Inspired by nature: timescale-free and grid-free event-based computing with\ua0spiking neural networks

Computer vision is enjoying huge success in visual processing applications such as facial recognition, object identification, and navigation. Most of these studies work with traditional cameras which produce frames at predetermined fixed time intervals. Real life visual stimuli are, however, generated when changes occur in the environment and are irregular in timing. Biological visual neural systems operate on these changes and are hence free from any fixed timescales that are related to the timing of events in visual input.Inspired by biological systems, neuromorphic devices provide a new way to record visual\ua0data. These devices typically have parallel arrays of sensors which operate asynchronously. They have particular potential for robotics due to their low latency, efficient use of bandwidth and low power requirements. There are a variety of neuromorphic devices for detecting different sensory information; this thesis focuses on using the Dynamic Vision Sensor (DVS) for visual data collection.Event-based sensory inputs are generated on demand as changes happen in the environment. There are no systematic timescales in these activities and the asynchronous nature of the sensors adds to the irregularity of time intervals between events, making event-based data timescale-free. Although the array of sensors are arranged as a grid in vision sensors generally, events in the real world exist in continuous space. Biological systems are not restricted to grid-based sampling, and it is an open question whether event-based data could similarly take advantage of grid-free processing algorithms. To study visual data in a way which is timescale-free and grid-free, which is\ua0 fundamentally different from traditional video data sampled at fixed time intervals which are dense and rigid in space, requires conceptual viewpoints and methods of computation which are not typically employed in existing studies.Bio-inspired computing involves computational components that mimic or at least take inspiration from how nature works. This fusion of engineering and biology often provides insights into complex computational problems. Artificial neural networks, a computing paradigm that is inspired by how our brains work, have been studied widely with visual data. This thesis uses a type of artificial neural network—event-based spiking neural networks—as the basic framework to process event-based visual data.Building upon spiking neural networks, this thesis introduces two methods that process event-based data with the principles of being timescale-free and grid-free. The first method preprocesses events as distributions of Gaussian shaped spatiotemporal volumes, and then introduces a new neuron model with time-delayed dendrites and dendritic and axonal computation as the main building blocks of the spiking neural network to perform long-term predictions. Gaussians are used for simplicity purposes. This Gaussian-based method is shown in this thesis to outperform a commonly used iterative prediction paradigm on DVS data.The second method involves a new concept for processing event-based data based on the “light cone” idea in physics. Starting from a given point in real space at a given time, a light cone is the set of points in spacetime reachable without exceeding the speed of light, and these points trace out spacetime trajectories called world lines. The light cone concept is applied to DVS data. As an object moves with respect to the DVS, the events generated are related by their speeds relative to the DVS. An observer can calculate possible world lines for each point but has no access to the correct one. The idea of a “motion cone” is introduced to refer to the distribution of possible world lines for an event. Motion cones provide a novel theory for the early stages of visual processing. Instead of spatial clustering, world lines produce a new representation determined by a speed-based clustering of events. A novel spiking neural network model with dendritic connections based on motion cones is proposed, with the ability predict future motion pattern in a long-term prediction.Freedom from timescales and fixed grid sizes are fundamental characteristics of neuromorphic event-based data but few algorithms to date exploit their potential. Focusing on the inter-event relationship in the continuous spatiotemporal volume can preserve these features during processing. This thesis presents two examples of incorporating the features of being timescale-free and grid-free into algorithm development and examines their performance on real world DVS data. These new concepts and models contribute to the neuromorphic computation field by providing new ways of thinking about event-based representations and their associated algorithms. They also have the potential to stimulate rethinking of representations in the early stages of an event-based vision system. To aid algorithm development, a benchmarking data set containing data ranging from simple environment changes collected from a stationary camera to complex environmentally rich navigation performed by mobile robots has been collated. Studies conducted in this thesis use examples from this benchmarking data set which is also made available to the public

The hippocampus and cerebellum in adaptively timed learning, recognition, and movement

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were not the release of these commands adaptively timed by the cerebellum. The model hippocampal system modulates cortical recognition learning without actually encoding the representational information that the cortex encodes. These properties avoid the difficulties faced by several models that propose a direct hippocampal role in recognition learning. Learning within the model hippocampal system controls adaptive timing and spatial orientation. Model properties hereby clarify how hippocampal ablations cause amnesic symptoms and difficulties with tasks which combine task delays, novelty detection, and attention towards goal objects amid distractions. When these model recognition, reinforcement, sensory-motor, and timing processes work together, they suggest how the brain can accomplish conditioning of multiple sensory events to delayed rewards, as during serial compound conditioning.Air Force Office of Scientific Research (F49620-92-J-0225, F49620-86-C-0037, 90-0128); Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-92-J-1904); National Institute of Mental Health (MH-42900

Enhancing Neuromorphic Computing with Advanced Spiking Neural Network Architectures

This dissertation proposes ways to address current limitations of neuromorphic computing to create energy-efficient and adaptable systems for AI applications. It does so by designing novel spiking neural networks architectures that improve their performance. Specifically, the two proposed architectures address the issues of training complexity, hyperparameter selection, computational flexibility, and scarcity of neuromorphic training data. The first architecture uses auxiliary learning to improve training performance and data usage, while the second architecture leverages neuromodulation capability of spiking neurons to improve multitasking classification performance. The proposed architectures are tested on Intel\u27s Loihi2 neuromorphic chip using several neuromorphic datasets, such as NMIST, DVSCIFAR10, and DVS128-Gesture. The presented results demonstrate potential of the proposed architectures but also reveal some of their limitations which are proposed as future research

Mejora de computación neuromórfica con arquitecturas avanzadas de redes neuronales por impulsos

La computación neuromórfica (NC, del inglés neuromorphic computing) pretende revolucionar el campo de la inteligencia artificial. Implica diseñar e implementar sistemas electrónicos que simulen el comportamiento de las neuronas biológicas utilizando hardware especializado, como matrices de puertas programables en campo (FPGA, del ingl´es field-programmable gate array) o chips neuromórficos dedicados [1, 2]. NC está diseñado para ser altamente eficiente, optimizado para bajo consumo de energía y alto paralelismo [3]. Estos sistemas son adaptables a entornos cambiantes y pueden aprender durante la operación, lo que los hace muy adecuados para resolver problemas dinámicos e impredecibles [4]. Sin embargo, el uso de NC para resolver problemas de la vida real actualmente está limitado porque el rendimiento de las redes neuronales por impulsos (SNN), las redes neuronales empleadas en NC, no es tan alta como el de los sistemas de computación tradicionales, como los alcanzados en dispositivos de aprendizaje profundo especializado, en términos de precisión y velocidad de aprendizaje [5, 6]. Varias razones contribuyen a la brecha de rendimiento: los SNN son más difíciles de entrenar debido a que necesitan algoritmos de entrenamiento especializados [7, 8]; son más sensibles a hiperparámetros, ya que son sistemas dinámicos con interacciones complejas [9], requieren conjuntos de datos especializados (datos neuromórficos) que actualmente son escasos y de tamaño limitado [10], y el rango de funciones que los SNN pueden aproximar es más limitado en comparación con las redes neuronales artificiales (ANN) tradicionales [11]. Antes de que NC pueda tener un impacto más significativo en la IA y la tecnología informática, es necesario abordar estos desafíos relacionados con los SNN.This dissertation addresses current limitations of neuromorphic computing to create energy-efficient and adaptable artificial intelligence systems. It focuses on increasing utilization of neuromorphic computing by designing novel architectures that improve the performance of the spiking neural networks. Specifically, the architectures address the issues of training complexity, hyperparameter selection, computational flexibility, and scarcity of training data. The first proposed architecture utilizes auxiliary learning to improve training performance and data usage, while the second architecture leverages neuromodulation capability of spiking neurons to improve multitasking classification performance. The proposed architectures are tested on the Intel’s Loihi2 neuromorphic computer using several neuromorphic data sets, such as NMIST, DVSCIFAR10, and DVS128-Gesture. Results presented in this dissertation demonstrate the potential of the proposed architectures, but also reveal some limitations that are proposed as future work

Computational modelling of neural mechanisms underlying natural speech perception

Humans are highly skilled at the analysis of complex auditory scenes. In particular, the human auditory system is characterized by incredible robustness to noise and can nearly effortlessly isolate the voice of a specific talker from even the busiest of mixtures. However, neural mechanisms underlying these remarkable properties remain poorly understood. This is mainly due to the inherent complexity of speech signals and multi-stage, intricate processing performed in the human auditory system. Understanding these neural mechanisms underlying speech perception is of interest for clinical practice, brain-computer interfacing and automatic speech processing systems. In this thesis, we developed computational models characterizing neural speech processing across different stages of the human auditory pathways. In particular, we studied the active role of slow cortical oscillations in speech-in-noise comprehension through a spiking neural network model for encoding spoken sentences. The neural dynamics of the model during noisy speech encoding reflected speech comprehension of young, normal-hearing adults. The proposed theoretical model was validated by predicting the effects of non-invasive brain stimulation on speech comprehension in an experimental study involving a cohort of volunteers. Moreover, we developed a modelling framework for detecting the early, high-frequency neural response to the uninterrupted speech in non-invasive neural recordings. We applied the method to investigate top-down modulation of this response by the listener's selective attention and linguistic properties of different words from a spoken narrative. We found that in both cases, the detected responses of predominantly subcortical origin were significantly modulated, which supports the functional role of feedback, between higher- and lower levels stages of the auditory pathways, in speech perception. The proposed computational models shed light on some of the poorly understood neural mechanisms underlying speech perception. The developed methods can be readily employed in future studies involving a range of experimental paradigms beyond these considered in this thesis.Open Acces

PRINCIPLES OF INFORMATION PROCESSING IN NEURONAL AVALANCHES

How the brain processes information is poorly understood. It has been suggested that the imbalance of excitation and inhibition (E/I) can significantly affect information processing in the brain. Neuronal avalanches, a type of spontaneous activity recently discovered, have been ubiquitously observed in vitro and in vivo when the cortical network is in the E/I balanced state. In this dissertation, I experimentally demonstrate that several properties regarding information processing in the cortex, i.e. the entropy of spontaneous activity, the information transmission between stimulus and response, the diversity of synchronized states and the discrimination of external stimuli, are optimized when the cortical network is in the E/I balanced state, exhibiting neuronal avalanche dynamics. These experimental studies not only support the hypothesis that the cortex operates in the critical state, but also suggest that criticality is a potential principle of information processing in the cortex. Further, we study the interaction structure in population neuronal dynamics, and discovered a special structure of higher order interactions that are inherent in the neuronal dynamics

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

Time- and value-continuous explainable affect estimation in-the-wild

Today, the relevance of Affective Computing, i.e., of making computers recognise and simulate human emotions, cannot be overstated. All technology giants (from manufacturers of laptops to mobile phones to smart speakers) are in a fierce competition to make their devices understand not only what is being said, but also how it is being said to recognise user’s emotions. The goals have evolved from predicting the basic emotions (e.g., happy, sad) to now the more nuanced affective states (e.g., relaxed, bored) real-time. The databases used in such research too have evolved, from earlier featuring the acted behaviours to now spontaneous behaviours. There is a more powerful shift lately, called in-the-wild affect recognition, i.e., taking the research out of the laboratory, into the uncontrolled real-world. This thesis discusses, for the very first time, affect recognition for two unique in-the-wild audiovisual databases, GRAS2 and SEWA. The GRAS2 is the only database till date with time- and value-continuous affect annotations for Labov effect-free affective behaviours, i.e., without the participant’s awareness of being recorded (which otherwise is known to affect the naturalness of one’s affective behaviour). The SEWA features participants from six different cultural backgrounds, conversing using a video-calling platform. Thus, SEWA features in-the-wild recordings further corrupted by unpredictable artifacts, such as the network-induced delays, frame-freezing and echoes. The two databases present a unique opportunity to study time- and value-continuous affect estimation that is truly in-the-wild. A novel ‘Evaluator Weighted Estimation’ formulation is proposed to generate a gold standard sequence from several annotations. An illustration is presented demonstrating that the moving bag-of-words (BoW) representation better preserves the temporal context of the features, yet remaining more robust against the outliers compared to other statistical summaries, e.g., moving average. A novel, data-independent randomised codebook is proposed for the BoW representation; especially useful for cross-corpus model generalisation testing when the feature-spaces of the databases differ drastically. Various deep learning models and support vector regressors are used to predict affect dimensions time- and value-continuously. Better generalisability of the models trained on GRAS2 , despite the smaller training size, makes a strong case for the collection and use of Labov effect-free data. A further foundational contribution is the discovery of the missing many-to-many mapping between the mean square error (MSE) and the concordance correlation coefficient (CCC), i.e., between two of the most popular utility functions till date. The newly invented cost function |MSE_{XY}/σ_{XY}| has been evaluated in the experiments aimed at demystifying the inner workings of a well-performing, simple, low-cost neural network effectively utilising the BoW text features. Also proposed herein is the shallowest-possible convolutional neural network (CNN) that uses the facial action unit (FAU) features. The CNN exploits sequential context, but unlike RNNs, also inherently allows data- and process-parallelism. Interestingly, for the most part, these white-box AI models have shown to utilise the provided features consistent with the human perception of emotion expression

SENSORY AND PERCEPTUAL CODES IN CORTICAL AUDITORY PROCESSING

A key aspect of human auditory cognition is establishing efficient and reliable representations about the acoustic environment, especially at the level of auditory cortex. Since the inception of encoding models that relate sound to neural response, three longstanding questions remain open. First, on the apparently insurmountable problem of fundamental changes to cortical responses depending on certain categories of sound (e.g. simple tones versus environmental sound). Second, on how to integrate inner or subjective perceptual experiences into sound encoding models, given that they presuppose existing, direct physical stimulation which is sometimes missed. And third, on how does context and learning fine-tune these encoding rules, as adaptive changes to improve impoverished conditions particularly important for communication sounds. In this series, each question is addressed by analysis of mappings from sound stimuli delivered-to and/or perceived-by a listener, to large-scale cortically-sourced response time series from magnetoencephalography. It is first shown that the divergent, categorical modes of sensory coding may unify by exploring alternative acoustic representations other than the traditional spectrogram, such as temporal transient maps. Encoding models of either of artificial random tones, music, or speech stimulus classes, were substantially matched in their structure when represented from acoustic energy increases –consistent with the existence of a domain-general common baseline processing stage. Separately, the matter of the perceptual experience of sound via cortical responses is addressed via stereotyped rhythmic patterns normally entraining cortical responses with equal periodicity. Here, it is shown that under conditions of perceptual restoration, namely cases where a listener reports hearing a specific sound pattern in the midst of noise nonetheless, one may access such endogenous representations in the form of evoked cortical oscillations at the same rhythmic rate. Finally, with regards to natural speech, it is shown that extensive prior experience over repeated listening of the same sentence materials may facilitate the ability to reconstruct the original stimulus even where noise replaces it, and to also expedite normal cortical processing times in listeners. Overall, the findings demonstrate cases by which sensory and perceptual coding approaches jointly continue to expand the enquiry about listeners’ personal experience of the communication-rich soundscape

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden