How Noisy Adaptation of Neurons Shapes Interspike Interval Histograms and Correlations

Channel noise is the dominant intrinsic noise source of neurons causing variability in the timing of action potentials and interspike intervals (ISI). Slow adaptation currents are observed in many cells and strongly shape response properties of neurons. These currents are mediated by finite populations of ionic channels and may thus carry a substantial noise component. Here we study the effect of such adaptation noise on the ISI statistics of an integrate-and-fire model neuron by means of analytical techniques and extensive numerical simulations. We contrast this stochastic adaptation with the commonly studied case of a fast fluctuating current noise and a deterministic adaptation current (corresponding to an infinite population of adaptation channels). We derive analytical approximations for the ISI density and ISI serial correlation coefficient for both cases. For fast fluctuations and deterministic adaptation, the ISI density is well approximated by an inverse Gaussian (IG) and the ISI correlations are negative. In marked contrast, for stochastic adaptation, the density is more peaked and has a heavier tail than an IG density and the serial correlations are positive. A numerical study of the mixed case where both fast fluctuations and adaptation channel noise are present reveals a smooth transition between the analytically tractable limiting cases. Our conclusions are furthermore supported by numerical simulations of a biophysically more realistic Hodgkin-Huxley type model. Our results could be used to infer the dominant source of noise in neurons from their ISI statistics

Real-Time Facial Emotion Recognition Using Fast R-CNN

In computer vision and image processing, object detection algorithms are used to detect semantic objects of certain classes of images and videos. Object detector algorithms use deep learning networks to classify detected regions. Unprecedented advancements in Convolutional Neural Networks (CNN) have led to new possibilities and implementations for object detectors. An object detector which uses a deep learning algorithm detect objects through proposed regions, and then classifies the region using a CNN. Object detectors are computationally efficient unlike a typical CNN which is computationally complex and expensive. Object detectors are widely used for face detection, recognition, and object tracking. In this thesis, deep learning based object detection algorithms are implemented to classify facially expressed emotions in real-time captured through a webcam. A typical CNN would classify images without specifying regions within an image, which could be considered as a limitation towards better understanding the network performance which depend on different training options. It would also be more difficult to verify whether a network have converged and is able to generalize, which is the ability to classify unseen data, data which was not part of the training set. Fast Region-based Convolutional Neural Network, an object detection algorithm; used to detect facially expressed emotion in real-time by classifying proposed regions. The Fast R-CNN is trained using a high-quality video database, consisting of 24 actors, facially expressing eight different emotions, obtained from images which were processed from 60 videos per actor. An object detector’s performance is measured using various metrics. Regardless of how an object detector performed with respect to average precision or miss rate, doing well on such metrics would not necessarily mean that the network is correctly classifying regions. This may result from the fact that the network model has been over-trained. In our work we showed that object detector algorithm such as Fast R-CNN performed surprisingly well in classifying facially expressed emotions in real-time, performing better than CNN

On the Techniques for Efficient Sampling, Uncertainty Quantification and Robust Control of Stochastic Multiscale Systems

In order to better understand and leverage natural phenomena to design materials and devices (e.g. biomedical coatings, catalytic reactors, thin conductive films for microprocessors, etc.), stochastic multiscale models have been developed that explicitly model the interactions and feedbacks between the electronic, atomistic/molecular, mesoscopic and macroscopic scales. These models attempt to use the accurate results from the fine scales to inform industrially relevant domain sizes and thereby improve product quality through optimal control actions during industrial manufacturing. However, the presence of stochastic calculations increases the computational cost of such modeling approaches and makes their direct application in uncertainty quantification, optimization and online control challenging. Uncertainty cannot be ignored from simulations, otherwise there will be model-plant mismatch and loss in performance. The added computational intensity necessitates the development of more efficient computational methods that can leverage the accurate predictions of stochastic multiscale models in the industrial setting where accuracy, efficiency and speed are of utmost importance. A lot of research has been done in the area of stochastic multiscale models over the past few decades, but some gaps in knowledge remain. For instance, the performance of traditional uncertainty quantification techniques such as power series (PSE) and polynomial chaos expansions (PCE) has not been compared in the context of stochastic multiscale systems. Furthermore, a novel sampling technique called Multilevel Monte Carlo (MLMC) sampling emerged from the field of computational finance with the aim of preserving accuracy of estimation of model observables while decreasing the required computational cost. However, its applications in the field of chemical engineering and in particular for stochastic multiscale systems remain limited. Also, the advancements in computing power caused the usefulness of machine learning methods such as Artificial Neural Networks (ANNs) to increase. Because of their flexibility, accuracy and computational efficiency, ANNs are experiencing a resurgence of research interest, but their application for stochastic multiscale chemical engineering systems are still limited at the moment. This thesis aims to fill the identified gaps in knowledge. The results of the conducted research indicate that PCE can be more computationally efficient and accurate than PSE for stochastic multiscale systems, but it may be vulnerable to the effects of stochastic noise. MLMC sampling provides an attractive advantage over the heuristic methods for uncertainty propagation in stochastic multiscale systems because it allows to estimate the level of noise in the observables. However, the stochastic noise imposes a limit on the maximum achievable MLMC accuracy, which was not observed for continuous systems that were originally used in MLMC development. ANNs appear to be a very promising method for online model predictive control of stochastic multiscale systems because of their computational efficiency, accuracy and robustness to large disturbances not seen in the training data

Hardware-Efficient Scalable Reinforcement Learning Systems

Reinforcement Learning (RL) is a machine learning discipline in which an agent learns by interacting with its environment. In this paradigm, the agent is required to perceive its state and take actions accordingly. Upon taking each action, a numerical reward is provided by the environment. The goal of the agent is thus to maximize the aggregate rewards it receives over time. Over the past two decades, a large variety of algorithms have been proposed to select actions in order to explore the environment and gradually construct an e¤ective strategy that maximizes the rewards. These RL techniques have been successfully applied to numerous real-world, complex applications including board games and motor control tasks. Almost all RL algorithms involve the estimation of a value function, which indicates how good it is for the agent to be in a given state, in terms of the total expected reward in the long run. Alternatively, the value function may re‡ect on the impact of taking a particular action at a given state. The most fundamental approach for constructing such a value function consists of updating a table that contains a value for each state (or each state-action pair). However, this approach is impractical for large scale problems, in which the state and/or action spaces are large. In order to deal with such problems, it is necessary to exploit the generalization capabilities of non-linear function approximators, such as arti…cial neural networks. This dissertation focuses on practical methodologies for solving reinforcement learning problems with large state and/or action spaces. In particular, the work addresses scenarios in which an agent does not have full knowledge of its state, but rather receives partial information about its environment via sensory-based observations. In order to address such intricate problems, novel solutions for both tabular and function-approximation based RL frameworks are proposed. A resource-efficient recurrent neural network algorithm is presented, which exploits adaptive step-size techniques to improve learning characteristics. Moreover, a consolidated actor-critic network is introduced, which omits the modeling redundancy found in typical actor-critic systems. Pivotal concerns are the scalability and speed of the learning algorithms, for which we devise architectures that map efficiently to hardware. As a result, a high degree of parallelism can be achieved. Simulation results that correspond to relevant testbench problems clearly demonstrate the solid performance attributes of the proposed solutions

Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics

Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional integration methods. Here, methods (1) and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3) relying on convolutional neural networks. Pure ML methods to solve (nonlinear) PDEs are represented by Physics-Informed Neural network (PINN) methods, which could be combined with attention mechanism to address discontinuous solutions. Both LSTM and attention architectures, together with modern and generalized classic optimizers to include stochasticity for DL networks, are extensively reviewed. Kernel machines, including Gaussian processes, are provided to sufficient depth for more advanced works such as shallow networks with infinite width. Not only addressing experts, readers are assumed familiar with computational mechanics, but not with DL, whose concepts and applications are built up from the basics, aiming at bringing first-time learners quickly to the forefront of research. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics, even in well-known references. Positioning and pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at CMES-Computer Modeling in Engineering & Science