High Efficiency Real-Time Sensor and Actuator Control and Data Processing

The advances in sensor and actuator technology foster the use of large multitransducer networks in many different fields. The increasing complexity of such networks poses problems in data processing, especially when high-efficiency is required for real-time applications. In fact, multi-transducer data processing usually consists of interconnection and co-operation of several modules devoted to process different tasks. Multi-transducer network modules often include tasks such as control, data acquisition, data filtering interfaces, feature selection and pattern analysis. Heterogeneous techniques derived from chemometrics, neural networks, fuzzy-rules used to implement such tasks may introduce module interconnection and co-operation issues. To help dealing with these problems the author here presents a software library architecture for a dynamic and efficient management of multi-transducer data processing and control techniques. The framework’s base architecture and the implementation details of several extensions are described. Starting from the base models available in the framework core dedicated models for control processes and neural network tools have been derived. The Facial Automaton for Conveying Emotion (FACE) has been used as a test field for the control architecture

Psychophysiological perspectives on autism

Autism is a severe developmental neuropsychiatric disorder, with an onset in the first three years of life. It essentially affects aspects of behaviour which are generally regarded as 'human'. Core characteristics of autism are abnormalities in language, communication and soical interaction, narrowed interests and stereotyped behaviour. Although theories of different nature exists on what the underlying deficit for these abnormalities might be, a common aspect of all theories may be a defect in attention. Event-related potentials (ERPs) are, due to their very high temporal resolution, very well suited for the study of attentional processing in the brain. ERP studies of the P3, a large positivity occurring around 300 milliseconds after a stimulus is presented, have pointed out that this component is much smaller in autistic individuals than in controls, especially over posterior parts of the brain. However, none of these studies have made a detailed study of attentional processing before 300 milliseconds. In this thesis, visual and auditory selective attention tasks are described in groups of high functioning autistic children and adolescents. It was found that autistic children show profoundly smaller P3 amplitudes than controls, but that these abnormalities were not preceded by abnormalities in selective attention. However, autistic children already showed abnormalities in visual processing around 100 milliseconds after stimulus presentation, as seen in much smaller P1 amplitudes. In autistic adolescents such amplitude reductions were not observed, but this group did show abnormal selective attention. These abnormalities in selective attention were interpreted as compensatory mechanisms, normalizing P3. Whether abnormal P3 was a reflection of deficiencies in attentional capacity was studied with a probe task. In this task, autistic individuals did not show the same trade-off between task- and probe-stimuli as was seen in controls. When probe stimuli became increasingly irrelevant, controls invested less capacity in processing these stimuli as evident in smaller P3 amplitudes to those stimuli. Autistic patients did not show such a decrease in P3 amplitude. Therefore, autistic patients seem to have a deficiency in the allocation of processing capacity. In order to gain more insight in the underlying neural sources of the scalp recorded ERPs, the data from the visual selective attention task were subjected to high-resolution source localization techniques based on individual head models derived from Magnetic Resonance Images (MRI). It was found that autistic children showed a more superior location of the sources for P1, which is discussed in light of functional and structural MRI findings in autism. The use of the technique described in this thesis is new in the field of autism and provides valuable new insights in the disorde

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202