Modeling the emergence of circadian rhythms in a clock neuron network

Circadian rhythms in pacemaker cells persist for weeks in constant darkness, while in other types of cells the molecular oscillations that underlie circadian rhythms damp rapidly under the same conditions. Although much progress has been made in understanding the biochemical and cellular basis of circadian rhythms, the mechanisms leading to damped or self-sustained oscillations remain largely unknown. There exist many mathematical models that reproduce the circadian rhythms in the case of a single cell of the Drosophila fly. However, not much is known about the mechanisms leading to coherent circadian oscillation in clock neuron networks. In this work we have implemented a model for a network of interacting clock neurons to describe the emergence (or damping) of circadian rhythms in Drosophila fly, in the absence of zeitgebers. Our model consists of an array of pacemakers that interact through the modulation of some parameters by a network feedback. The individual pacemakers are described by a well-known biochemical model for circadian oscillation, to which we have added degradation of PER protein by light and multiplicative noise. The network feedback is the PER protein level averaged over the whole network. In particular, we have investigated the effect of modulation of the parameters associated with (i) the control of net entrance of PER into the nucleus and (ii) the non-photic degradation of PER. Our results indicate that the modulation of PER entrance into the nucleus allows the synchronization of clock neurons, leading to coherent circadian oscillations under constant dark condition. On the other hand, the modulation of non-photic degradation cannot reset the phases of individual clocks subjected to intrinsic biochemical noise.Facultad de Ciencias Exacta

Modeling the emergence of circadian rhythms in a clock neuron network

Circadian rhythms in pacemaker cells persist for weeks in constant darkness, while in other types of cells the molecular oscillations that underlie circadian rhythms damp rapidly under the same conditions. Although much progress has been made in understanding the biochemical and cellular basis of circadian rhythms, the mechanisms leading to damped or self-sustained oscillations remain largely unknown. There exist many mathematical models that reproduce the circadian rhythms in the case of a single cell of the Drosophila fly. However, not much is known about the mechanisms leading to coherent circadian oscillation in clock neuron networks. In this work we have implemented a model for a network of interacting clock neurons to describe the emergence (or damping) of circadian rhythms in Drosophila fly, in the absence of zeitgebers. Our model consists of an array of pacemakers that interact through the modulation of some parameters by a network feedback. The individual pacemakers are described by a well-known biochemical model for circadian oscillation, to which we have added degradation of PER protein by light and multiplicative noise. The network feedback is the PER protein level averaged over the whole network. In particular, we have investigated the effect of modulation of the parameters associated with (i) the control of net entrance of PER into the nucleus and (ii) the non-photic degradation of PER. Our results indicate that the modulation of PER entrance into the nucleus allows the synchronization of clock neurons, leading to coherent circadian oscillations under constant dark condition. On the other hand, the modulation of non-photic degradation cannot reset the phases of individual clocks subjected to intrinsic biochemical noise.Facultad de Ciencias Exacta

Peripheral Nerve Imaging: Focus on Sonography

The diagnosis of different peripheral nerve disorders is basically established by electrodiagnostic tests; the assessment of the function of peripheral nerve disorders is estimated by nerve conduction tests (NCT) and electromyography (EMG). The need for more information about nerve morphology mandated the usage of more diagnostic tools. This role is now achieved by means of peripheral nerve imaging consisting mainly of magnetic resonance imaging (MRI) and ultrasonography. In this chapter we will clarify the role of imaging in the diagnosis of peripheral nerve disorders, concentrating more on the role of modern high-resolution ultrasound, considering its advantages like cheap price, dynamic ability, and possibility of comparison with the contralateral side at the same setting

Representations and processes: What role for multivariate methods in cognitive neuroscience?

Abstract: The significance of neuroscientific findings for the analysis of central problems in cognitive science has long been a matter of debate. Recent developments in cognitive neuroscience have reignited this discussion, especially with regard to the study of cognitive representations and cognitive processes. The present paper focuses on multivariate analyses, a class of neuroscientific methods that promises to shed new light on the neural bases of cognitive representations. Multivariate approaches are both powerful and increasingly used. Yet, we argue that their successful application in neuroscience requires significant theoretical and methodological clarification. After providing a preliminary assessment of the pros and cons of multivariate methods, we claim that their successful application crucially depends on how we conceptualize the relationships between representations, cognitive processes, and neural data, in other words, on the cognitive ontology we use to describe the human mind. Our discussion also highlights some general strengths and weaknesses of neuroscientific contributions to the program of classical cognitive science.Keywords: Cognitive Process; fMRI; Multivariate Analysis; Marr’s Three Levels of Analysis; Cognitive Ontology Rappresentazioni e processi: quale ruolo per i metodi multivariati nelle neuroscienze cognitive?Riassunto: La rilevanza dei risultati neuroscientifici per quanto riguarda i problemi centrali delle scienze cognitive è motivo di discussione. Recenti sviluppi nelle neuroscienze cognitive hanno rianimato tale dibattito, in particolare rispetto allo studio delle rappresentazioni e dei processi cognitivi. Il presente articolo si focalizza sull’analisi multivariata, un insieme di metodi neuroscientifici che si promettono di studiare le basi neurali delle rappresentazioni cognitive. Nonostante le potenzialità e l’uso pervasivo degli approcci multivariati, in questo lavoro sosteniamo che prima di poter valutare il loro effettivo contributo nello studio delle rappresentazioni cognitive sia necessaria una chiarificazione teorica e metodologica. Dopo una discussione preliminare dei vantaggi e degli svantaggi dei metodi multivariati, evidenziamo come una loro efficace applicazione dipenda in maniera sostanziale da come viene intesa la relazione tra rappresentazioni, processi cognitivi e dati neurali o, in altre parole, dall’ontologia cognitiva che impieghiamo per descrivere la mente umana. Il presente lavoro affronta inoltre i generali punti di forza e gli elementi critici rispetto al contributo della ricerca neuroscientifica nel programma delle scienze cognitive classiche.Parole chiave: Cognitive Process; fMRI; Multivariate Analysis; Marr’s Three Levels of Analysis; Cognitive Ontolog

Comparison of two deep reinforcement learning algorithms towards an optimal policy for smart building thermal control

Heating, Ventilation, and Air Conditioning (HVAC) systems are the main providers of occupant comfort, and at the same time, they represent a significant source of energy consumption. Improving their efficiency is essential for reducing the environmental impact of buildings. However, traditional rule-based and model-based strategies are often inefficient in real-world applications due to the complex building thermal dynamics and the influence of heterogeneous disturbances, such as unpredictable occupant behavior. In order to address this issue, the performance of two state-of-the-art model-free Deep Reinforcement Learning (DRL) algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), has been compared when the percentage valve opening is managed in a thermally activated building system, modeled in a simulated environment from data collected in an existing office building in Switzerland. Results show that PPO reduced energy costs by 18% and decreased temperature violations by 33%, while SAC achieved a 14% reduction in energy costs and 64% fewer temperature violations compared to the onsite Rule-Based Controller (RBC)

Online implementation of a soft actor-critic agent to enhance indoor temperature control and energy efficiency in buildings

Recently, a growing interest has been observed in HVAC control systems based on Artificial Intelligence, to improve comfort conditions while avoiding unnecessary energy consumption. In this work, a model-free algorithm belonging to the Deep Reinforcement Learning (DRL) class, Soft Actor-Critic, was implemented to control the supply water temperature to radiant terminal units of a heating system serving an office building. The controller was trained online, and a preliminary sensitivity analysis on hyperparameters was performed to assess their influence on the agent performance. The DRL agent with the best performance was compared to a rule-based controller assumed as a baseline during a three-month heating season. The DRL controller outperformed the baseline after two weeks of deployment, with an overall performance improvement related to control of indoor temperature conditions. Moreover, the adaptability of the DRL agent was tested for various control scenarios, simulating changes of external weather conditions, indoor temperature setpoint, building envelope features and occupancy patterns. The agent dynamically deployed, despite a slight increase in energy consumption, led to an improvement of indoor temperature control, reducing the cumulative sum of temperature violations on average for all scenarios by 75% and 48% compared to the baseline and statically deployed agent respectively

Documentário autobiográfico e feminismo: O quarteto de filmes de Miriam Weinstein

O documentário autobiográfico herdeiro da tradição do cinema direto foi desenvolvido pelos cineastas de Cambridge, agrupados no centro de ensino, pesquisa e produção cinematográfica do MIT, o MIT Film Section. Membro do grupo, Miriam Weinstein realizou, na década de 1970, quatro documentários de curta-metragem: My father the Doctor (1972); Living With Peter (1973); We Get Married Twice (1973) e Cal Me Mama (1976). Neste artigo discutiremos como as contribuições estético-políticas de sua obra constituem parte do legado de toda uma geração de cineastas mulheres feministas

Management of neuropathic pain: A graph theory-based presentation of literature review.

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