GreenMate: A Serious Game Educating Children about Energy Efficiency

Improving the energy efficiency and reducing the CO2 emissions are key strategic objectives for governments on an international level. Although children represent the future energy consumers and efforts have been made to enhance their understanding of energy consumption, there is a lack of initiatives focused on assessing children's awareness regarding energy usage at home and school. Serious games present a highly effective approach to educate and raise children's awareness about energy production and consumption; they are widely used to simulate real-world scenarios, inform or raise players' awareness, and stimulate their problem-solving abilities. Moreover, they assist players in visualising their actions and intuitively exploring different events. This paper discusses the development of a serious game, the GreenMate game, which visually portrays the daily activities and experiences of a primary school student. It incorporates progressive levels to enhance comprehension and knowledge of the diverse factors influencing the growing energy crisis, as well as strategies for managing and mitigating them. According to our experimental findings, the GreenMate game can be used as a productive educational tool to effectively foster desired changes in children's behavior regarding energy consumption

Learning to focus on number.

With age and education, children become increasingly accurate in processing numerosity. This developmental trend is often interpreted as a progressive refinement of the mental representation of number. Here we provide empirical and theoretical support for an alternative possibility, the filtering hypothesis, which proposes that development primarily affects the ability to focus on the relevant dimension of number and to avoid interference from irrelevant but often co-varying quantitative dimensions. Data from the same numerical comparison task in adults and children of various levels of numeracy, including Mundurucú Indians and western dyscalculics, show that, as predicted by the filtering hypothesis, age and education primarily increase the ability to focus on number and filter out potentially interfering information on the non-numerical dimensions. These findings can be captured by a minimal computational model where learning consists in the training of a multivariate classifier whose discrimination boundaries get progressively aligned to the task-relevant dimension of number. This view of development has important consequences for education

Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately

Towards an Accurate Measure of Emotional Pupil Dilation Responses: A Model for Removing the Effect of Luminosity

Pupil dilation is a fundamental marker of emotional response, and indicates emotional arousal independently of stimulus valence. As such, pupil dilation provides invaluable insight into emotional engagement and arousal levels. One of the challenges faced when studying emotion response through pupil diameter is distinguishing between dilation caused by light and dilation caused by emotion. This is particularly challenging when examining emotional responses in individuals viewing videos, where luminosity changes constantly occur. To solve this problem, we propose a model that accurately predicts how pupil size changes with brightness, taking into account the nonlinearity of the relationship. Since effects of luminosity and emotion are additive without interacting [1], pure emotional effects on pupil size may be isolated by subtracting the model estimate from the total pupil size recorded in response to the visual stimuli. The general structure of the model was developed from findings in the literature and by analysing data collected from seven participants. The model was then tested on 10 subjects and different monitor models.We obtained an average error of 6.69% (SD:1.05%) and a maximum error of 23.29% (SD: 3.66%) between the actual pupil size and the pupil size predicted by the model. To the best of our knowledge, this is the only nonlinear model that has been validated on a sample of subjects. This research lays the groundwork for the accurate capture of emotional responses from pupil diameter under varying lighting conditions

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical system's position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMI's performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost

Thalamic and Entorhinal Network Activity Differently Modulates the Functional Development of Prefrontal-Hippocampal Interactions.

Precise information flow during mnemonic and executive tasks requires the coactivation of adult prefrontal and hippocampal networks in oscillatory rhythms. This interplay emerges early in life, most likely as an anticipatory template of later cognitive performance. At neonatal age, hippocampal theta bursts drive the generation of prefrontal theta-gamma oscillations. In the absence of direct reciprocal interactions, the question arises of which feedback mechanisms control the early entrainment of prefrontal-hippocampal networks. Here, we demonstrate that prefrontal-hippocampal activity couples with discontinuous theta oscillations and neuronal firing in both lateral entorhinal cortex and ventral midline thalamic nuclei of neonatal rats. However, these two brain areas have different contributions to the neonatal long-range communication. The entorhinal cortex mainly modulates the hippocampal activity via direct axonal projections. In contrast, thalamic theta bursts are controlled by the prefrontal cortex via mutual projections and contribute to hippocampal activity. Thus, the neonatal prefrontal cortex modulates the level of hippocampal activation by directed interactions with the ventral midline thalamus. Similar to the adult task-related communication, theta-band activity ensures the feedback control of long-range coupling in the developing brain.Significance statementMemories are encoded by finely tuned interactions within large-scale neuronal networks. This cognitive performance is not inherited, but progressively matures in relationship with the establishment of long-range coupling in the immature brain. The hippocampus initiates and unidirectionally drives the oscillatory entrainment of neonatal prefrontal cortex, yet feedback interactions that precisely control this early communication are still unresolved. Here, we identified distinct roles of entorhinal cortex and ventral midline thalamus for the functional development of prefrontal-hippocampal interactions. While entorhinal oscillations modulate the hippocampal activity by timing the neuronal firing via monosynaptic afferents, thalamic nuclei act as a relay station routing prefrontal activation back to hippocampus. Understanding the mechanisms of network maturation represents the prerequisite for assessing circuit dysfunction in neurodevelopmental disorders

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive

From Shortage to Surge : A Developmental Switch in Hippocampal-Prefrontal Coupling in a Gene-Environment Model of Neuropsychiatric Disorders

Cognitive deficits represent a major burden of neuropsychiatric disorders and result in part from abnormal communication within hippocampal-prefrontal circuits. While it has been hypothesized that this network dysfunction arises during development, long before the first clinical symptoms, experimental evidence is still missing. Here, we show that pre-juvenile mice mimicking genetic and environmental risk factors of disease (dual-hit GE mice) have poorer recognition memory that correlates with augmented coupling by synchrony and stronger directed interactions between prefrontal cortex and hippocampus. The network dysfunction emerges already during neonatal development, yet it initially consists in a diminished hippocampal theta drive and consequently, a weaker and disorganized entrainment of local prefrontal circuits in discontinuous oscillatory activity in dual-hit GE mice when compared with controls. Thus, impaired maturation of functional communication within hippocampal-prefrontal networks switching from hypo- to hyper-coupling may represent a mechanism underlying the pathophysiology of cognitive deficits in neuropsychiatric disorders.Peer reviewe

Validation of a New Classification Method of Postoperative Complications in Patients Undergoing Coronary Surgery

International audienceObjective The authors aimed to validate the European Multicenter Study on Coronary Artery Bypass Grafting (E-CABG) classification of postoperative complications in patients undergoing coronary artery bypass grafting (CABG). Design Retrospective, observational study. Setting University hospital. Participants A total of 2,764 patients with severe coronary artery disease. Complete baseline, operative, and postoperative data were available for patients who underwent isolated CABG. Interventions Isolated CABG. Measurements and Main Results The E-CABG complication classification was used to stratify the severity and prognostic impact of adverse postoperative events. Primary outcome endpoints were 30-day, 90-day, and long-term all-cause mortality. The secondary outcome endpoints was the length of intensive care unit stay. Both the E-CABG complication grades and additive score were predictive of 30-day (area under the receiver operating characteristics curve 0.866, 95% confidence interval [CI] 0.829-0.903; and 0.876; 95% CI 0.844-0.908, respectively) and 90-day (area under the receiver operating characteristics curve 0.850, 95% CI 0.812-0.887; and 0.863, 95% CI 0.829-0.897, respectively) all-cause mortality. The complication grades were independent predictors of increased mortality at actuarial (log-rank: p<0.0001) and adjusted analysis (p<0.0001; grade 1: hazard ratio [HR] 1.757, 95% CI 1.111-2.778; grade 2: HR 2.704, 95% CI 1.664-4.394; grade 3: HR 5.081, 95% CI 3.148-8.201). When patients who died within 30 days were excluded from the analysis, this grading method still was associated with late mortality (p<0.0001). The grading method (p<0.0001) and the additive score (rho, 0.514; p<0.0001) were predictive of the length of intensive care unit stay. Conclusions The E-CABG postoperative complication classification seems to be a promising tool for stratifying the severity and prognostic impact of postoperative complications in patients undergoing cardiac surger