Sensor fusion in distributed cortical circuits

The substantial motion of the nature is to balance, to survive, and to reach perfection. The evolution in biological systems is a key signature of this quintessence. Survival cannot be achieved without understanding the surrounding world. How can a fruit fly live without searching for food, and thereby with no form of perception that guides the behavior? The nervous system of fruit fly with hundred thousand of neurons can perform very complicated tasks that are beyond the power of an advanced supercomputer. Recently developed computing machines are made by billions of transistors and they are remarkably fast in precise calculations. But these machines are unable to perform a single task that an insect is able to do by means of thousands of neurons. The complexity of information processing and data compression in a single biological neuron and neural circuits are not comparable with that of developed today in transistors and integrated circuits. On the other hand, the style of information processing in neural systems is also very different from that of employed by microprocessors which is mostly centralized. Almost all cognitive functions are generated by a combined effort of multiple brain areas. In mammals, Cortical regions are organized hierarchically, and they are reciprocally interconnected, exchanging the information from multiple senses. This hierarchy in circuit level, also preserves the sensory world within different levels of complexity and within the scope of multiple modalities. The main behavioral advantage of that is to understand the real-world through multiple sensory systems, and thereby to provide a robust and coherent form of perception. When the quality of a sensory signal drops, the brain can alternatively employ other information pathways to handle cognitive tasks, or even to calibrate the error-prone sensory node. Mammalian brain also takes a good advantage of multimodal processing in learning and development; where one sensory system helps another sensory modality to develop. Multisensory integration is considered as one of the main factors that generates consciousness in human. Although, we still do not know where exactly the information is consolidated into a single percept, and what is the underpinning neural mechanism of this process? One straightforward hypothesis suggests that the uni-sensory signals are pooled in a ploy-sensory convergence zone, which creates a unified form of perception. But it is hard to believe that there is just one single dedicated region that realizes this functionality. Using a set of realistic neuro-computational principles, I have explored theoretically how multisensory integration can be performed within a distributed hierarchical circuit. I argued that the interaction of cortical populations can be interpreted as a specific form of relation satisfaction in which the information preserved in one neural ensemble must agree with incoming signals from connected populations according to a relation function. This relation function can be seen as a coherency function which is implicitly learnt through synaptic strength. Apart from the fact that the real world is composed of multisensory attributes, the sensory signals are subject to uncertainty. This requires a cortical mechanism to incorporate the statistical parameters of the sensory world in neural circuits and to deal with the issue of inaccuracy in perception. I argued in this thesis how the intrinsic stochasticity of neural activity enables a systematic mechanism to encode probabilistic quantities within neural circuits, e.g. reliability, prior probability. The systematic benefit of neural stochasticity is well paraphrased by the problem of Duns Scotus paradox: imagine a donkey with a deterministic brain that is exposed to two identical food rewards. This may make the animal suffer and die starving because of indecision. In this thesis, I have introduced an optimal encoding framework that can describe the probability function of a Gaussian-like random variable in a pool of Poisson neurons. Thereafter a distributed neural model is proposed that can optimally combine conditional probabilities over sensory signals, in order to compute Bayesian Multisensory Causal Inference. This process is known as a complex multisensory function in the cortex. Recently it is found that this process is performed within a distributed hierarchy in sensory cortex. Our work is amongst the first successful attempts that put a mechanistic spotlight on understanding the underlying neural mechanism of Multisensory Causal Perception in the brain, and in general the theory of decentralized multisensory integration in sensory cortex. Engineering information processing concepts in the brain and developing new computing technologies have been recently growing. Neuromorphic Engineering is a new branch that undertakes this mission. In a dedicated part of this thesis, I have proposed a Neuromorphic algorithm for event-based stereoscopic fusion. This algorithm is anchored in the idea of cooperative computing that dictates the defined epipolar and temporal constraints of the stereoscopic setup, to the neural dynamics. The performance of this algorithm is tested using a pair of silicon retinas

Sensor fusion in distributed cortical circuits

The substantial motion of the nature is to balance, to survive, and to reach perfection. The evolution in biological systems is a key signature of this quintessence. Survival cannot be achieved without understanding the surrounding world. How can a fruit fly live without searching for food, and thereby with no form of perception that guides the behavior? The nervous system of fruit fly with hundred thousand of neurons can perform very complicated tasks that are beyond the power of an advanced supercomputer. Recently developed computing machines are made by billions of transistors and they are remarkably fast in precise calculations. But these machines are unable to perform a single task that an insect is able to do by means of thousands of neurons. The complexity of information processing and data compression in a single biological neuron and neural circuits are not comparable with that of developed today in transistors and integrated circuits. On the other hand, the style of information processing in neural systems is also very different from that of employed by microprocessors which is mostly centralized. Almost all cognitive functions are generated by a combined effort of multiple brain areas. In mammals, Cortical regions are organized hierarchically, and they are reciprocally interconnected, exchanging the information from multiple senses. This hierarchy in circuit level, also preserves the sensory world within different levels of complexity and within the scope of multiple modalities. The main behavioral advantage of that is to understand the real-world through multiple sensory systems, and thereby to provide a robust and coherent form of perception. When the quality of a sensory signal drops, the brain can alternatively employ other information pathways to handle cognitive tasks, or even to calibrate the error-prone sensory node. Mammalian brain also takes a good advantage of multimodal processing in learning and development; where one sensory system helps another sensory modality to develop. Multisensory integration is considered as one of the main factors that generates consciousness in human. Although, we still do not know where exactly the information is consolidated into a single percept, and what is the underpinning neural mechanism of this process? One straightforward hypothesis suggests that the uni-sensory signals are pooled in a ploy-sensory convergence zone, which creates a unified form of perception. But it is hard to believe that there is just one single dedicated region that realizes this functionality. Using a set of realistic neuro-computational principles, I have explored theoretically how multisensory integration can be performed within a distributed hierarchical circuit. I argued that the interaction of cortical populations can be interpreted as a specific form of relation satisfaction in which the information preserved in one neural ensemble must agree with incoming signals from connected populations according to a relation function. This relation function can be seen as a coherency function which is implicitly learnt through synaptic strength. Apart from the fact that the real world is composed of multisensory attributes, the sensory signals are subject to uncertainty. This requires a cortical mechanism to incorporate the statistical parameters of the sensory world in neural circuits and to deal with the issue of inaccuracy in perception. I argued in this thesis how the intrinsic stochasticity of neural activity enables a systematic mechanism to encode probabilistic quantities within neural circuits, e.g. reliability, prior probability. The systematic benefit of neural stochasticity is well paraphrased by the problem of Duns Scotus paradox: imagine a donkey with a deterministic brain that is exposed to two identical food rewards. This may make the animal suffer and die starving because of indecision. In this thesis, I have introduced an optimal encoding framework that can describe the probability function of a Gaussian-like random variable in a pool of Poisson neurons. Thereafter a distributed neural model is proposed that can optimally combine conditional probabilities over sensory signals, in order to compute Bayesian Multisensory Causal Inference. This process is known as a complex multisensory function in the cortex. Recently it is found that this process is performed within a distributed hierarchy in sensory cortex. Our work is amongst the first successful attempts that put a mechanistic spotlight on understanding the underlying neural mechanism of Multisensory Causal Perception in the brain, and in general the theory of decentralized multisensory integration in sensory cortex. Engineering information processing concepts in the brain and developing new computing technologies have been recently growing. Neuromorphic Engineering is a new branch that undertakes this mission. In a dedicated part of this thesis, I have proposed a Neuromorphic algorithm for event-based stereoscopic fusion. This algorithm is anchored in the idea of cooperative computing that dictates the defined epipolar and temporal constraints of the stereoscopic setup, to the neural dynamics. The performance of this algorithm is tested using a pair of silicon retinas

Effect of Gelatin-Based Edible Coatings Incorporated with Aloe vera

The aim of this study was to evaluate the effect of gelatin coating incorporated with Aloe vera gel (50,100%) and green and black tea extracts (5,10%) on physicochemical, microbial, and sensorial properties of fresh-cut oranges at 4°C for 17 days. Significant differences in terms of quality parameters were observed between the control and coated fresh-cut oranges. The highest variation of quality parameters was observed in control, while the least variations were observed in coated slices with 100% Aloe vera and 10% green tea extract. The weight loss was increased with time, but the coating treatment especially with 100% Aloe vera had significant effect on the prevention of weight loss. Also, Aloe vera coated samples obtained the highest score in sensory evaluation. Coating with gelatin incorporated with Aloe vera and green tea extracts successfully retarded the microbial growth and therefore extended the shelf life of fresh-cut oranges during cold storage

Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach

Preisach model is a well-known hysteresis identification method in which the hysteresis is modeled by linear combination of hysteresis operators. Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real-time control applications. Here a novel neural network approach based on the Preisach model is addressed, provides accurate hysteresis nonlinearity modeling in comparison with the classical Preisach model and can be used for many applications such as hysteresis nonlinearity control and identification in SMA and Piezo actuators and performance evaluation in some physical systems such as magnetic materials. To evaluate the proposed approach, an experimental apparatus consisting one-dimensional flexible aluminum beam actuated with an SMA wire is used. It is shown that the proposed ANN-based Preisach model can identify hysteresis nonlinearity more accurately than the classical one. It also has powerful ability to precisely predict the higher-order hysteresis minor loops behavior even though only the first-order reversal data are in use. It is also shown that to get the same precise results in the classical Preisach model, many more data should be used, and this directly increases the experimental cost

The Combined Effects of Levothyroxine and Low Level Laser Therapy on Wound Healing in Hypothyroidism Male Rat Model

Introduction: Hypothyroidism is caused by inadequate production and storage of thyroid hormones. Hypothyroidism is associated with delayed wound healing. Laser therapy may stimulate wound regeneration. The aim of this study was to determine the combined effects of levothyroxine and low level laser therapy during the wound healing process on skin of hypothyroidism male rat model.Methods: Thirty male Wistar rats were randomly divided into 5 groups: control group, hypothyroidism group, hypothyroidism group treated by laser, hypothyroidism group treated by levothyroxine, and hypothyroidism group treated by laser and levothyroxine. To induce hypothyroidism, methimazole was given at a dose of 4 mg/100 mL in their drinking water. After hypothyroidism was proven through immunoassay commercial kit, rats were generally anesthetized with ketamine and xylazine, then, an incisional skin wound was created in a length of 1.2 cm on the back of the ribcage. The surgical day is considered as the zero day. The third and fifth groups were treated with a pulse laser, 810 nm wavelength 80 Hz frequency and 0.2 J/cm2 energy densities for 200 seconds. Levothyroxine was injected to the fourth and fifth groups intraperitoneally. On the 14th day, a normal sample of each healing skin wound was harvested for biomechanical examination. The obtained data were analyzed by the SPSS software 21 and reported as a mean ± standard error of mean (SEM). P < 0.05 was considered statistically significant.Results: The results showed that the mean maximum force and the accomplished work (energy) made a significant difference in the group receiving both laser and levothyroxine synchronously rather than the other groups (P ≤ 0.05). The elasticity of the wound healing in the groups that received laser and levothyroxine synchronously was significantly higher in comparison with the control and hypothyroidism groups but the difference was not significant in comparison with the laser or levothyroxine groups.Conclusion: The results of our study showed that the application of laser and levothyroxine synchronously improves the biomechanical parameters of wound during healing in comparison to the use of laser and levothyroxine solel

Combined Effect of Low-Level Laser Treatment and Levothyroxine on Wound Healing in Rats With Hypothyroidism

Introduction: Hypothyroidism delays wound healing by reducing the synthesis of keratinocytes, fibroblast cells, and collagen. Methods for enhancement of wound healing include laser therapy and hormone therapy. The current study evaluated the combined effect of laser and levothyroxine therapy to cure wounds in male rats with hypothyroidism.Methods: Sixty male Wistar rats were randomly divided into 5 groups: (1) healthy controls; (2) controls with hypothyroidism; (3) hypothyroidism + laser treatment; (4) hypothyroidism + levothyroxine treatment; (5) hypothyroidism + laser + levothyroxine treatment. Hypothyroidism was induced by dissolving 4 mg of methimazole in 100 mL of drinking water daily for 28 days. After hypothyroidism had been confirmed, a longitudinal incisional wound was created on the dorsal rib cages of the rats. The wounds that received laser treatment were divided into 12 sections and treated at 810 nm wavelength and 0.2 J/cm2 of energy density for 200 seconds. Levothyroxine was administrated in doses of 20 μg/kg/d i.p. All groups were divided into 3 subgroups for testing on days 4, 7 and 14. Samples were collected in all the subgroups.Results: The results showed that hypothyroidism reduced fibrous tissue volume, fibroblasts, and basal cell numbers. The combined effect of laser and levothyroxine improved all parameters.Conclusion: Combined laser and levothyroxine treatment showed the best effect on wound healing and accelerated the closure of the wounds

MODEL KLINIČKOG ZAKLJUČIVANJA U PRISTUPU FEBRILNIM ZARAZNIM BOLESTIMA U SREDNJOVJEKOVNOJ PERZIJI

Reviewing ancient manuscripts of Persian medicine (PM) reveals that there have been some basic principles for decision-making in epidemic infectious diseases that existed in the past. These PM rules for clinical reasoning were applied through a personalized approach along with public health advice in such situations. Currently, the coronavirus pandemic has been the biggest problem in the world. Its mainstay of treatment is based on preventative measures and symptomatic treatments. Meanwhile, traditional medical systems for providing preventive, supportive, and rehabilitative care to patients have received more attention than before. Thus, the specific individual approach considered by PM scholars for clinical courses of epidemic infectious diseases may help shed more light on the spread of knowledge on epidemic diseases in ancient Persia.Pregledom drevnih rukopisa perzijske medicine (PM) otkriveno je da su u prošlosti postojali neki osnovni principi za donošenje odluka o epidemijskim zaraznim bolestima. Ta PM pra-vila za kliničko zaključivanje primijenjena su kroz personalizirani pristup zajedno sa savjeti-ma o javnom zdravlju u takvim situacijama. Trenutačno je pandemija koronavirusa najveći problem u svijetu. Glavni temelj liječenja koronavirusa preventivne su mjere i simptomatska obrada. U međuvremenu, tradicionalnim medicinskim sustavima za pružanje preventivne, potporne i rehabilitacijske skrbi pacijentima posvećena je veća pozornost nego prije. Prema tome, specifičan individualni pristup koji su znanstvenici PM-a razmatrali kod kliničkog tijeka epidemijskih zaraznih bolesti mogao bi pomoći u rasvjetljavanju širenja znanja o epi-demijskim bolestima u drevnoj Perziji

Immediate Results of Percutaneous Trans-Luminal Mitral Commissurotomy in Pregnant Women with Severe Mitral Stenosis

Background Valvular heart diseases and mainly rheumatic heart diseases complicate about 1% of pregnancies. During pregnancy physiological hemodynamic changes of the circulation are the main cause of mitral stenosis (MS) decompensation. Prior to introduction of percutaneous mitral balloon commissuroplasty (PTMC), surgical comissurotomy was the preferred method of treatment in patients with refractory symptoms. PTMC is an established non-surgical treatment of rheumatic mitral stenosis. The study aimed to assess the safety and efficacy of PTMC in pregnant women with severs mitral stenosis. Material and Method Thirty three consecutive patients undergoing PTMC during pregnancy enrolled in this prospective study. Mitral valve area (MVA), transmitral valve gradient (MVG), and severity of mitral regurgitation (MR) were assessed before and 24 hour after the procedure by transthoracic and transesophageal echocardiography. Mitral valve morphology was evaluated before the procedure using Wilkin's criteria. Patient followed for one month and neonates monitored for weight and height and adverse effect of radiation. Result Mitral valve area increased from 0.83 ± 0.13 cm 2 to 1.38 ± 0.29 cm 2 ( P = 0.007). Mean gradient of mitral valve decreased from 15.5 ± 7.4 mmHg to 2.3 ± 2.3 mmHg ( P = <0.001). Pulmonary artery pressure decreased from 65.24 ± 17.9 to 50.45 ± 15.33 ( P = 0.012). No maternal death, abortion, intrauterine growth restriction was observed and only one stillbirth occurred. Conclusion PTMC in pregnant women has favorable outcome and no harmful effect on children noted

Harnessing the Power of Smart and Connected Health to Tackle COVID-19:IoT, AI, Robotics, and Blockchain for a Better World

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) &#x2014; including Machine Learning (ML) and Big Data analytics &#x2014; as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This paper provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas where IoT can contribute are discussed, namely, i) tracking and tracing, ii) Remote Patient Monitoring (RPM) by Wearable IoT (WIoT), iii) Personal Digital Twins (PDT), and iv) real-life use case: ICT/IoT solution in Korea. Second, the role and novel applications of AI are explained, namely: i) diagnosis and prognosis, ii) risk prediction, iii) vaccine and drug development, iv) research dataset, v) early warnings and alerts, vi) social control and fake news detection, and vii) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including i) crowd surveillance, ii) public announcements, iii) screening and diagnosis, and iv) essential supply delivery. Finally, we discuss how Distributed Ledger Technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19