Active inference and robot control: a case study.

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

Interindividual variability in functional connectivity as long-term correlate of temporal discounting

During intertemporal choice (IT) future outcomes are usually devaluated as a function of the delay, a phenomenon known as temporal discounting (TD). Based on task-evoked activity, previous neuroimaging studies have described several networks associated with TD. However, given its relevance for several disorders, a critical challenge is to define a specific neural marker able to predict TD independently of task execution. To this aim, we used restingstate functional connectivity MRI (fcMRI) and measured TD during economic choices several months apart in 25 human subjects.We further explored the relationship between TD, impulsivity and decision uncertainty by collecting standard questionnaires on individual trait/ state differences. Our findings indicate that fcMRI within and between critical nodes of taskevoked neural networks associated with TD correlates with discounting behavior measured a long time afterwards, independently of impulsivity. Importantly, the nodes form an intrinsic circuit that might support all the mechanisms underlying TD, from the representation of subjective value to choice selection through modulatory effects of cognitive control and episodic prospection

Words as social tools: flexibility, situatedness, language and sociality in abstract concepts. Reply to comments on “Words as social tools: language, sociality and inner grounding in abstract concepts”

The article is a reply to comments by other authors on the topic of abstract concepts

A framework to identify structured behavioral patterns within rodent spatial trajectories

Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments

Elevated Paracellular Glucose Flux across Cystic Fibrosis Airway Epithelial Monolayers Is an Important Factor for Pseudomonas aeruginosa Growth.

People with cystic fibrosis (CF) who develop related diabetes (CFRD) have accelerated pulmonary decline, increased infection with antibiotic-resistant Pseudomonas aeruginosa and increased pulmonary exacerbations. We have previously shown that glucose concentrations are elevated in airway surface liquid (ASL) of people with CF, particularly in those with CFRD. We therefore explored the hypotheses that glucose homeostasis is altered in CF airway epithelia and that elevation of glucose flux into ASL drives increased bacterial growth, with an effect over and above other cystic fibrosis transmembrane conductance regulator (CFTR)-related ASL abnormalities. The aim of this study was to compare the mechanisms governing airway glucose homeostasis in CF and non-CF primary human bronchial epithelial (HBE) monolayers, under normal conditions and in the presence of Ps. aeruginosa filtrate. HBE-bacterial co-cultures were performed in the presence of 5 mM or 15 mM basolateral glucose to investigate how changes in blood glucose, such as those seen in CFRD, affects luminal Ps. aeruginosa growth. Calu-3 cell monolayers were used to evaluate the potential importance of glucose on Ps. aeruginosa growth, in comparison to other hallmarks of the CF ASL, namely mucus hyperviscosity and impaired CFTR-dependent fluid secretions. We show that elevation of basolateral glucose promotes the apical growth of Ps. aeruginosa on CF airway epithelial monolayers more than non-CF monolayers. Ps. aeruginosa secretions elicited more glucose flux across CF airway epithelial monolayers compared to non-CF monolayers which we propose increases glucose availability in ASL for bacterial growth. In addition, elevating basolateral glucose increased Ps. aeruginosa growth over and above any CFTR-dependent effects and the presence or absence of mucus in Calu-3 airway epithelia-bacteria co-cultures. Together these studies highlight the importance of glucose as an additional factor in promoting Ps. aeruginosa growth and respiratory infection in CF disease

Hippocampal place cells encode global location but not connectivity in a complex space

Flexible navigation relies on a cognitive map of space, thought to be implemented by hippocampal place cells: neurons that exhibit location-specific firing. In connected environments, optimal navigation requires keeping track of one’s location and of the available connections between subspaces. We examined whether the dorsal CA1 place cells of rats encode environmental connectivity in four geometrically identical boxes arranged in a square. Rats moved between boxes by pushing saloon-type doors that could be locked in one or both directions. Although rats demonstrated knowledge of environmental connectivity, their place cells did not respond to connectivity changes, nor did they represent doorways differently from other locations. Place cells coded location in a global reference frame, with a different map for each box and minimal repetitive fields despite the repetitive geometry. These results suggest that CA1 place cells provide a spatial map that does not explicitly include connectivity

Irreparable Massive Rotator Cuff Tears: Subacromial Balloon Surgical Technique

Massive irreparable rotator cuff tears pose a significant challenge for both the treating orthopedic surgeon and patient. Surgical treatment options for massive rotator cuff tears include arthroscopic debridement, biceps tenotomy or tenodesis, arthroscopic rotator cuff repair, partial rotator cuff repair, cuff augmentation, tendon transfers, superior capsular reconstruction, subacromial balloon spacer, and ultimately reverse shoulder arthroplasty. The present study will provide a brief overview of these treatment options along with a description of the surgical technique for subacromial balloon spacer placement

Metformin reduces airway glucose permeability and hyperglycaemia-induced Staphylococcus aureus load independently of effects on blood glucose

Background Diabetes is a risk factor for respiratory infection, and hyperglycaemia is associated with increased glucose in airway surface liquid and risk of Staphylococcus aureus infection. Objectives To investigate whether elevation of basolateral/blood glucose concentration promotes airway Staphylococcus aureus growth and whether pretreatment with the antidiabetic drug metformin affects this relationship. Methods Human airway epithelial cells grown at air–liquid interface (±18 h pre-treatment, 30 μM–1 mM metformin) were inoculated with 5×105 colony-forming units (CFU)/cm2 S aureus 8325-4 or JE2 or Pseudomonas aeruginosa PA01 on the apical surface and incubated for 7 h. Wild-type C57BL/6 or db/db (leptin receptor-deficient) mice, 6–10 weeks old, were treated with intraperitoneal phosphate-buffered saline or 40 mg/kg metformin for 2 days before intranasal inoculation with 1×107 CFU S aureus. Mice were culled 24 h after infection and bronchoalveolar lavage fluid collected. Results Apical S aureus growth increased with basolateral glucose concentration in an in vitro airway epithelia–bacteria co-culture model. S aureus reduced transepithelial electrical resistance (RT) and increased paracellular glucose flux. Metformin inhibited the glucose-induced growth of S aureus, increased RT and decreased glucose flux. Diabetic (db/db) mice infected with S aureus exhibited a higher bacterial load in their airways than control mice after 2 days and metformin treatment reversed this effect. Metformin did not decrease blood glucose but reduced paracellular flux across ex vivo murine tracheas. Conclusions Hyperglycaemia promotes respiratory S aureus infection, and metformin modifies glucose flux across the airway epithelium to limit hyperglycaemia-induced bacterial growth. Metformin might, therefore, be of additional benefit in the prevention and treatment of respiratory infection

Evidence for surprise minimization over value maximization in choice behavior

Classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents' to model their environment and minimize surprise about the states they frequent. We propose that choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone. Minimizing surprise makes a prediction at variance with expected utility models; namely, that in addition to attaining valuable states, agents attempt to maximize the entropy over outcomes and thus 'keep their options open'. We tested this prediction using a simple binary choice paradigm and show that human decision-making is better explained by surprise minimization compared to utility maximization. Furthermore, we replicated this entropy-seeking behavior in a control task with no explicit utilities. These findings highlight a limitation of purely economic motivations in explaining choice behavior and instead emphasize the importance of belief-based motivations