A differential memristive synapse circuit for on-line learning in neuromorphic computing systems

Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes. This imposes severe constraints on the length of the pulses transmitted in the network, and on the network's throughput. Furthermore, most of these circuits do not decouple the currents flowing through memristive devices from the one stimulating the target neuron. This can be a problem when using devices with high conductance values, because of the resulting large currents. In this paper we propose a novel circuit that decouples the current produced by the memristive device from the one used to stimulate the post-synaptic neuron, by using a novel differential scheme based on the Gilbert normalizer circuit. We show how this circuit is useful for reducing the effect of variability in the memristive devices, and how it is ideally suited for spike-based learning mechanisms that do not require overlapping pre- and post-synaptic pulses. We demonstrate the features of the proposed synapse circuit with SPICE simulations, and validate its learning properties with high-level behavioral network simulations which use a stochastic gradient descent learning rule in two classification tasks.Comment: 18 Pages main text, 9 pages of supplementary text, 19 figures. Patente

Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses

Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mechanisms that can solve the temporal credit assignment problem and ensure stable network dynamics, even when the weight resolution is limited. These challenges are further accentuated, if one resorts to using memristive devices for in-memory computing to resolve the von-Neumann bottleneck problem, at the expense of a substantial increase in variability in both the computation and the working memory of the spiking RNNs. To address these challenges and enable online learning in memristive neuromorphic RNNs, we present a simulation framework of differential-architecture crossbar arrays based on an accurate and comprehensive Phase-Change Memory (PCM) device model. We train a spiking RNN whose weights are emulated in the presented simulation framework, using a recently proposed e-prop learning rule. Although e-prop locally approximates the ideal synaptic updates, it is difficult to implement the updates on the memristive substrate due to substantial PCM non-idealities. We compare several widely adapted weight update schemes that primarily aim to cope with these device non-idealities and demonstrate that accumulating gradients can enable online and efficient training of spiking RNN on memristive substrates

Adaptive extreme edge computing for wearable devices

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions towards smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g. memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices

High speed event-based visual processing in the presence of noise

Standard machine vision approaches are challenged in applications where large amounts of noisy temporal data must be processed in real-time. This work aims to develop neuromorphic event-based processing systems for such challenging, high-noise environments. The novel event-based application-focused algorithms developed are primarily designed for implementation in digital neuromorphic hardware with a focus on noise robustness, ease of implementation, operationally useful ancillary signals and processing speed in embedded systems

Process control of a laboratory combustor using neural networks

Active feedback and feedforward-feedback control systems based on static-trained feedforward multi-layer-perceptron (FMLP) neural networks were designed and demonstrated, by experiment and simulation, for selected species from a laboratory two stage combustor. These virtual controllers functioned through a Visual Basic platform. A proportional neural network controller (PNNC) was developed for a monotonic control problem - the variation of outlet oxygen level with overall equivalence ratio (Φ0). The FMLP neural network maps the control variable to the manipulated variable. This information is in turn transferred to a proportional controller, through the variable control bias value. The proposed feedback control methodology is robust and effective to improve control performance of the conventional control system without drastic changes in the control structure. A detailed case study in which two clusters of FMLP neural networks were applied to a non-monotonic control problem - the variation of outlet nitric oxide level with first-stage equivalence ratio (Φ0) - was demonstrated. The two clusters were used in the feedforward-feedback control scheme. The key novelty is the functionalities of these two network clusters. The first cluster is a neural network-based model-predictive controller (NMPC). It identifies the process disturbance and adjusts the manipulated variables. The second cluster is a neural network-based Smith time-delay compensator (NSTC) and is used to reduce the impact of the long sampling/analysis lags in the process. Unlike other neural network controllers reported in the control field, NMPC and NSTC are efficiently simple in terms of the network structure and training algorithm. With the pre-filtered steady-state training data, the neural networks converged rapidly. The network transient response was originally designed and enabled here using additional tools \u27and mathematical functions in the Visual Basic program. The controller based on NMPC/NSTC showed a superior performance over the conventional proportional-integral derivative (PID) controller. The control systems developed in this study are not limited to the combustion process. With sufficient steady-state training data, the proposed control systems can be applied to control applications in other engineering fields

Interdisciplinary study of the antidepressant effect of dexmecamylamine: a meta-analysis and computational study

Nicotinic acetylcholine receptors (nAChRs) are widespread ligand-gated ion channels in the human brain, playing crucial roles in various physiological processes such as hormone secretion, learning, and pain perception. These receptors have been identified as potential therapeutic targets for mental health disorders, particularly major depressive disorder (MDD). Despite extensive preclinical and clinical investigations of nAChR agonists, positive allosteric modulators, and antagonists, no antidepressant drug targeting nAChRs has been successfully marketed. Given the substantial unmet need for MDD treatment, alternative compounds with distinct mechanisms of action, such as those targeting nAChRs, warrant further exploration. Dexmecamylamine is one such compound, and it is the dextrorotatory enantiomer of mecamylamine. Dexmecamylamine has demonstrated significant antidepressant-like effects in multiple animal studies. Nevertheless, human clinical trials have yielded conflicting results regarding its antidepressant efficacy. In this study, we employed a meta-analysis to assess the antidepressant-like effect of dexmecamylamine. After conducting an exhaustive literature search, we identified nine high-quality randomized controlled trials (RCTs) eligible for inclusion in the meta-analysis. Our analysis aimed to evaluate dexmecamylamine's efficacy as an adjunct therapy for MDD treatment. The results indicated that dexmecamylamine did not demonstrate superior efficacy compared to placebo in terms of the Hamilton Depression Scale-17 score change [mean difference = 0.70 (95% CI = -0.24 to 1.64)], the Montgomery-Asberg Depression Rating Scale score change [-0.52 (95% CI=-0.15 to -0.02)] and other secondary endpoints

Biomarkers of Nutrition for Development (BOND)—Iron Review

This is the fifth in the series of reviews developed as part of the Biomarkers of Nutrition for Development (BOND) program. The BOND Iron Expert Panel (I-EP) reviewed the extant knowledge regarding iron biology, public health implications, and the relative usefulness of currently available biomarkers of iron status from deficiency to overload. Approaches to assessing intake, including bioavailability, are also covered. The report also covers technical and laboratory considerations for the use of available biomarkers of iron status, and concludes with a description of research priorities along with a brief discussion of new biomarkers with potential for use across the spectrum of activities related to the study of iron in human health. The I-EP concluded that current iron biomarkers are reliable for accurately assessing many aspects of iron nutrition. However, a clear distinction is made between the relative strengths of biomarkers to assess hematological consequences of iron deficiency versus other putative functional outcomes, particularly the relationship between maternal and fetal iron status during pregnancy, birth outcomes, and infant cognitive, motor and emotional development. The I-EP also highlighted the importance of considering the confounding effects of inflammation and infection on the interpretation of iron biomarker results, as well as the impact of life stage. Finally, alternative approaches to the evaluation of the risk for nutritional iron overload at the population level are presented, because the currently designated upper limits for the biomarker generally employed (serum ferritin) may not differentiate between true iron overload and the effects of subclinical inflammation

In Pursuit of Clarity:Understanding the biology of schizophrenia by functional investigations and integrative genomic data analyses

Schizophrenia is a complex and heterogenous illness for which the underlying biology is largely unknown. Using functional investigations and integrative genomic data analyses, this thesis sheds light on the biological causes and consequences of schizophrenia

Developing preclinical devices for neuroscience research in the fields of animal tracking, fMRI acquisition, and 3D histology cutting

[ES] La neurociencia es un campo que abarca muchas especialidades. El objetivo de esta tesis es subsanar algunas carencias tecnológicas que existen en los métodos actuales de experimentación animal en neurociencia. En esta tesis, se presentan seis proyectos, que tendrán como objetivo mejorar el "Principio de las tres R", el cual fue enunciado por los biólogos ingleses W. M. S. Russell y R. L. Burch, durante la experimentación animal. El comportamiento es uno de los aspectos más importantes de la vida animal. Depende de los vínculos entre los animales, sus sistemas nerviosos y sus entornos. Para estudiar el comportamiento de los animales de laboratorio, se necesitan varias herramientas, pero una herramienta de seguimiento es esencial para llevar a cabo un estudio de comportamiento exhaustivo. Varias herramientas de seguimiento visual están actualmente disponibles. Sin embargo, todas tienen algunos inconvenientes. Por ejemplo, en una situación en la que un animal está dentro de una madriguera o cerca de otros animales, las cámaras de rastreo (tracking) no siempre pueden detectar la ubicación precisa o el movimiento del animal. Por esta razón, los entornos enriquecidos para intentar recrear el hábitat natural de los animales en experimentación no pueden utilizarse, ya que los datos recopilados son insuficientes/inexactos. Con la finalidad de mejorar los experimentos de tracking RFID Assisted Tracking Tile (RATT) es presentado en esta tesis. RATT es un sistema de seguimiento basado en tecnología de identificación pasiva de radiofrecuencia (RFID) y está compuesto por baldosas electrónicas con las que se puede construir una gran superficie, sobre la cual los animales pueden moverse libremente. Esto permite la identificación más precisa de los animales, así como el seguimiento de sus movimientos. Este sistema, que también se puede combinar con un sistema de seguimiento con cámaras, allana el camino para estudios completos de comportamiento en entornos enriquecidos. Dada la capacidad de rastrear animales y, por lo tanto, realizar experimentos de comportamiento exhaustivos, es posible observar cómo se comportan los sujetos desde un punto de vista externo. Sin embargo, si queremos comprender lo que sucede en el cerebro de estos sujetos, es necesario aplicar otras técnicas de análisis, por ejemplo, el estudio de señales dependientes del nivel de oxígeno en la sangre (BOLD, por sus siglas en inglés). Las señales BOLD se basan en las respuestas vasculares a la activación neuronal y se utilizan ampliamente en estudios de investigación clínicos y preclínicos. En entornos preclínicos, los animales suelen ser anestesiados. Sin embargo, los anestésicos causan cambios en la fisiología de los animales, p. Ej. hipotermia, y esto tiene el potencial de alterar las señales funcionales de MRI (fMRI). Para evitar la hipotermia en roedores anestesiados, se presenta TherMouseDuino. Este es un sistema de control automático de temperatura de código abierto, que reduce las fluctuaciones de la temperatura, lo que proporciona condiciones sólidas para realizar experimentos de resonancia magnética funcional. En los cursos de biología y neurociencia, la anatomía del cerebro se enseña generalmente utilizando imágenes de resonancia magnética (IRM) o secciones histológicas de diferentes planos. Estos muestran las áreas macroscópicas más importantes en el cerebro de un animal. Sin embargo, este método no es dinámico ni intuitivo. En esta tesis se presenta un cerebro de rata impreso en 3D con fines educativos. La manipulación manual de la estructura, facilitada por la ampliación de sus dimensiones, junto con la capacidad de desmontar el "cerebro" en algunas de sus partes principales, facilita la comprensión de la organización 3D del sistema nervioso. Este es un método alternativo y mejorado para enseñar a los estudiantes en general y a los biólogos, en particular, la anatomía del cerebro de rata.[CA] La neurociència és un camp que abasta moltes especialitats. L'objectiu d'aquesta tesi és esmenar algunes manques tecnològiques que existeixen en els mètodes actuals d'experimentació animal en neurociència. En aquesta tesi, es presenten sis projectes, que tindran com a objectiu millorar el "Principi de les tres R", el qual va ser enunciat pels biòlegs anglesos W. M. S. Russell i R. L. Burch, durant l'experimentació animal. El comportament és un dels aspectes m'és importants de la vida animal. Depèn dels vincles entre els animals, els seus sistemes nerviosos i els seus entorns. Per estudiar el comportament dels animals de laboratori, es necessiten diverses eines, però` una eina de seguiment és essencial per a dur a terme un estudi de comportament exhaustiu. Diverses eines de seguiment visual estan actualment disponibles. No obstant això, totes tenen alguns inconvenients. Per exemple, en una situació en la qual un animal esta` dins d'un cau o prop d'altres animals, les cambres de rastreig (tracking) no sempre poden detectar la ubicació precisa o el moviment de l'animal. Per aquesta raó, els entorns enriquits per a intentar recrear l'hàbitat natural dels animals en experimentació no poden utilitzar-se, ja que les dades recopilades són insuficients/inexactes. Amb la finalitat de millorar els experiments de tracking/seguiment RFID Assisted Tracking Tile (RATT) és presentat en aquesta tesi. RATT es un sistema de seguiment basat en tecnologia d'identificació passiva de radiofreqüència (RFID) i esta` compost per rajoles electròniques amb les quals es pot construir una gran superfície, sobre la qual els animals poden moures lliurement. Això permet la identificació més precisa dels animals, així com el seguiment dels seus moviments. Aquest sistema, que també es pot combinar amb un sistema de seguiment amb cambres, aplana el camí per a estudis complets de comportament en entorns enriquits. Donada la capacitat de rastrejar animals i, per tant, realitzar experiments de comportament exhaustius, és possible observar com es comporten els subjectes des d'un punt de vista extern. No obstant això, si volem comprendre el que succeeix en el cervell d'aquests subjectes, és necessari aplicar altres tècniques d'anàlisis, per exemple, l'estudi de senyals dependents del nivell d'oxigen en la sang (BOLD, per les seues sigles en anglès). Els senyals BOLD es basen en les respostes vasculars a l'activació neuronal i s'utilitzen àmpliament en estudis d'investigació clínics i preclínics. En entorns preclínics, els animals solen ser anestesiats. No obstant això, els anestèsics causen canvis en la fisiologia de els animals, per exemple hipotèrmia, i això te el potencial d'alterar els senyals funcionals de MRI (fMRI). Per a evitar la hipotèrmia en rosegadors anestesiats, es presenta TherMouseDuino. Aquest és un sistema de control automàtic de temperatura de codi obert, que redueix les fluctuacions de la temperatura, la qual cosa proporciona condicions solides per a realitzar experiments de ressonància magnètica funcional. En els cursos de biologia i neurociència, l'anatomia del cervell s'ensenya generalment utilitzant imatges de ressonància magnètica (IRM) o seccions histològiques de diferents plans. Aquests mostren les àrees macroscòpiques més importants en el cervell de un animal. No obstant això, aquest mètode no és dinàmic ni intuïtiu. En aquesta tesi es presenta un cervell de rata imprès en 3D amb finalitats educatius. La manipulació manual de l'estructura, facilitada per l'ampliació de les seues dimensions, juntament amb la capacitat de desmuntar el "cervell" en algunes de les seues parts principals, facilita la comprensió de l'organització 3D del sistema nerviós. Aquest és un mètode alternatiu i millorat per a ensenyar a els estudiants en general i als biòlegs, en particular, l'anatomia del cervell de rata.[EN] Neuroscience is a field that covers many specialties. The objective of this thesis is to correct some technological deficiencies that exist in current methods of animal experimentation in neuroscience. In this thesis, six projects are presented, which will aim to improve the "Principle of the three Rs" in animal experimentation enunciated by the English biologists W. M. S. Russell and R. L. Burch. In the present era of impressive progress in neuroscience, it is still not arguable that a complete understanding of the brain cannot be possible without a comparable understanding of animal behavior. In order to study the behavior of laboratory animals, various tools are needed, being a reliable tracking system one of the most important to follow large populations of individual subjects that interact in complex manners. Several visual tracking tools are currently available. However, they all have some drawbacks. For example, in a situation where an animal is inside a cave, or is in close proximity to other animals, tracking cameras cannot always detect the precise location or movement of the animal. For this reason, environments that have been enriched in order to attempt to recreate the natural habitat of the animals under experiment, cannot be used, as the data gathered is insufficient/inaccurate. In order to improve the current tracking systems , the RATT is presented. RATT is a tracking system based on passive RFID technology and it is composed of electronic tiles. Using several tiles, a large surface area, on which the animals can move freely, can be built. This enables the more accurate identification of the animals, as well as the tracking of their movements. This system, which can also be combined with a visual tracking system, paves the way for complete behavioral studies in enriched environments. Given the ability to track animals and thus conduct thorough behavioral experiments, it is possible to observe how the subjects behave from an external viewpoint. However, if we want to understand what is going on in the brains of these subjects, it is necessary to apply other analysis techniques, for example the study of BOLD signals. BOLD signals are based on vascular responses to neuronal activation and are used extensively in clinical and preclinical research studies. In preclinical settings, animals are usually anesthetized. However, anesthetics cause changes in the physiology of the animals, e.g. hypothermia, and this has the potential to disrupt fMRI signals. In order to avoid hypothermia in anesthetized rodents, TherMouseDuino is presented. This is an Open-Source automatic temperature control system, which reduces temperature fluctuations, thus providing robust conditions in which to perform fMRI experiments. In biology and neuroscience courses, brain anatomy is generally taught using MRI or histological sections of different planes. These show the most important macroscopic areas in an animals' brain. However, this method is neither dynamic nor intuitive. An anatomical 3D printed rat brain with educative purposes is presented in this thesis. Hand manipulation of the structure, facilitated by the scaling up of its dimensions, together with the ability to dismantle the "brain" into some of main its constituent parts, facilitates the understanding of the 3D organization of the nervous system. This is an alternative and improved method for teaching students in general and biologists, in particular, the rat brain anatomy.This work was supported in part by the Spanish Ministerio de Economía y Competitividad (MINECO) and FEDER funds under grants BFU2015-64380-C2-2-R (D.M.) and BFU2015-64380-C2-1-R, by EU Horizon 2020 Program 668863-SyBil-AA grant (S.C.). S.C. acknowledges financial support from the Spanish State Research Agency, through the “Severo Ochoa” Programme for Centres of Excellence in R&D (ref. SEV-2013-0317) and by a grant “Ayudas para la formación de personal investigador (FPI)” from the Vicerrectorado de Investigación, Innovación y Transferencia of the Universitat Politècnica de València.Quiñones Colomer, DR. (2019). Developing preclinical devices for neuroscience research in the fields of animal tracking, fMRI acquisition, and 3D histology cutting [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/118795TESI