Ferroelectric-based synapses and neurons for neuromorphic computing

The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as Spiking Neural Networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems' power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technology for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.

Neural Circuits Underlying Behavioral Flexibility: Insights From Drosophila

Behavioral flexibility is critical to survival. Animals must adapt their behavioral responses based on changes in the environmental context, internal state, or experience. Studies in Drosophila melanogaster have provided insight into the neural circuit mechanisms underlying behavioral flexibility. Here we discuss how Drosophila behavior is modulated by internal and behavioral state, environmental context, and learning. We describe general principles of neural circuit organization and modulation that underlie behavioral flexibility, principles that are likely to extend to other species.

Y2O3-based Memristive Crossbar Array for Synaptic Learning

Here, we report a fabrication of Y2O3-based memristive crossbar array along with an analytical model to evaluate the performance of such memristive array system to understand the forgetting and retention behavior in the neuromorphic computation. The developed analytical model is able to simulate the highly-dense memristive crossbar array based neural network of biological synapses. These biological synapses control the communication efficiency between neurons and can implement the learning capability of the neurons. During electrical stimulation of the memristive devices, the memory transition is exhibited along with the number of applied voltage pulses which is analogous to the real human brain functionality. Further, to obtain the forgetting and retention behavior of the memristive devices, a modified window function equation is proposed by incorporating two novel internal state variables in the form of forgetting rate and retention. The obtained results confirm that the effect of variation in electrical stimuli on forgetting and retention as similar to the biological brain. Therefore, the developed analytical memristive model further can be utilized in the memristive system to develop real-world applications in neuromorphic domains.

Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study

This study aims to build a system for detecting a driver’s internal state using body-worn sensors. Our system is intended to detect inattentive driving that occurs during long-term driving on a monotonous road, such as a high-way road. The inattentive state of a driver in this study is an absent-minded state caused by a decrease in driver vigilance levels due to fatigue or drowsiness. However, it is difficult to clearly define these inattentive states because it is difficult for the driver to recognize when they fall into an absent-minded state. To address this problem and achieve our goal, we have proposed a detection algorithm for inattentive driving that not only uses a heart rate sensor, but also uses body-worn inertial sensors, which have the potential to detect driver behavior more accurately and at a much lower cost. The proposed method combines three detection models: body movement, drowsiness, and inattention detection, based on an anomaly detection algorithm. Furthermore, we have verified the accuracy of the algorithm with the experimental data for five participants that were measured in long-term and monotonous driving scenarios by using a driving simulator. The results indicate that our approach can detect both the inattentive and drowsiness states of drivers using signals from both the heart rate sensor and accelerometers placed on wrists.

Contribution of behavioural variability to representational drift

Neuronal responses to similar stimuli change dynamically over time, raising the question of how internal representations can provide a stable substrate for neural coding. While the drift of these representations is mostly characterized in relation to external stimuli or tasks, behavioural or internal state of the animal is also known to modulate the neural activity. We therefore asked how the variability of such modulatory mechanisms can contribute to representational drift. By analysing publicly available datasets from the Allen Brain Observatory, we found that behavioural variability significantly contributes to changes in stimulus-induced neuronal responses across various cortical areas in the mouse. This effect could not be explained by a gain model in which change in the behavioural state scaled the signal or the noise. A better explanation was provided by a model in which behaviour contributed independently to neuronal tuning. Our results are consistent with a view in which behaviour modulates the low-dimensional, slowly-changing setpoints of neurons, upon which faster operations like sensory processing are performed. Importantly, our analysis suggests that reliable but variable behavioural signals might be misinterpreted as representational drift, if neuronal representations are only characterized in the stimulus space and marginalised over behavioural parameters.

Protection of Critical Infrastructure Facilities as a Component of the National Security

The issue of protecting critical infrastructure as one of the components of national security is analyzed. The following methods were used in the study: bibliographic, dialectical, empirical, and theoretical, comparative, and legal. The essence of the term "critical infrastructure” is explained both according to the opinions of scientists and from the very position of the authors of the article. The importance of proper protection and proper functioning of infrastructure in Ukraine is well founded. It emphasizes the fact that for many years the issue of the importance of protecting critical infrastructure has been almost forgotten and is not relevant to the governing bodies of the state. In addition, this situation applies to many other countries in the world. The current situation shows that there are countries that, despite being among the most prosperous and innovative, did not pay attention to their situation with their own security infrastructure. It is concluded that, based on a comparative analysis of international experience, in addition to exploring the peculiarities of national realities, the article proposed measures to improve the internal state of protection of critical infrastructure.

Algorithm for developing and implementing enterprise strategy

The article examines the theoretical and methodological foundations and existing approaches to the process of developing an enterprise development strategy. It is established that the procedure of development and implementation of development strategy is a debatable issue among scientists, who determine different methodological approaches to the main stages of strategy development and implementation, components of this process, the relationship and interdependence between them. Based on the research, we propose to identify three stages of the strategy development process, combining 8 phases. The first stage - preparatory or analytical, includes a step-by-step comprehensive strategic analysis of the external and internal environment. The second stage defines the process of strategy development, which includes 4 phases: formulation of the mission; substantiation of strategic goals and determination of strategic gap; development of alternative strategies taking into account the limitations and possible risks, identification of promising and priority areas of development; choice of development strategy. The third stage - the realization of the strategy combines the stages of implementation of the strategy, its evaluation, and adjustment in accordance with unforeseen changes in the external environment. The defined stages of strategy development characterize the current state of the object, the forecasted goals of its development, and actions to achieve them. An important stage in developing a strategy is analytical. It determines the internal state of the system and its necessary changes, in accordance with the projected changes in the external environment, to achieve certain strategic goals. In general, the strategic development of the enterprise is a continuous process of formation and implementation of management decisions aimed at ensuring the strategic dynamism and effective operation in conditions of increased complexity and unpredictability of the business environment.

Internal state effects on behavioral shifts in freely behaving praying mantises (Tenodera sinensis)

How we interact with our environment largely depends on both the external cues presented by our surroundings and the internal state from within. Internal states are the ever-changing physiological conditions that communicate the immediate survival needs and motivate the animal to behaviorally fulfill them. Satiety level constitutes such a state, and therefore has a dynamic influence on the output behaviors of an animal. In predatory insects like the praying mantis, hunting tactics, grooming, and mating have been shown to change hierarchical organization of behaviors depending on satiety. Here, we analyze behavior sequences of freely hunting praying mantises (Tenodera sinensis) to explore potential differences in sequential patterning of behavior as a correlate of satiety. First, our data supports previous work that showed starved praying mantises were not just more often attentive to prey, but also more often attentive to further prey. This was indicated by the increased time fraction spent in attentive bouts such as prey monitoring, head turns (to track prey), translations (closing the distance to the prey), and more strike attempts. With increasing satiety, praying mantises showed reduced time in these behaviors and exhibited them primarily towards close-proximity prey. Furthermore, our data demonstrates that during states of starvation, the praying mantis exhibits a stereotyped pattern of behavior that is highly motivated by prey capture. As satiety increased, the sequenced behaviors became more variable, indicating a shift away from the necessity of prey capture to more fluid presentations of behavior assembly.