Scalable Front End Designs for Communication and Learning

In this work we provide three examples of estimation/detection problems, for which customizing the Front End to the specific application makes the system more efficient and scalable. The three problems we consider are all classical, but face new scalability challenges. This introduces additional constraints, accounting for which results in front end designs that are very distinct from the conventional approaches. The first two case studies pertain to the canonical problems of synchronization and equalization for communication links. As the system bandwidths scale, challenges arise due to the limiting resolution of analog-to-digital converters (ADCs). We discuss system designs that react to this bottleneck by drastically relaxing the precision requirements of the front end and correspondingly modifying the back end algorithms using Bayesian principles. The third problem we discuss belongs to the field of computer vision. Inspired by the research in neuroscience about the mammalian visual system, we redesign the front end of a machine vision system to be neuro-mimetic, followed by layers of unsupervised learning using simple k-means clustering. This results in a framework that is intuitive, more computationally efficient compared to the approach of supervised deep networks, and amenable to the increasing availability of large amounts of unlabeled data. We first consider the problem of blind carrier phase and frequency synchronization in order to obtain insight into the performance limitations imposed by severe quantization constraints. We adopt a mixed signal analog front end that coarsely quantizes the phase and employs a digitally controlled feedback that applies a phase shift prior to the ADC, this acts as a controllable dither signal and aids in the estimation process. We propose a control policy for the feedback and show that combined with blind Bayesian algorithms, it results in excellent performance, close to that of an unquantized system.Next, we take up the problem of channel equalization with severe limits on the number of slicers available for the ADC. We find that the standard flash ADC architecture can be highly sub-optimal in the presence of such constraints. Hence we explore a ``space-time'' generalization of the flash architecture by allowing a fixed numberof slicers to be dispersed in time (sampling phase) as well as space (i.e., amplitude). We show that optimizing the slicer locations, conditioned on the channel, results in significant gains in the bit error rate (BER) performance. Finally, we explore alternative ways of learning convolutionalnets for machine vision, making it easier to interpret and simpler to implement than currently used purely supervised nets. In particular, we investigate a framework that combines a neuro-mimetic front end (designed in collaboration with the neuroscientists from the psychology department at UCSB) together with unsupervised feature extraction based on clustering. Supervised classification, using a generic support vector machine (SVM), is applied at the end.We obtain competitive classification results on standard image databases, beating the state of the art for NORB (uniform-normalized) and approaching it for MNIST

A biologically inspired meta-control navigation system for the Psikharpax rat robot

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics

The landscape of combination therapies against glioblastoma:From promises to challenges

We demonstrate in this thesis how new targets can be identified and highlight the challenges that lie in front of us when trying to translate these steps toward the clinic. We conclude that the blood-brain barrier, PD/PK of drugs, and therapy resistance are still major challenges and explain the limited improvement in treatment options for patients with GBM. First, GBM is a diffuse glioma located in the brain where the blood-brain barrier prevents the crossing of drugs and thereby limits the efficacy of treatment. Second, inter- and intratumoral heterogeneity have been observed in GBM leading to different cellular subpopulations with distinctive genetic profiles. Hence, treating these subpopulations with targeted drugs allows until now still survival of certain subpopulations that are not sensitive to this treatment. Lastly, therapy resistance is often seen in GBM patients and is probably related to intratumoral heterogeneity, but the intrinsic molecular mechanism is still not fully understood. Together they lead to the inevitable recurrence of the tumor

Understanding Physiological and Degenerative Natural Vision Mechanisms to Define Contrast and Contour Operators

BACKGROUND:Dynamical systems like neural networks based on lateral inhibition have a large field of applications in image processing, robotics and morphogenesis modeling. In this paper, we will propose some examples of dynamical flows used in image contrasting and contouring. METHODOLOGY:First we present the physiological basis of the retina function by showing the role of the lateral inhibition in the optical illusions and pathologic processes generation. Then, based on these biological considerations about the real vision mechanisms, we study an enhancement method for contrasting medical images, using either a discrete neural network approach, or its continuous version, i.e. a non-isotropic diffusion reaction partial differential system. Following this, we introduce other continuous operators based on similar biomimetic approaches: a chemotactic contrasting method, a viability contouring algorithm and an attentional focus operator. Then, we introduce the new notion of mixed potential Hamiltonian flows; we compare it with the watershed method and we use it for contouring. CONCLUSIONS:We conclude by showing the utility of these biomimetic methods with some examples of application in medical imaging and computed assisted surgery

Actor & Avatar: A Scientific and Artistic Catalog

What kind of relationship do we have with artificial beings (avatars, puppets, robots, etc.)? What does it mean to mirror ourselves in them, to perform them or to play trial identity games with them? Actor & Avatar addresses these questions from artistic and scholarly angles. Contributions on the making of "technical others" and philosophical reflections on artificial alterity are flanked by neuroscientific studies on different ways of perceiving living persons and artificial counterparts. The contributors have achieved a successful artistic-scientific collaboration with extensive visual material

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