A novel approach to neutron dosimetry

Purpose: Having been overlooked for many years, research is now starting to take into account the directional distribution of neutron workplace fields. Existing neutron dosimetry instrumentation does not account for this directional distribution, resulting in conservative estimates of dose in neutron workplace fields (by around a factor of 2, although this is heavily dependent on the type of field). This conservatism could influence epidemiological studies on the health effects of radiation exposure. This paper reports on the development of an instrument which can estimate the effective dose of a neutron field, accounting for both the direction and the energy distribution. Methods: A 6Li-loaded scintillator was used to perform neutron assays at a number of locations in a 20 × 20 × 17.5 cm3 water phantom. The variation in thermal and fast neutron response to different energies and field directions was exploited. The modeled response of the instrument to various neutron fields was used to train an artificial neural network (ANN) to learn the effective dose and ambient dose equivalent of these fields. All experimental data published in this work were measured at the National Physical Laboratory (UK). Results: Experimental results were obtained for a number of radionuclide source based neutron fields to test the performance of the system. The results of experimental neutron assays at 25 locations in a water phantom were fed into the trained ANN. A correlation between neutron counting rates in the phantom and neutron fluence rates was experimentally found to provide dose rate estimates. A radionuclide source behind shadow cone was used to create a more complex field in terms of energy and direction. For all fields, the resulting estimates of effective dose rate were within 45% or better of their calculated values, regardless of energy distribution or direction for measurement times greater than 25 min. Conclusions: This work presents a novel, real-time, approach to workplace neutron dosimetry. It is believed that in the research presented in this paper, for the first time, a single instrument has been able to estimate effective dose

Hardware Implementations of Spiking Neural Networks and Artificially Intelligent Systems

Artificial spiking neural networks are gaining increasing prominence due to their potential advantages over traditional, time-static artificial neural networks. Custom hardware implementations of spiking neural networks present many advantages over other implementation mediums. Two main topics are the focus of this work. Firstly, digital hardware implementations of spiking neurons and neuromorphic hardware are explored and presented. These implementations include novel implementations for lowered digital hardware requirements and reduced power consumption. The second section of this work proposes a novel method for selectively adding sparsity to a spiking neural network based on training set images for pattern recognition applications, thereby greatly reducing the inference time required in a digital hardware implementation

High-Throughput Operant Conditioning in Drosophila Larvae

Operant conditioning is the process by which animals learn to associate their own behaviour with positive or negative outcomes, biasing future action selection in order to maximise reward and avoid punishment. It is an important strategy to ensure survival in an ever-changing environment. Although operant conditioning has been observed across vertebrate and invertebrate species, the underlying neural mechanisms are still not fully understood. The Drosophila larva is an excellent model system to study neural circuits, since it is genetically tractable, with a variety of tools available. Although it is quite small, it is capable of a diverse range of behaviours and can achieve complex learning tasks. However, while the mechanisms underlying classical conditioning, where animals learn about the appetitive or aversive qualities of an external sensory cue, have been extensively studied in larvae, it has remained an open question whether they are capable of operant conditioning. This is in part due to the challenges which arise during the training process: in order to train an animal to associate its own actions with their outcomes, the experimenter needs to be able to deliver rewarding or punishing stimuli directly in response to behaviour. In this thesis, I introduce a novel high-throughput tracker suitable for training up to 16 larvae simultaneously. I have developed a customised software for real-time detection of various actions that larvae perform: left and right bend, forward crawl, roll and back-up. Light and heat stimuli can be administered at individual animals with minimal delay, enabling optogenetic or thermogenetic activation of circuits encoding reward or punishment in response to behaviour. Using this system, I show that Drosophila larvae are capable of operant conditioning. Pairing bends to one direction, e.g. the left, with optogenetic activation of a large group of reward-encoding dopaminergic and serotonergic neurons is sufficient to induce a learned preference for bending towards this side after training. I explore whether there are other types of actions which larvae can learn to associate with valence, and introduce a second operant conditioning paradigm, in which larvae modify their behaviour following pairing of the stimulus with forward crawls. To identify new candidate neurons signalling valence in a learning context, I also conduct a classical conditioning screen, in which I pair an odour with optogenetic activation of distinct neuron types covered by different driver lines. While activation of many types of gustatory sensory neurons paired with the odour was insufficient for memory formation, I find that the serotonergic neurons of the brain and the subesophageal zone (SEZ) can induce strong appetitive learning. Finally, I show that activity of serotonergic rather than dopaminergic neurons is sufficient for memory formation in the operant bend direction paradigm, and that operant conditioning is impaired when restricting activation to the serotonergic neurons of the brain and the SEZ. My results suggest a novel role of serotonergic neurons for learning in insects as well as the existence of learning circuits outside of the mushroom body. Different subsets of serotonergic neurons mediate classical and operant conditioning. This works lays a foundation for future studies of the function of serotonin and the mechanisms underlying operant conditioning at both circuit level and cellular level.Gates Cambridge Scholarshi

Forum Bildverarbeitung 2022

Bildverarbeitung verknüpft das Fachgebiet die Sensorik von Kameras – bildgebender Sensorik – mit der Verarbeitung der Sensordaten – den Bildern. Daraus resultiert der besondere Reiz dieser Disziplin. Der vorliegende Tagungsband des „Forums Bildverarbeitung“, das am 24. und 25.11.2022 in Karlsruhe als Veranstaltung des Karlsruher Instituts für Technologie und des Fraunhofer-Instituts für Optronik, Systemtechnik und Bildauswertung stattfand, enthält die Aufsätze der eingegangenen Beiträge

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community