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

Interfacing of neuromorphic vision, auditory and olfactory sensors with digital neuromorphic circuits

The conventional Von Neumann architecture imposes strict constraints on the development of intelligent adaptive systems. The requirements of substantial computing power to process and analyse complex data make such an approach impractical to be used in implementing smart systems. Neuromorphic engineering has produced promising results in applications such as electronic sensing, networking architectures and complex data processing. This interdisciplinary field takes inspiration from neurobiological architecture and emulates these characteristics using analogue Very Large Scale Integration (VLSI). The unconventional approach of exploiting the non-linear current characteristics of transistors has aided in the development of low-power adaptive systems that can be implemented in intelligent systems. The neuromorphic approach is widely applied in electronic sensing, particularly in vision, auditory, tactile and olfactory sensors. While conventional sensors generate a huge amount of redundant output data, neuromorphic sensors implement the biological concept of spike-based output to generate sparse output data that corresponds to a certain sensing event. The operation principle applied in these sensors supports reduced power consumption with operating efficiency comparable to conventional sensors. Although neuromorphic sensors such as Dynamic Vision Sensor (DVS), Dynamic and Active pixel Vision Sensor (DAVIS) and AEREAR2 are steadily expanding their scope of application in real-world systems, the lack of spike-based data processing algorithms and complex interfacing methods restricts its applications in low-cost standalone autonomous systems. This research addresses the issue of interfacing between neuromorphic sensors and digital neuromorphic circuits. Current interfacing methods of these sensors are dependent on computers for output data processing. This approach restricts the portability of these sensors, limits their application in a standalone system and increases the overall cost of such systems. The proposed methodology simplifies the interfacing of these sensors with digital neuromorphic processors by utilizing AER communication protocols and neuromorphic hardware developed under the Convolution AER Vision Architecture for Real-time (CAVIAR) project. The proposed interface is simulated using a JAVA model that emulates a typical spikebased output of a neuromorphic sensor, in this case an olfactory sensor, and functions that process this data based on supervised learning. The successful implementation of this simulation suggests that the methodology is a practical solution and can be implemented in hardware. The JAVA simulation is compared to a similar model developed in Nengo, a standard large-scale neural simulation tool. The successful completion of this research contributes towards expanding the scope of application of neuromorphic sensors in standalone intelligent systems. The easy interfacing method proposed in this thesis promotes the portability of these sensors by eliminating the dependency on computers for output data processing. The inclusion of neuromorphic Field Programmable Gate Array (FPGA) board allows reconfiguration and deployment of learning algorithms to implement adaptable systems. These low-power systems can be widely applied in biosecurity and environmental monitoring. With this thesis, we suggest directions for future research in neuromorphic standalone systems based on neuromorphic olfaction

New directions for medical artificial intelligence

AbstractThe past decade has seen significant advances in medical artificial intelligence (MAI), but its role in medicine and medical education remains limited. The goal for the next decade must be directed towards maximizing the utility of MAI in the clinic and classroom. Fundamental to achieving this is increasing the involvement of clinicians in MAI development. MAI developers must move from “pet projects” toward generalizable tasks meeting recognized clinical needs. Clinical researchers must be made aware of knowledge engineering, so clinical data bases can be prospectively designed to contribute directly into MAI “knowledge bases”. Closer involvement of MAI scientists with clinicians is also essential to further understanding of cognitive processes in medical decision-making. Technological advances in user interfaces—including voice recognition, natural language processing, enhanced graphics and videodiscs— must be rapidly introduced into MAI to increase physician acceptance. Development of expert systems in non-clinical areas must expand, particularly resource management, e.g. operating room or hospital admission scheduling. The establishment of MAI laboratories at major medical centers around the country, involving both clinicians and computer scientists, represents an ideal mechanism for bringing MAI into the mainstream of medical computing

Event related (de-)synchronization patterns in actual and imagined hand movements

Projecte final de carrera realitzat en col.laboració amb Philips ResearchThis project presents different signal processing techniques, such as Principal Component Analysis (PCA) and Common Spatial Patterns (CSP), applied to characterize the reactivity of central rhythms in the alpha and beta bands during self paced voluntary and imaginary movement. The idea is to allow people to control devices, or interact with machines by simply thinking. To do so, we monitor the brain activity using electroencephalogram (EEG) measurements which record the signals from electrodes positioned on the scalp. The objective is to use motor imagery signals to build a brain computer interface, able to learn from data analyzed before, using the properties of neural networks. The possibility of designing an intuitive communication system between a brain and a computer, available to be operated by everyone, even by people with severe motor impairments, is the main objective of this stud

Ultrasound based Silent Speech Interface using Deep Learning

Silent Speech Interface (SSI) is a technology able to synthesize speech in the absence of any acoustic signal. It can be useful in cases like laryngectomy patients, noisy environments or silent calls. This thesis explores the particular case of SSI using ultrasound images of the tongue as input signals. A 'direct synthesis' approach based on Deep Neural Networks and Mel-generalized cepstral coefficients is proposed. This document is an extension of Csapó et al. "DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface". Several deep learning models, such as the basic Feed-forward Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks are presented and discussed. A denoising pre-processing based on a Deep Convolutional Autoencoder has also been studied. A considerable number of experiments using a set of different deep learning architectures and an extensive hyperperameter optimization study have been realized. The different experiments have been testing and rating several objective and subjective quality measures. According to the experiments, an architecture based on a CNN and bidirectional LSTM layers has shown the best results in both objective and subjective terms.Silent Speech Interface (SSI) is a technology able to synthesize speech in the absence of any acoustic signal. It can be useful in cases like laryngectomy patients, noisy environments or silent calls. This thesis explores the particular case of SSI using ultrasound images of the tongue as input signals. A 'direct synthesis' approach based on Deep Neural Networks and Mel-generalized cepstral coefficients is proposed. This document is an extension of Csapó et al. "DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface". Several deep learning models, such as the basic Feed-forward Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks are presented and discussed. A denoising pre-processing based on a Deep Convolutional Autoencoder has also been studied. A considerable number of experiments using a set of different deep learning architectures and an extensive hyperperameter optimization study have been realized. The different experiments have been testing and rating several objective and subjective quality measures. According to the experiments, an architecture based on a CNN and bidirectional LSTM layers has shown the best results in both objective and subjective terms.Silent Speech Interface (SSI) és una tecnologia capaç de sintetitzar veu partint únicament de senyals no-acústiques. Pot tenir gran utilitat en casos com pacients de laringectomia, ambients sorollosos o trucades silencioses. Aquesta tèsis explora el cas particular de SSI utilitzant imatges de la llengua captades amb ultrasons com a senyals d'entrada. Es proposa un enfocament de 'síntesis directa' basat en Xarxes Neuronals Profundes i coeficients Mel-generalized cepstral. Aquest document és una extensió del treball de Csapó et al. "DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface" . Diversos models de xarxes neuronals són presentats i discutits, com les bàsiques xarxes neuronals directes, xarxes neuronals convolucionals o xarxes neuronals recurrents. També s'ha estudiat un pre-processat reductor de soroll basat en un Autoencoder convolucional profund. S'ha portat a terme un nombre considerable d'experiments utilitzant diverses arquitectures de Deep Learning, així com un extens estudi d'optimització d'hyperparàmetres. Els diferents experiments han estat evaluar i qualificar a partir de diferentes mesures de qualitat objectives i subjectives. Els millors resultats, tant en termes objectius com subjectius, els ha presentat una arquitectura basada en una CNN i capes bidireccionals de LSTMs