A USB3.0 FPGA Event-based Filtering and Tracking Framework for Dynamic Vision Sensors

Dynamic vision sensors (DVS) are frame-free sensors with an asynchronous variable-rate output that is ideal for hard real-time dynamic vision applications under power and latency constraints. Post-processing of the digital sensor output can reduce sensor noise, extract low level features, and track objects using simple algorithms that have previously been implemented in software. In this paper we present an FPGA-based framework for event-based processing that allows uncorrelated-event noise removal and real-time tracking of multiple objects, with dynamic capabilities to adapt itself to fast or slow and large or small objects. This framework uses a new hardware platform based on a Lattice FPGA which filters the sensor output and which then transmits the results through a super-speed Cypress FX3 USB microcontroller interface to a host computer. The packets of events and timestamps are transmitted to the host computer at rates of 10 Mega events per second. Experimental results are presented that demonstrate a low latency of 10us for tracking and computing the center of mass of a detected object.Ministerio de Economía y Competitividad TEC2012-37868-C04-0

Using FPGA for visuo-motor control with a silicon retina and a humanoid robot

The address-event representation (AER) is a neuromorphic communication protocol for transferring asynchronous events between VLSI chips. The event information is transferred using a high speed digital parallel bus. This paper present an experiment based on AER for visual sensing, processing and finally actuating a robot. The AER output of a silicon retina is processed by an AER filter implemented into a FPGA to produce a mimicking behaviour in a humanoid robot (The RoboSapiens V2). We have implemented the visual filter into the Spartan II FPGA of the USB-AER platform and the Central Pattern Generator (CPG) into the Spartan 3 FPGA of the AER-Robot platform, both developed by authors.Unión Europea IST-2001-34124 (CAVIAR)Ministerio de Ciencia y Tecnología TIC-2003-08164-C03-0

Analog VLSI Phototransduction by continuous-time, adaptive, logarithmic photoreceptor circuits

Over the last few years, we and others have built a number of interesting neuromorphic analog vision chips that do focal-plane time-domain computation. These chips do local, continuous-time, spatiotemporal processing that takes place before any sampling or long-range communication, for example, motion processing, change detection, neuromorphic retinal preprocessing, stereo image matching, and synthesis of auditory images from visual scenes. This processing requires photoreceptor circuits that transduce from light falling on the chip to an electrical signal. If we want to build analog vision chips that do high-quality focal plane processing, then we need good photoreceptors. It's not enough to just demonstrate a concept; ultimate usefulness will be determined by market forces, which, among other factors, depend a lot on raw performance. The receptor circuits we discuss here have not been used in any commercial product, so they have not yet passed that most crucial test, but by every performance metric we can come up with, including successful fabrication and test of demonstration systems, they match performance criteria met by other phototransduction techniques that are used in end-product consumer electronic devices. We hope that this article will serve several purposes: We want people to have a reference where they can look to see the functioning and practical problems of phototransducers built in a typical CMOS or BiCMOS process. We want to inspire people to build low-power, integrated commercial vision devices for practical purposes. We want to provide a photoreceptor that can be used as a front end transducer in more advanced research on neuromorphic systems. The transduction process seems mundane, but it is important --GIGO comes to mind. Subsequent computation relies on the information. We don't know of any contemporary (VLSI-era) literature that comprehensively explore the subject. Previous results are lacking in some aspect, either in the circuit itself, or in the understanding of the physics, or in the realistic measurement of limitations on behavior. We'll focus on one highly-evolved adaptive receptor circuit to understand how it operates, what are the limitations on its dynamic range, and what is the physics of the noise behavior. The receptor has new and previously unpublished technical improvements, and we understand the noise properties and illumination limits much better than we did before. We'll also discuss the practical aspects of the interaction of light with silicon: What are the spectral responses of various devices? How far do light-generated minority carriers diffuse and how do they affect circuit operation? How effective are guard bars to protect against them? Finally, we'll talk about biological receptors: How do their functional characteristics inspire the electronic model? How are the mechanisms of gain and adaptation related

On the Dominance of Trivial Knots among SAPs on a Cubic Lattice

The knotting probability is defined by the probability with which an $N$-step self-avoiding polygon (SAP) with a fixed type of knot appears in the configuration space. We evaluate these probabilities for some knot types on a simple cubic lattice. For the trivial knot, we find that the knotting probability decays much slower for the SAP on the cubic lattice than for continuum models of the SAP as a function of $N$. In particular the characteristic length of the trivial knot that corresponds to a `half-life' of the knotting probability is estimated to be $2.5 \times 10^5$ on the cubic lattice.Comment: LaTeX2e, 21 pages, 8 figur

Critical exponents for random knots

The size of a zero thickness (no excluded volume) polymer ring is shown to scale with chain length $N$ in the same way as the size of the excluded volume (self-avoiding) linear polymer, as $N^{\nu}$, where $\nu \approx 0.588$. The consequences of that fact are examined, including sizes of trivial and non-trivial knots.Comment: 4 pages, 0 figure

Overview of mathematical approaches used to model bacterial chemotaxis I: the single cell

Mathematical modeling of bacterial chemotaxis systems has been influential and insightful in helping to understand experimental observations. We provide here a comprehensive overview of the range of mathematical approaches used for modeling, within a single bacterium, chemotactic processes caused by changes to external gradients in its environment. Specific areas of the bacterial system which have been studied and modeled are discussed in detail, including the modeling of adaptation in response to attractant gradients, the intracellular phosphorylation cascade, membrane receptor clustering, and spatial modeling of intracellular protein signal transduction. The importance of producing robust models that address adaptation, gain, and sensitivity are also discussed. This review highlights that while mathematical modeling has aided in understanding bacterial chemotaxis on the individual cell scale and guiding experimental design, no single model succeeds in robustly describing all of the basic elements of the cell. We conclude by discussing the importance of this and the future of modeling in this area

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

The Generalized Second Law implies a Quantum Singularity Theorem

The generalized second law can be used to prove a singularity theorem, by generalizing the notion of a trapped surface to quantum situations. Like Penrose's original singularity theorem, it implies that spacetime is null geodesically incomplete inside black holes, and to the past of spatially infinite Friedmann--Robertson--Walker cosmologies. If space is finite instead, the generalized second law requires that there only be a finite amount of entropy producing processes in the past, unless there is a reversal of the arrow of time. In asymptotically flat spacetime, the generalized second law also rules out traversable wormholes, negative masses, and other forms of faster-than-light travel between asymptotic regions, as well as closed timelike curves. Furthermore it is impossible to form baby universes which eventually become independent of the mother universe, or to restart inflation. Since the semiclassical approximation is used only in regions with low curvature, it is argued that the results may hold in full quantum gravity. An introductory section describes the second law and its time-reverse, in ordinary and generalized thermodynamics, using either the fine-grained or the coarse-grained entropy. (The fine-grained version is used in all results except those relating to the arrow of time.) A proof of the coarse-grained ordinary second law is given.Comment: 46 pages, 8 figures. v2: discussion of global hyperbolicity revised (4.1, 5.2), more comments on AdS. v3: major revisions including change of title. v4: similar to published version, but with corrections to plan of paper (1) and definition of global hyperbolicity (3.2). v5: fixed proof of Thm. 1, changed wording of Thm. 3 & proof of Thm. 4, revised Sec. 5.2, new footnote

Time-delayed spread of viruses in growing plaques

The spread of viruses in growing plaques predicted by classical models is greater than that measured experimentally. There is a widespread belief that this discrepancy is due to biological factors. Here we show that the observed speeds can be satisfactorily predicted by a purely physical model that takes into account the delay time due to virus reproduction inside infected cells. No free or adjustable parameters are used