Déconvolution impulsionnelle par filtre de Hunt et seuillage

- Nous présentons une nouvelle méthode de déconvolution impulsionnelle basée sur le couplage du filtre de Hunt et un seuillage (afin d'obtenir un signal impulsionnel). On montre qu'un mélange de gaussiennes est un bon modèle pour la distribution de la sortie du filtre de Hunt, ce qui permet de trouver une expression du seuil qui minimise la probabilité d'erreurs. La méthode pouvant être interprétée comme un estimateur MAP, les hyperparamètres du problème sont estimés grâce à une approche JMAP (joint MAP). Cette méthode est appliquée avec de bons résultats sur des signaux de décharges partielles

Cell Detection by Functional Inverse Diffusion and Non-negative Group Sparsity−-Part I: Modeling and Inverse Problems

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this first part, we start by presenting a physical partial differential equations (PDE) model up to image acquisition for these biochemical assays. Then, we use the PDEs' Green function to derive a novel parametrization of the acquired images. This parametrization allows us to propose a functional optimization problem to address inverse diffusion. In particular, we propose a non-negative group-sparsity regularized optimization problem with the goal of localizing and characterizing the biological cells involved in the said assays. We continue by proposing a suitable discretization scheme that enables both the generation of synthetic data and implementable algorithms to address inverse diffusion. We end Part I by providing a preliminary comparison between the results of our methodology and an expert human labeler on real data. Part II is devoted to providing an accelerated proximal gradient algorithm to solve the proposed problem and to the empirical validation of our methodology.Comment: published, 15 page

Bayesian image restoration and bacteria detection in optical endomicroscopy

Optical microscopy systems can be used to obtain high-resolution microscopic images of tissue cultures and ex vivo tissue samples. This imaging technique can be translated for in vivo, in situ applications by using optical fibres and miniature optics. Fibred optical endomicroscopy (OEM) can enable optical biopsy in organs inaccessible by any other imaging systems, and hence can provide rapid and accurate diagnosis in a short time. The raw data the system produce is difficult to interpret as it is modulated by a fibre bundle pattern, producing what is called the “honeycomb effect”. Moreover, the data is further degraded due to the fibre core cross coupling problem. On the other hand, there is an unmet clinical need for automatic tools that can help the clinicians to detect fluorescently labelled bacteria in distal lung images. The aim of this thesis is to develop advanced image processing algorithms that can address the above mentioned problems. First, we provide a statistical model for the fibre core cross coupling problem and the sparse sampling by imaging fibre bundles (honeycomb artefact), which are formulated here as a restoration problem for the first time in the literature. We then introduce a non-linear interpolation method, based on Gaussian processes regression, in order to recover an interpretable scene from the deconvolved data. Second, we develop two bacteria detection algorithms, each of which provides different characteristics. The first approach considers joint formulation to the sparse coding and anomaly detection problems. The anomalies here are considered as candidate bacteria, which are annotated with the help of a trained clinician. Although this approach provides good detection performance and outperforms existing methods in the literature, the user has to carefully tune some crucial model parameters. Hence, we propose a more adaptive approach, for which a Bayesian framework is adopted. This approach not only outperforms the proposed supervised approach and existing methods in the literature but also provides computation time that competes with optimization-based methods

Delta rhythms as a substrate for holographic processing in sleep and wakefulness

PhD ThesisWe initially considered the theoretical properties and benefits of so-called holographic processing in a specific type of computational problem implied by the theories of synaptic rescaling processes in the biological wake-sleep cycle. This raised two fundamental questions that we attempted to answer by an experimental in vitro electrophysiological approach. We developed a comprehensive experimental paradigm based on a pharmacological model of the wake-sleep-associated delta rhythm measured with a Utah micro-electrode array at the interface between primary and associational areas in the rodent neocortex. We first verified that our in vitro delta rhythm model possessed two key features found in both in vivo rodent and human studies of synaptic rescaling processes in sleep: The first property being that prior local synaptic potentiation in wake leads to increased local delta power in subsequent sleep. The second property is the reactivation in sleep of neural firing patterns observed prior to sleep. By reproducing these findings we confirmed that our model is arguably an adequate medium for further study of the putative sleep-related synaptic rescaling process. In addition we found important differences between neural units that reactivated or deactivated during delta; these were differences in cell types based on unit spike shapes, in prior firing rates and in prior spike-train-to-local-field-potential coherence. Taken together these results suggested a mechanistic chain of explanation of the two observed properties, and set the neurobiological framework for further, more computationally driven analysis. Using the above experimental and theoretical substrate we developed a new method of analysis of micro-electrode array data. The method is a generalization to the electromagnetic case of a well-known technique for processing acoustic microphone array data. This allowed calculation of: The instantaneous spatial energy flow and dissipation in the neocortical areas under the array; The spatial energy source density in analogy to well-known current source density analysis. We then refocused our investigation on the two theoretical questions that we hoped to achieve experimental answers for: Whether the state of the neocortex during a delta rhythm could be described by ergodic statistics, which we determined by analyzing the spectral properties of energy dissipation as a signature of the state of the dynamical system; A more explorative approach prompting an investigation of the spatiotemporal interactions across and along neocortical layers and areas during a delta rhythm, as implied by energy flow patterns. We found that the in vitro rodent neocortex does not conform to ergodic statistics during a pharmacologically driven delta or gamma rhythm. We also found a delta period locked pattern of energy flow across and along layers and areas, which doubled the processing cycle relative to the fundamental delta rhythm, tentatively suggesting a reciprocal, two-stage information processing hierarchy similar to a stochastic Helmholtz machine with a wake-sleep training algorithm. Further, the complex valued energy flow might suggest an improvement to the Helmholtz machine concept by generalizing the complex valued weights of the stochastic network to higher dimensional multi-vectors of a geometric algebra with a metric particularity suited for holographic processes. Finally, preliminary attempts were made to implement and characterize the above network dynamics in silico. We found that a qubit valued network does not allow fully holographic processes, but tentatively suggest that an ebit valued network may display two key properties of general holographic processing

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

Glottal-synchronous speech processing

Glottal-synchronous speech processing is a field of speech science where the pseudoperiodicity of voiced speech is exploited. Traditionally, speech processing involves segmenting and processing short speech frames of predefined length; this may fail to exploit the inherent periodic structure of voiced speech which glottal-synchronous speech frames have the potential to harness. Glottal-synchronous frames are often derived from the glottal closure instants (GCIs) and glottal opening instants (GOIs). The SIGMA algorithm was developed for the detection of GCIs and GOIs from the Electroglottograph signal with a measured accuracy of up to 99.59%. For GCI and GOI detection from speech signals, the YAGA algorithm provides a measured accuracy of up to 99.84%. Multichannel speech-based approaches are shown to be more robust to reverberation than single-channel algorithms. The GCIs are applied to real-world applications including speech dereverberation, where SNR is improved by up to 5 dB, and to prosodic manipulation where the importance of voicing detection in glottal-synchronous algorithms is demonstrated by subjective testing. The GCIs are further exploited in a new area of data-driven speech modelling, providing new insights into speech production and a set of tools to aid deployment into real-world applications. The technique is shown to be applicable in areas of speech coding, identification and artificial bandwidth extension of telephone speec

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript

Block-level discrete cosine transform coefficients for autonomic face recognition

This dissertation presents a novel method of autonomic face recognition based on the recently proposed biologically plausible network of networks (NoN) model of information processing. The NoN model is based on locally parallel and globally coordinated transformations. In the NoN architecture, the neurons or computational units form distributed networks, which themselves link to form larger networks. In the general case, an n-level hierarchy of nested distributed networks is constructed. This models the structures in the cerebral cortex described by Mountcastle and the architecture based on that proposed for information processing by Sutton. In the implementation proposed in the dissertation, the image is processed by a nested family of locally operating networks along with a hierarchically superior network that classifies the information from each of the local networks. The implementation of this approach helps obtain sensitivity to the contrast sensitivity function (CSF) in the middle of the spectrum, as is true for the human vision system. The input images are divided into blocks to define the local regions of processing. The two-dimensional Discrete Cosine Transform (DCT), a spatial frequency transform, is used to transform the data into the frequency domain. Thereafter, statistical operators that calculate various functions of spatial frequency in the block are used to produce a block-level DCT coefficient. The image is now transformed into a variable length vector that is trained with respect to the data set. The classification was done by the use of a backpropagation neural network. The proposed method yields excellent results on a benchmark database. The results of the experiments yielded a maximum of 98.5% recognition accuracy and an average of 97.4% recognition accuracy. An advanced version of the method where the local processing is done on offset blocks has also been developed. This has validated the NoN approach and further research using local processing as well as more advanced global operators is likely to yield even better results