Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Scaling Multidimensional Inference for Big Structured Data

In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications [151]. In a world of increasing sensor modalities, cheaper storage, and more data oriented questions, we are quickly passing the limits of tractable computations using traditional statistical analysis methods. Methods which often show great results on simple data have difficulties processing complicated multidimensional data. Accuracy alone can no longer justify unwarranted memory use and computational complexity. Improving the scaling properties of these methods for multidimensional data is the only way to make these methods relevant. In this work we explore methods for improving the scaling properties of parametric and nonparametric models. Namely, we focus on the structure of the data to lower the complexity of a specific family of problems. The two types of structures considered in this work are distributive optimization with separable constraints (Chapters 2-3), and scaling Gaussian processes for multidimensional lattice input (Chapters 4-5). By improving the scaling of these methods, we can expand their use to a wide range of applications which were previously intractable open the door to new research questions

Noise and vestibular perception of passive self-motion

Noise defined as random disturbances is ubiquitous in both the external environment and the nervous system. Depending on the context, noise can degrade or improve information processing and performance. In all cases, it contributes to neural systems dynamics. We review some effects of various sources of noise on the neural processing of self-motion signals at different stages of the vestibular pathways and the resulting perceptual responses. Hair cells in the inner ear reduce the impact of noise by means of mechanical and neural filtering. Hair cells synapse on regular and irregular afferents. Variability of discharge (noise) is low in regular afferents and high in irregular units. The high variability of irregular units provides information about the envelope of naturalistic head motion stimuli. A subset of neurons in the vestibular nuclei and thalamus are optimally tuned to noisy motion stimuli that reproduce the statistics of naturalistic head movements. In the thalamus, variability of neural discharge increases with increasing motion amplitude but saturates at high amplitudes, accounting for behavioral violation of Weber’s law. In general, the precision of individual vestibular neurons in encoding head motion is worse than the perceptual precision measured behaviorally. However, the global precision predicted by neural population codes matches the high behavioral precision. The latter is estimated by means of psychometric functions for detection or discrimination of whole-body displacements. Vestibular motion thresholds (inverse of precision) reflect the contribution of intrinsic and extrinsic noise to perception. Vestibular motion thresholds tend to deteriorate progressively after the age of 40 years, possibly due to oxidative stress resulting from high discharge rates and metabolic loads of vestibular afferents. In the elderly, vestibular thresholds correlate with postural stability: the higher the threshold, the greater is the postural imbalance and risk of falling. Experimental application of optimal levels of either galvanic noise or whole-body oscillations can ameliorate vestibular function with a mechanism reminiscent of stochastic resonance. Assessment of vestibular thresholds is diagnostic in several types of vestibulopathies, and vestibular stimulation might be useful in vestibular rehabilitation

Characterisation of an Electrolyte-Gated Organic Field-Effect Transistor for the Measurement of Extracellular Potentials

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2021-2022. Tutor/Director: Gabriel Gomila Lluch i Shubham Tanwa

Flipping Biological Switches: Solving for Optimal Control: A Dissertation

Switches play an important regulatory role at all levels of biology, from molecular switches triggering signaling cascades to cellular switches regulating cell maturation and apoptosis. Medical therapies are often designed to toggle a system from one state to another, achieving a specified health outcome. For instance, small doses of subpathologic viruses activate the immune system’s production of antibodies. Electrical stimulation revert cardiac arrhythmias back to normal sinus rhythm. In all of these examples, a major challenge is finding the optimal stimulus waveform necessary to cause the switch to flip. This thesis develops, validates, and applies a novel model-independent stochastic algorithm, the Extrema Distortion Algorithm (EDA), towards finding the optimal stimulus. We validate the EDA’s performance for the Hodgkin-Huxley model (an empirically validated ionic model of neuronal excitability), the FitzHugh-Nagumo model (an abstract model applied to a wide range of biological systems that that exhibit an oscillatory state and a quiescent state), and the genetic toggle switch (a model of bistable gene expression). We show that the EDA is able to not only find the optimal solution, but also in some cases excel beyond the traditional analytic approaches. Finally, we have computed novel optimal stimulus waveforms for aborting epileptic seizures using the EDA in cellular and network models of epilepsy. This work represents a first step in developing a new class of adaptive algorithms and devices that flip biological switches, revealing basic mechanistic insights and therapeutic applications for a broad range of disorders

Computational methods toward early detection of neuronal deterioration

In today's world, because of developments in medical sciences, people are living longer, particularly in the advanced countries. This increasing of the lifespan has caused the prevalence of age-related diseases like Alzheimer’s and dementia. Researches show that ion channel disruptions, especially the formation of permeable pores to cations by Aβ plaques, play an important role in the occurrence of these types of diseases. Therefore, early detection of such diseases, particularly using non-invasive tools can aid both patients and those scientists searching for a cure. To achieve the goal toward early detection, the computational analysis of ion channels, ion imbalances in the presence of Aβ pores in neurons and fault detection is done. Any disruption in the membrane of the neuron, like the formation of permeable pores to cations by Aβ plaques, causes ionic imbalance and, as a result, faults occur in the signalling of the neuron.The first part of this research concentrates on ion channels, ion imbalances and their impacts on the signalling behaviour of the neuron. This includes investigating the role of Aβ channels in the development of neurodegenerative diseases. Results revealed that these types of diseases can lead to ionic imbalances in the neuron. Ion imbalances can change the behaviour of neuronal signalling. Therefore, by identifying the pattern of these changes, the disease can be detected in the very early stages. Then the role of coupling and synchronisation effects in such diseases were studied. After that, a novel method to define minimum requirements for synchronicity between two coupled neurons is proposed. Further, a new computational model of Aβ channels is proposed and developed which mimics the behaviour of a neuron in the course of Alzheimer's disease. Finally, both fault computation and disease detection are carried out using a residual generation method, where the residuals from two observers are compared to assess their performance

Electrical Impedance Based Spectroscopy and Tomography Techniques for Obesity and Heart Diseases

Despite advances in diagnosis and therapy, atherosclerosis cardiovascular disease remains the leading cause of morbidity and mortality. Predicting metabolically active atherosclerotic lesions has remained an unmet clinical need. Specially, atherosclerotic plaques that are prone to rupture are of extremely high-risk and can cause detrimental heart attacks and/or strokes, leading to sudden death. It has been shown that atheroscleroses is correlated to the level of obesity of an individual [1] Usually in clinical practice, the doctor will assess a patient's "risk factor" based on his or her Body Mass Index (BMS), and measurement of the waist circumference. Meanwhile the level of fatty droplet deposits in the liver is an important bio-marker to assess the patient's risk factor, however the patient will need to undergo radiation imaging such as CT scan or MRI scan. For the vulnerable plaques that can lead to sudden rupture, the ability to distinguish them at an early stage remains largely lacking. Therefore it is of great clinical interest to find improved diagnostic techniques to identify and localize such vulnerable plaques. Meanwhile, lipid has significantly lower electrical impedance than the rest of the vessel tissues in certain frequency bands [2]. In this thesis we explore spectroscopic and tomographic methods to characterize such plaques. In addition, with the Electrical Impedance Tomography method we will propose a novel method to detect fatty liver in an early stage with non-radiating and non-invasive manner.</p

Inferring collective dynamical states from widely unobserved systems

When assessing spatially-extended complex systems, one can rarely sample the states of all components. We show that this spatial subsampling typically leads to severe underestimation of the risk of instability in systems with propagating events. We derive a subsampling-invariant estimator, and demonstrate that it correctly infers the infectiousness of various diseases under subsampling, making it particularly useful in countries with unreliable case reports. In neuroscience, recordings are strongly limited by subsampling. Here, the subsampling-invariant estimator allows to revisit two prominent hypotheses about the brain's collective spiking dynamics: asynchronous-irregular or critical. We identify consistently for rat, cat and monkey a state that combines features of both and allows input to reverberate in the network for hundreds of milliseconds. Overall, owing to its ready applicability, the novel estimator paves the way to novel insight for the study of spatially-extended dynamical systems.Comment: 7 pages + 12 pages supplementary information + 7 supplementary figures. Title changed to match journal referenc