wise error rates in statistical parametric mapping-using random field theory-reported in (Eklund et al. []: arXiv 1511.01863). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of parametric assumptions-and random field theory-in the analysis of functional neuroimaging data. We briefly rehearse the advantages parametric analyses offer over nonparametric alternatives and then unpack the implications of (Eklund et al. []: arXiv 1511.01863) for parametric procedures. Hum Brain Mapp, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc