Active inference is a general framework for perception and action that is gaining prominence in computational and systems neuroscience but is less known outside these fields. Here, we discuss a proof-of-principle implementation of the active inference scheme for the control or the 7-DoF arm of a (simulated) PR2 robot. By manipulating visual and proprioceptive noise levels, we show under which conditions robot control under the active inference scheme is accurate. Besides accurate control, our analysis of the internal system dynamics (e.g. the dynamics of the hidden states that are inferred during the inference) sheds light on key aspects of the framework such as the quintessentially multimodal nature of control and the differential roles of proprioception and vision. In the discussion, we consider the potential importance of being able to implement active inference in robots. In particular, we briefly review the opportunities for modelling psychophysiological phenomena such as sensory attenuation and related failures of gain control, of the sort seen in Parkinson's disease. We also consider the fundamental difference between active inference and optimal control formulations, showing that in the former the heavy lifting shifts from solving a dynamical inverse problem to creating deep forward or generative models with dynamics, whose attracting sets prescribe desired behaviours.

Balancing habitual and deliberate forms of choice entails a comparison of their respective merits-the former being faster but inflexible, and the latter slower but more versatile. Here, we show that arbitration between these two forms of control can be derived from first principles within an Active Inference scheme. We illustrate our arguments with simulations that reproduce rodent spatial decisions in T-mazes. In this context, deliberation has been associated with vicarious trial and error (VTE) behavior (i.e., the fact that rodents sometimes stop at decision points as if deliberating between choice alternatives), whose neurophysiological correlates are "forward sweeps" of hippocampal place cells in the arms of the maze under consideration. Crucially, forward sweeps arise early in learning and disappear shortly after, marking a transition from deliberative to habitual choice. Our simulations show that this transition emerges as the optimal solution to the trade-off between policies that maximize reward or extrinsic value (habitual policies) and those that also consider the epistemic value of exploratory behavior (deliberative or epistemic policies)-the latter requiring VTE and the retrieval of episodic information via forward sweeps. We thus offer a novel perspective on the optimality principles that engender forward sweeps and VTE, and on their role on deliberate choice.Substantial evidence indicates that animal behavior is determined both by deliberative processes (i.e., based on predictions of future outcomes and rewards) and by habitual reflexes (i.e., based on stimulus-response associations; Balleine and Dickinson 1998). The former are more resource intensive and sensitive to changes in task contingencies, while the latter are cheaper but inflexible; hence whether it is optimal to call on deliberative or habitual choice depends on the trade-off between the advantage of flexibility and computational costs (Balleine and Dickinson 1998;Dolan and Dayan 2013;Lee et al. 2014). In this paper, we try to understand the contextualization of behavior and the trade-off between deliberative and habitual choice from first principles, using Active Inference and Markov decision process models of exploitation and exploration (Friston et al. 2013(Friston et al. , 2014Pezzulo et al. 2015).We focus specifically on vicarious trial and error (VTE) behavior, which is considered a hallmark of deliberation (Muenzinger 1938;Tolman 1938Tolman , 1939. This is based on the observation that, when rodents have to remember or search the correct route to a reward in a maze (e.g., a T-maze), they sometimes stop at choice points, to look left and right before choosing which direction to go. This has been interpreted as a signature of cognitive search and deliberation between the two choices (i.e., going right or left). In keeping with a role of VTE behavior for deliberation, it occurs early in learning and decreases or disappears after significant experience (Tolman 1939;van der Meer and Redish 2010;van der Meer et al. 2012) but it can incr...

Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE.

Active inference is a leading theory in neuroscience that provides a simple and neuro-biologically plausible account of how action and perception are coupled in producing (Bayes) optimal behavior; and has been recently used to explain a variety of psychopathological conditions. In parallel, morphogenesis has been described as the behavior of a (non-neural) cellular collective intelligence solving problems in anatomical morphospace. In this article, we establish a link between the domains of cell biology and neuroscience, by analyzing disorders of morphogenesis as disorders of (active) inference. The aim of this article is three-fold. We want to: (i) reveal a connection between disorders of morphogenesis and disorders of active inference as apparent in psychopathological conditions; (ii) show how disorders of morphogenesis can be simulated using active inference; (iii) suggest that active inference can shed light on developmental defects or aberrant morphogenetic processes, seen as disorders of information processing, and perhaps suggesting novel intervention and repair strategies. We present four simulations illustrating application of these ideas to cellular behavior during morphogenesis. Three of the simulations show that the same forms of aberrant active inference (e.g., deficits of sensory attenuation and low sensory precision) that have been used to explain psychopathological conditions (e.g., schizophrenia and autism) also produce familiar disorders of development and morphogenesis when implemented at the level of the collective behavior of a group of cells. The fourth simulation involves two cells with too high precision, in which we show that the reduction of concentration signaling and sensitivity to the signals of other cells treats the development defect. Finally, we present the results of an experimental test of one of the model's predictions in early Xenopus laevis embryos: thioridazine (a dopamine antagonist that may reduce sensory precision in biological systems) induced developmental (anatomical) defects as predicted. The use of conceptual and empirical tools from neuroscience to understand the morphogenetic behavior of pre-neural agents offers the possibility of new approaches in regenerative medicine and evolutionary developmental biology.