Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page

Analysis of EEG signals using complex brain networks

The human brain is so complex that two mega projects, the Human Brain Project and the BRAIN Initiative project, are under way in the hope of answering important questions for peoples' health and wellbeing. Complex networks become powerful tools for studying brain function due to the fact that network topologies on real-world systems share small world properties. Examples of these networks are the Internet, biological networks, social networks, climate networks and complex brain networks. Complex brain networks in real time biomedical signal processing applications are limited because some graph algorithms (such as graph isomorphism), cannot be solved in polynomial time. In addition, they are hard to use in single-channel EEG applications, such as clinic applications in sleep scoring and depth of anaesthesia monitoring. The first contribution of this research is to present two novel algorithms and two graph models. A fast weighted horizontal visibility algorithm (FWHVA) overcoming the speed limitations for constructing a graph from a time series is presented. Experimental results show that the FWHVA can be 3.8 times faster than the Fast Fourier Transfer (FFT) algorithm when input signals exceed 4000 data points. A linear time graph isomorphism algorithm (HVGI) can determine the isomorphism of two horizontal visibility graphs (HVGs) in a linear time domain. This is an efficient way to measure the synchronized index between two time series. Difference visibility graphs (DVGs) inherit the advantages of horizontal visibility graphs. They are noise-robust, and they overcome a pitfall of visibility graphs (VG): that the degree distribution (DD) doesn't satisfy a pure power-law. Jump visibility graphs (JVGs) enhance brain graphs allowing the processing of non-stationary biomedical signals. This research shows that the DD of JVGs always satisfies a power-lower if the input signals are purely non-stationary. The second highlight of this work is the study of three clinical biomedical signals: alcoholic, epileptic and sleep EEGs. Based on a synchronization likelihood and maximal weighted matching method, this work finds that the processing repeated stimuli and unrepeated stimuli in the controlled drinkers is larger than that in the alcoholics. Seizure detections based on epileptic EEGs have also been investigated with three graph features: graph entropy of VGs, mean strength of HVGs, and mean degrees of JVGs. All of these features can achieve 100% accuracy in seizure identification and differentiation from healthy EEG signals. Sleep EEGs are evaluated based on VG and DVG methods. It is shown that the complex brain networks exhibit more small world structure during deep sleep. Based on DVG methods, the accuracy peaks at 88:9% in a 5-state sleep stage classification from 14; 943 segments from single-channel EEGs. This study also introduces two weighted complex network approaches to analyse the nonlinear EEG signals. A weighted horizontal visibility graph (WHVG) is proposed to enhance noise-robustness properties. Tested with two Chaos signals and an epileptic EEG database, the research shows that the mean strength of the WHVG is more stable and noise-robust than those features from FFT and entropy. Maximal weighted matching algorithms have been applied to evaluate the difference in complex brain networks of alcoholics and controlled drinkers. The last contribution of this dissertation is to develop an unsupervised classifier for biomedical signal pattern recognition. A Multi-Scale Means (MSK-Means) algorithm is proposed for solving the subject-dependent biomedical signals classification issue. Using JVG features from the epileptic EEG database, the MSK-Means algorithm is 4:7% higher in identifying seizures than those by the K-means algorithm and achieves 92:3% accuracy for localizing the epileptogenic zone. The findings suggest that the outcome of this thesis can improve the performance of complex brain networks for biomedical signal processing and nonlinear time series analysis

Sistema de predicción epileptogenica en lazo cerrado basado en matrices sub-durales

The human brain is the most complex organ in the human body, which consists of approximately 100 billion neurons. These cells effortlessly communicate over multiple hemispheres to deliver our everyday sensorimotor and cognitive abilities. Although the underlying principles of neuronal communication are not well understood, there is evidence to suggest precise synchronisation and/or de-synchronisation of neuronal clusters could play an important role. Furthermore, new evidence suggests that these patterns of synchronisation could be used as an identifier for the detection of a variety of neurological disorders including, Alzheimers (AD), Schizophrenia (SZ) and Epilepsy (EP), where neural degradation or hyper synchronous networks have been detected. Over the years many different techniques have been proposed for the detection of synchronisation patterns, in the form of spectral analysis, transform approaches and statistical based studies. Nonetheless, most are confined to software based implementations as opposed to hardware realisations due to their complexity. Furthermore, the few hardware implementations which do exist, suffer from a lack of scalability, in terms of brain area coverage, throughput and power consumption. Here we introduce the design and implementation of a hardware efficient algorithm, named Delay Difference Analysis (DDA), for the identification of patient specific synchronisation patterns. The design is remarkably hardware friendly when compared with other algorithms. In fact, we can reduce hardware requirements by as much as 80% and power consumption as much as 90%, when compared with the most common techniques. In terms of absolute sensitivity the DDA produces an average sensitivity of more than 80% for a false positive rate of 0.75 FP/h and indeed up to a maximum of 90% for confidence levels of 95%. This thesis presents two integer-based digital processors for the calculation of phase synchronisation between neural signals. It is based on the measurement of time periods between two consecutive minima. The simplicity of the approach allows for the use of elementary digital blocks, such as registers, counters or adders. In fact, the first introduced processor was fabricated in a 0.18μm CMOS process and only occupies 0.05mm2 and consumes 15nW from a 0.5V supply voltage at a signal input rate of 1024S/s. These low-area and low-power features make the proposed circuit a valuable computing element in closed-loop neural prosthesis for the treatment of neural disorders, such as epilepsy, or for measuring functional connectivity maps between different recording sites in the brain. A second VLSI implementation was designed and integrated as a mass integrated 16-channel design. Incorporated into the design were 16 individual synchronisation processors (15 on-line processors and 1 test processor) each with a dedicated training and calculation module, used to build a specialised epileptic detection system based on patient specific synchrony thresholds. Each of the main processors are capable of calculating the phase synchrony between 9 independent electroencephalography (EEG) signals over 8 epochs of time totalling 120 EEG combinations. Remarkably, the entire circuit occupies a total area of only 3.64 mm2. This design was implemented with a multi-purpose focus in mind. Firstly, as a clinical aid to help physicians detect pathological brain states, where the small area would allow the patient to wear the device for home trials. Moreover, the small power consumption would allow to run from standard batteries for long periods. The trials could produce important patient specific information which could be processed using mathematical tools such as graph theory. Secondly, the design was focused towards the use as an in-vivo device to detect phase synchrony in real time for patients who suffer with such neurological disorders as EP, which need constant monitoring and feedback. In future developments this synchronisation device would make an good contribution to a full system on chip device for detection and stimulation.El cerebro humano es el órgano más complejo del cuerpo humano, que consta de aproximadamente 100 mil millones de neuronas. Estas células se comunican sin esfuerzo a través de ambos hemisferios para favorecer nuestras habilidades sensoriales y cognitivas diarias. Si bien los principios subyacentes de la comunicación neuronal no se comprenden bien, existen pruebas que sugieren que la sincronización precisa y/o la desincronización de los grupos neuronales podrían desempeñar un papel importante. Además, nuevas evidencias sugieren que estos patrones de sincronización podrían usarse como un identificador para la detección de una gran variedad de trastornos neurológicos incluyendo la enfermedad de Alzheimer(AD), la esquizofrenia(SZ) y la epilepsia(EP), donde se ha detectado la degradación neural o las redes hiper sincrónicas. A lo largo de los años, se han propuesto muchas técnicas diferentes para la detección de patrones de sincronización en forma de análisis espectral, enfoques de transformación y análisis estadísticos. No obstante, la mayoría se limita a implementaciones basadas en software en lugar de realizaciones de hardware debido a su complejidad. Además, las pocas implementaciones de hardware que existen, sufren una falta de escalabilidad, en términos de cobertura del área del cerebro, rendimiento y consumo de energía. Aquí presentamos el diseño y la implementación de un algoritmo eficiente de hardware llamado “Delay Difference Aproximation” (DDA) para la identificación de patrones de sincronización específicos del paciente. El diseño es notablemente compatible con el hardware en comparación con otros algoritmos. De hecho, podemos reducir los requisitos de hardware hasta en un 80% y el consumo de energía hasta en un 90%, en comparación con las técnicas más comunes. En términos de sensibilidad absoluta, la DDA produce una sensibilidad promedio de más del 80% para una tasa de falsos positivos de 0,75 PF / hr y hasta un máximo del 90% para niveles de confianza del 95%. Esta tesis presenta dos procesadores digitales para el cálculo de la sincronización de fase entre señales neuronales. Se basa en la medición de los períodos de tiempo entre dos mínimos consecutivos. La simplicidad del enfoque permite el uso de bloques digitales elementales, como registros, contadores o sumadores. De hecho, el primer procesador introducido se fabricó en un proceso CMOS de 0.18μm y solo ocupa 0.05mm2 y consume 15nW de un voltaje de suministro de 0.5V a una tasa de entrada de señal de 1024S/s Estas características de baja área y baja potencia hacen que el procesador propuesto sea un valioso elemento informático en prótesis neurales de circuito cerrado para el tratamiento de trastornos neuronales, como la epilepsia, o para medir mapas de conectividad funcional entre diferentes sitios de registro en el cerebro. Además, se diseñó una segunda implementación VLSI que se integró como un diseño de 16 canales integrado en masa. Se incorporaron al diseño 16 procesadores de sincronización individuales (15 procesadores en línea y 1 procesador de prueba), cada uno con un módulo de entrenamiento y cálculo dedicado, utilizado para construir un sistema de detección epiléptico especializado basado en umbrales de sincronía específicos del paciente. Cada uno de los procesadores principales es capaz de calcular la sincronización de fase entre 9 señales de electroencefalografía (EEG) independientes en 8 épocas de tiempo que totalizan 120 combinaciones de EEG. Cabe destacar que todo el circuito ocupa un área total de solo 3.64 mm2. Este diseño fue implementado teniendo en mente varios propósitos. En primer lugar, como ayuda clínica para ayudar a los médicos a detectar estados cerebrales patológicos, donde el área pequeña permitiría al paciente usar el dispositivo para las pruebas caseras. Además, el pequeño consumo de energía permitiría una carga cero del dispositivo, lo que le permitiría funcionar con baterías estándar durante largos períodos. Los ensayos podrían producir información importante específica para el paciente que podría procesarse utilizando herramientas matemáticas como la teoría de grafos. En segundo lugar, el diseño se centró en el uso como un dispositivo in-vivo para detectar la sincronización de fase en tiempo real para pacientes que sufren trastornos neurológicos como el EP, que necesitan supervisión y retroalimentación constantes. En desarrollos futuros, este dispositivo de sincronización sería una buena base para desarrollar un sistema completo de un dispositivo chip para detección de trastornos neurológicos

Uncovering the Correlation between COVID-19 and Neurodegenerative Processes: Toward a New Approach Based on EEG Entropic Analysis

COVID-19 is an ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although it primarily attacks the respiratory tract, inflammation can also affect the central nervous system (CNS), leading to chemo-sensory deficits such as anosmia and serious cognitive problems. Recent studies have shown a connection between COVID-19 and neurodegenerative diseases, particularly Alzheimer’s disease (AD). In fact, AD appears to exhibit neurological mechanisms of protein interactions similar to those that occur during COVID-19. Starting from these considerations, this perspective paper outlines a new approach based on the analysis of the complexity of brain signals to identify and quantify common features between COVID-19 and neurodegenerative disorders. Considering the relation between olfactory deficits, AD, and COVID-19, we present an experimental design involving olfactory tasks using multiscale fuzzy entropy (MFE) for electroencephalographic (EEG) signal analysis. Additionally, we present the open challenges and future perspectives. More specifically, the challenges are related to the lack of clinical standards regarding EEG signal entropy and public data that can be exploited in the experimental phase. Furthermore, the integration of EEG analysis with machine learning still requires further investigatio

Neurophysiological correlates underlying social behavioural adjustment of conformity

[eng] Conformity is the act of changing one’s behaviour to adjust to other human beings. It is a crucial social adaptation that happens when people cooperate, where one sacrifices their own perception, expectations, or beliefs to reach convergence with another person. The aim of the present study was to increase the understanding of the neurophysiological underpinnings regarding the social behavioural adjustment of conformity. We start by introducing cooperation and how it is ingrained in human behaviour. Then we explore the different processes that the brain requires for the social behavioural adjustment of conformity. To engage in this social adaptation, a person needs a self-referenced learning mechanism based on a predictive model that helps them track the prediction errors from unexpected events. Also, the brain uses its monitoring and control systems to encode different value functions used in action selection. The use of different learning models in neuroscience, such as reinforcement learning (RL) algorithms, has been a success story identifying learning systems by means of the mapped activity of different regions in the brain. Importantly, experimental paradigms which has been used to study conformity have not been based in a social interaction setting and, hence, the results, cannot be used to explain an inherently social phenomenon. The main goal of the present thesis is to study the neurophysiological mechanisms underlying the social behavioural adjustment of conformity and its modulation with repeated interaction. To reach this goal, we have first designed a new experimental task where conformity appears spontaneously between two persons and in a reiterative way. This design exposes learning acquisition processes, which require iterative loops, as well as other cognitive control mechanisms such as feedback processing, value-based decision making and attention. The first study shows that people who previously cooperate increase their level of convergence and report a significantly more satisfying overall experience. In addition, participants learning on their counterparts’ behaviour can be explained using a RL algorithm as opposed to when they do not have previously cooperated. In the second study, we have studied the event-related potentials (ERP) and oscillatory power underlying conformity. ERP results show different levels of cognitive engagement that are associated to distinct levels of conformity. Also, time-frequency analysis shows evidence in theta, alpha and beta related to different functions such as cognitive control, attention and, also, reward processing, supporting the idea that convergence between dyads acts as a social reward. Finally, in the third study, we explored the intra- and inter- oscillatory connectivity between electrodes related to behavioural convergence. In intra-brain oscillatory connectivity coherence, we have found two different dynamics related to attention and executive functions in alpha. Also, we have found that the learning about peer’s behaviour as computed using a RL is mediated by theta oscillatory connectivity. Consequently, combined evidence from Study 2 and Study 3 suggests that both cognitive control and learning computations happening in the social behavioural adaptation of conformity are signalled in theta frequency band. The present work is one of the first studies describing, with credible evidence, that conformity, when this occurs willingly and spontaneously rather than induced, engages different brain activity underlying reward-guided learning, cognitive control, and attention.[spa] La conformidad es el acto de cambiar el comportamiento de uno a favor de ajustarnos a otros seres humanos. Se trata de una adaptación crucial que ocurre cuando la gente coopera, donde uno sacrifica su propia percepción, expectativas o creencias en aras de conseguir una convergencia con la otra persona. El objetivo del presente estudio ha sido tratar de aportar a la comprensión de las estructuras neurofisiológicas que soportan un ajuste social como el de la conformidad. En la primera parte de esta tesis comenzamos hablando de la cooperación y lo profundamente arraigada que está en nuestro comportamiento. Más tarde exploramos diferentes procesos que el cerebro requiere en el ajuste social de la conformidad. Así pues, para involucrarse en esta adaptación social, una persona requiere de un mecanismo de aprendizaje auto-referenciado basado en un modelo predictivo que le ayude a seguir el rastro de los errores de predicción que acompañan a los eventos inesperados. Además, el cerebro usa sus sistemas de control y predicción para codificar diferentes funciones de valor usadas en la selección de acción. El uso de diferentes modelos de aprendizaje en neurociencia, como los algoritmos de aprendizaje por refuerzo (RL), han sido una historia de éxito a la hora de identificar los sistemas de aprendizaje a través del mapeo de la actividad de diferentes regiones del cerebro. Es importante destacar que los paradigmas experimentales que se han usado para estudiar la conformidad no se han basado en entornos de interacción social y que, por lo tanto, sus resultados no pueden usarse para explicar un fenómeno inherentemente social. El objetivo principal de la presente tesis es el estudio de los mecanismos neurofisiológicos que fundamentan el comportamiento de ajuste social de la conformidad y su modulación con la interacción repetida. Para alcanzar este objetivo, primero hemos diseñado una nueva tarea experimental en la que la conformidad aparece de forma espontánea entre dos personas y, además, de forma reiterativa. Este diseño permite exponer tanto los procesos de adquisición del aprendizaje, que requieren de ciclos iterativos, así como otros mecanismos de control cognitivo tales como el procesamiento de la retroalimentación, las tomas de decisiones basadas en procesos valorativos y la atención. El primer estudio nos muestra que la gente que coopera previamente incrementa sus niveles de convergencia y reportan significativamente una experiencia generalmente más satisfactoria en el experimento. Adicionalmente, un modelo de RL nos explica que los participantes tratan de aprender del comportamiento de sus parejas en mayor medida si estos han cooperado previamente. En el segundo estudio, hemos estudiado los potenciales relacionados con eventos (ERP) y el poder de las oscilaciones que sustentan la conformidad. Los estudios de ERP muestran diferentes niveles de implicación cognitiva asociados con diferentes niveles de conformidad. Además, los análisis de tiempo-frecuencia muestran evidencia en theta, alfa y beta relacionados con diferentes funciones como el control cognitivo, la atención, y, también, el procesamiento de la recompensa, apoyando la idea de que la convergencia entre díadas actúa como una recompensa social. Finalmente, en el tercer estudio, exploramos la conectividad oscilatoria intra e inter entre electrodos que se pudieran relacionar con la conducta de convergencia. A propósito de la conectividad oscilatoria coherente intra, hemos hallado dos dinámicas relacionadas con la atención y las funciones ejecutivas en alfa. Asimismo, hemos encontrado que el aprendizaje de la conducta de la pareja computada a través de RL está mediada a través de la conectividad oscilatoria de theta. Consecuentemente, la evidencia combinada entre el estudio 2 y el estudio 3 sugiere que conjuntamente el control cognitivo y las computaciones de aprendizaje que ocurren en la conducta de adaptación social de la conformidad están relacionadas con la actividad de la banda de frecuencia theta. Este trabajo constituye uno de los primeros estudios que describen, con evidencia creíble, que la conformidad, cuando ocurre voluntaria y espontáneamente a diferencia cuando esta es inducida, involucra actividad del cerebro que se fundamenta en el aprendizaje guiado por reforzamiento, el control cognitivo y la atención

Probing of Brain States in Real-Time: Introducing the ConSole Environment

Recent years have seen huge advancements in the methods available and used in neuroscience employing EEG or MEG. However, the standard approach is to average a large number of trials for experimentally defined conditions in order to reduce intertrial-variability, i.e., treating it as a source of “noise.” Yet it is now more and more accepted that trial-to-trial fluctuations bear functional significance, reflecting fluctuations of “brain states” that predispose perception and action. Such effects are often revealed in a pre-stimulus period, when comparing response variability to an invariant stimulus. However such offline analyses are disadvantageous as they are correlational by drawing conclusions in a post hoc-manner and stimulus presentation is random with respect to the feature of interest. A more direct test is to trigger stimulus presentation when the relevant feature is present. The current paper introduces Constance System for Online EEG (ConSole), a software package capable of analyzing ongoing EEG/MEG in real-time and presenting auditory and visual stimuli via internal routines. Stimulation via external devices (e.g., transcranial magnetic stimulation) or third-party software (e.g., PsyScope X) is possible by sending TTL-triggers. With ConSole it is thus possible to target the stimulation at specific brain states. In contrast to many available applications, ConSole is open-source. Its modular design enhances the power of the software as it can be easily adapted to new challenges and writing new experiments is an easy task. ConSole is already pre-equipped with modules performing standard signal processing steps. The software is also independent from the EEG/MEG system, as long as a driver can be written (currently two EEG systems are supported). Besides a general introduction, we present benchmark data regarding performance and validity of the calculations used, as well as three example applications of ConSole in different settings. ConSole can be downloaded at: http://console-kn.sf.net