Pairs of neurons in brain networks often share much of the input they receive from other neurons. Due to essential non-linearities of the neuronal dynamics, the consequences for the correlation of the output spike trains are generally not well understood. Here we analyze the case of two leaky integrate-and-fire neurons using a novel non-perturbative approach. Our treatment covers both weakly and strongly correlated dynamics, generalizing previous results based on linear response theory.

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.

The Internet of Things (IoT) applications has been developing greatly in recent years to solve communication problems, especially in rural areas. Within the IoT, the context-awareness paradigm, especially in precision agricultural practices, has come to a state of the planning of production time. As smart cities approach, the smart environment approach also increases its place in IoT applications and has dominated research in recent years in literature. In this study, soil and environmental information were collected in 17 km diameter in rural area with developed Long Range (LoRa) based context-aware platform. With the developed sensor and actuator control unit, soil moisture at 5 cm and 30 cm depth and soil surface temperature information were collected and the communication performance was investigated. During the study, the performance measurements of the developed Serial Peripheral Interface (SPI) enabled Long Range Wide Area Network (LoRaWAN) gateway were also performed.

The Internet of Things (IoT) paradigm is referring to the underlying constituents of the 4th Industrial Revolution that will also affect the use of the internet in industrial production in the future. More than 50 billion smart devices will be able to communicate with each other and internet services over the increasing network capabilities of wireless sensor networks nodes on IoT applications in the next ten years. One of the leading production areas using IoT within wireless sensor networks is precision agricultural practices. In this study, a new sensor node design, which includes ambient light and temperature sensors employing Bluetooth Low Energy (BLE) communication protocol, is used as an IoT application. Subsequent to this, sensor node power consumption and management cost was investigated. The experimental results show that the developed sensor node lifetime is about 8 years and the total cost of nodes and gateway model is under $50 per year per 0.1 hectare.

Nowadays, there is an increasing demand for indoor positioning systems as services based on location are very important in mobile applications. Since Global Positioning System (GPS) makes only outdoor positioning successful, there is a need of new approaches for indoor positioning systems. Some techniques on indoor positioning systems have been proposed in this scope, but they have not reached to the success of outdoor systems in terms of speed, consistency and power management. The aim of this study is to develop an indoor positioning system using latest Bluetooth Low Energy (BLE) technology. This system consists of BLE sensor nodes, a mobile device and a mobile application that calculates indoor position by measuring the signal levels of the sensor nodes designed. BLE sensor nodes have low power consumption and can be powered by a coin battery. Since most consumers have a mobile phone today, the system can be used easily by installing a mobile application.

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