The Effects of Radiation on Memristor-Based Electronic Spiking Neural Networks

In this dissertation, memristor-based spiking neural networks (SNNs) are used to analyze the effect of radiation on the spatio-temporal pattern recognition (STPR) capability of the networks. Two-terminal resistive memory devices (memristors) are used as synapses to manipulate conductivity paths in the network. Spike-timing-dependent plasticity (STDP) learning behavior results in pattern learning and is achieved using biphasic shaped pre- and post-synaptic spikes. A TiO2 based non-linear drift memristor model designed in Verilog-A implements synaptic behavior and is modified to include experimentally observed effects of state-altering, ionizing, and off-state degradation radiation on the device. The impact of neuron “death” (disabled neuron circuits) due to radiation is also examined. In general, radiation interaction events distort the STDP learning curve undesirably, favoring synaptic potentiation. At lower short-term flux, the network is able to recover and relearn the pattern with consistent training, although some pixels may be affected due to stability issues. As the radiation flux and duration increases, it can overwhelm the leaky integrate-and-fire (LIF) post-synaptic neuron circuit, and the network does not learn the pattern. On the other hand, in the absence of the pattern, the radiation effects cumulate, and the system never regains stability. Neuron-death simulation results emphasize the importance of non-participating neurons during the learning process, concluding that non-participating afferents contribute to improving the learning ability of the neural network. Instantaneous neuron death proves to be more detrimental for the network compared to when the afferents die over time thus, retaining the network’s pattern learning capability

Flexible Electronics for Neurological Electronic Skin with Multiple Sensing Modalities

The evolution of electronic skin (E-skin) technology in the past decade has resulted in a great variety of flexible electronic devices that mimic the physical and chemical sensing properties of skin for applications in advanced robotics, prosthetics, and health monitoring technologies. The further advancement of E-skin technology demands closer imitation of skin receptors\u27 transduction mechanisms, simultaneous detection of multiple information from different sources, and the study of transmission, processing and memory of the signals among the neurons. Motivated by such demands, this thesis focuses on design, fabrication, characterization of novel flexible electronic devices and integration of individual devices to realize prototype biomimetic E-skin with neurological and multimodal sensing functions. More specifically, we have studied flexible carbon nanotube thin-film transistors (CNT-TFTs) as control and signal processing units of E-skin and flexible ferroelectret nanogenerator (FENG) and triboelectric nanogenerator (TENG) as skin mechanoreceptors. Multiple fabrication methods, such as low-cost printing and conventional cleanroom-based microfabrication have been implemented to fabricate flexible CNT-TFTs with different structures and functions, especially the synaptic functions. Based on the research on individual devices, we have demonstrated a prototype force-sensing flexible neurological E-skin and its sensory nerve and synapse, with FENG serving as the sensory mechanoreceptor that generates action potentials (pulsed voltages) to be processed and transmitted by the flexible synaptic CNT-TFT. It allows for instantaneous detection of force stimuli and offers biological synapse-like behavior to store the stimulus information and relay the stimulus signals to the next stage. The force-sensing neurological E-skin was further augmented with visual and auditory sensing modalities by introducing phototransistor-based optical sensor and FENG-based acoustic sensor. Successful transduction of visual, auditory and tactile stimuli and synaptic processing and memory of those signals have all been demonstrated. Thanks to the multimodal sensing capability of the neurological E-skin, psychological associative learning experiment-“Pavlov’s dog\u27s experiment”, was also successfully implemented electronically by synergizing actual visual and auditory signals in the synaptic transistor. Flexible electronics and prototype neurological E-skin system demonstrated in this thesis may offer an entry into novel multimodal, user-environment interactive soft E-skin system for soft robotic and diagnostic applications

Neuromorphic systems based on memristive devices - From the material science perspective to bio-inspired learning hardware

Hardware computation is facing in the present age a deep transformation of its own paradigms. Silicon based computation is reaching its limit due to the physical constraints of transistor technology. As predicted by the Moore’s law, downscaling of transistor dimensions doubled each year since the 60s, leading nowadays to the extreme of 16-nm channel width of the present state-of-the-art technology. No further improvement is possible, since laws of physics impose a different electrical behavior when lower dimensions are attempted. Multiple solutions are then envisaged, spanning the range from quantum computing to neuromorphic computing. The present dissertation wants to be a preliminary study for understanding the opportunities enabled by neuromorphic computing based on resistive switching memories. In particular, brain inspires technology and architecture of new generation processors because of its unique properties: parallel and distributed computation, superposition of processing and memory unit, low power consumption, to cite only some of them. Such features make brain particularly efficient and robust against degraded data, further than particularly suitable to process and store in memory new nformation. Despite many research projects and some commercial products are already proposing brain-like computing processors, like spiNNaker or IBM’s Bluenorth, they only mimic the brain functioning with standard Silicon technology, that is inherently serial and distinguish between processing and memory unit. Resistive switching technology on the other hand, would allow to overcome many of these issues, enabling a far better match between biological and artificial neuromorphic computation. Resistive switching are, generally speaking, Metal-Insulator-Metal structures able to change their electrical conductance as a consequence of the history of applied electric signal. In such sense, they behave exactly as synapses do in a biological neural networks. For this reason, resistive switching when modeled as memristor, i.e. memory-resistor, can act as artificial synapses and, moreover, are particularly suitable to be interfaced with artificial Silicon neurons that are designed to replicate the biological behavior when excited with electric pulses. Anyhow, from the technological standpoint, there is still no standard on the design and fabrication of resistive switching, so that multiple structure and materials are investigated. In this dissertation, it is reported an analysis of multiple resistive switching devices, based on various materials, i.e. TiO2, ZnO and HfO, and device architectures, i.e. thin film and nanostructured devices, with the scope of both characterizing and comprehending the physics behind resistive switching phenomena. Furthermore, numerical simulations of artificial spiking neural networks, embedding Silicon neurons and HfO-based resistive switching are designed and performed, in order to give a systematic analysis of the performances reached by this new kind of computing paradigm

Modulating the processing of complex sounds: How inhibition of nitric oxide alters evoked responses in the bullfrog torus semicircularis

Nitric Oxide (NO) is a gaseous molecule that functions as a retrograde messenger in several regions of the brain. Activation of glutamate N-methyl-D-aspartate (NMDA) receptors stimulates NO production via the activity of nitric oxide synthase (NOS). NO is released and subsequently enhances the presynaptic release of glutamate. Staining for β nicotinamide adenine dinucleotide phosphate diaphorase, an indicator of NO production, as well as immunohistochemical studies have revealed the presence of NOS-labeled neurons in a number of vertebrate brain structures including the inferior colliculus (IC), an important auditory processing center. These neurons presumably produce and release NO. However, the function of nitric oxide in auditory processing at the level of the IC is not known. Here we address this issue using extracellular single-unit recording combined with microiontophoresis to investigate the role of NO in how different species calls are analyzed by neurons in the IC of the American bullfrog, Lithobates catesbeiana. Of particular interest was if NO modulates the responses of IC neurons to conspecific and heterospecific mating calls. In vivo iontophoretic application of L-NAME (a NOS inhibitor), and L-Arg (a NOS substrate), was used to evaluate the effect of NO on the sound-evoked responses of neurons (n=35) in the IC. We found that NO modulated neuronal responses in a call dependent manner. Upon application of L-NAME we observed changes in neuronal responses with respect to spike counts, first-spike response latencies, and interspike intervals. Recovery of original response was seen after application of the NOS substrate, L-Arg after cessation of L-NAME application. Our data suggest a role for NO in regulating both gain control and response selectivity in the IC which may influence the output of neural circuits engaged in the analysis of behaviorally relevant acoustic signals, such as speech

Noninvasive brain stimulation techniques can modulate cognitive processing

Recent methods that allow a noninvasive modulation of brain activity are able to modulate human cognitive behavior. Among these methods are transcranial electric stimulation and transcranial magnetic stimulation that both come in multiple variants. A property of both types of brain stimulation is that they modulate brain activity and in turn modulate cognitive behavior. Here, we describe the methods with their assumed neural mechanisms for readers from the economic and social sciences and little prior knowledge of these techniques. Our emphasis is on available protocols and experimental parameters to choose from when designing a study. We also review a selection of recent studies that have successfully applied them in the respective field. We provide short pointers to limitations that need to be considered and refer to the relevant papers where appropriate