26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Large-Scale Brain Dynamics: Plasticity and States of Consciousness

The brain is a complex system that exhibits rich multiscale dynamics. Large-scale activity in this system are readily observable by a range of methods including electroencephalography (EEG). Changes to the global state of the brain, including arousal states such as wakeful consciousness and its temporary disappearance with sleep onset, are associated with major changes in brain electric activity as seen in the EEG. However, the purpose of the sleep state, which involves widespread changes in activity that occur in the brain, remains a matter of debate, especially because very little content of the brain activity that occurs during sleep directly enters conscious awareness. Analysis of EEG using modeling approaches has been highly successful at relating large scale brain physiology to experimental observations. In particular, physiologically based modeling addresses significant issues that commonly arise in high-dimensional models, by constraining each parameter on the basis of experimental data, and by providing a physiologically meaningful interpretation of all model parameters. One class of brain models is based on neural field theory, which averages the properties of neurons over short temporal and spatial scales to form continuous fields that represent neural activity. These models are ideally suited to EEG comparison and analysis because the EEG reflects the combined activity of millions of individual neurons. This thesis uses an established neural field model of the brain to investigate large-scale synaptic plasticity over a range of brain states. In particular, the model is used to investigate the synaptic homeostasis hypothesis, which postulates that sleep is necessary for long-term synaptic stability. The same biophysical model is also used to estimate physiological quantities in various disorders of consciousness

Technologies and Data Analytics to Manage Grain Quality On-Farm—A Review

Grains intended for human consumption or feedstock are typically high-value commodities that are marketed based on either their visual characteristics or compositional properties. The combination of visual traits, chemical composition and contaminants is generally referred to as grain quality. Currently, the market value of grain is quantified at the point of receival, using trading standards defined in terms of visual criteria of the bulk grain and chemical constituency. The risk for the grower is that grain prices can fluctuate throughout the year depending on world production, quality variation and market needs. The assessment of grain quality and market value on-farm, rather than post-farm gate, may identify high- and low-quality grain and inform a fair price for growers. The economic benefits include delivering grain that meets specifications maximizing the aggregate price, increasing traceability across the supply chain from grower to consumer and identifying greater suitability of differentiated products for high-value niche markets, such as high protein product ideal for plant-based proteins. This review focuses on developments that quantify grain quality with a range of spectral sensors in an on-farm setting. If the application of sensor technologies were expanded and adopted on-farm, growers could identify the impact and manage the harvesting operation to meet a range of quality targets and provide an economic advantage to the farming enterprise