97 research outputs found
Digital neural circuits : from ions to networks
PhD ThesisThe biological neural computational mechanism is always fascinating to human beings since it shows several state-of-the-art characteristics: strong fault tolerance, high power efficiency and self-learning capability. These behaviours lead the developing trend of designing the next-generation digital computation platform. Thus investigating and understanding how the neurons talk with each other is the key to replicating these calculation features. In this work I emphasize using tailor-designed digital circuits for exactly implementing bio-realistic neural network behaviours, which can be considered a novel approach to cognitive neural computation. The first advance is that biological real-time computing performances allow the presented circuits to be readily adapted for real-time closed-loop in vitro or in vivo experiments, and the second one is a transistor-based circuit that can be directly translated into an impalpable chip for high-level neurologic disorder rehabilitations. In terms of the methodology, first I focus on designing a heterogeneous or multiple-layer-based architecture for reproducing the finest neuron activities both in voltage-and calcium-dependent ion channels. In particular, a digital optoelectronic neuron is developed as a case study. Second, I focus on designing a network-on-chip architecture for implementing a very large-scale neural network (e.g. more than 100,000) with human cognitive functions (e.g. timing control mechanism). Finally, I present a reliable hybrid bio-silicon closed-loop system for central pattern generator prosthetics, which can be considered as a framework for digital neural circuit-based neuro-prosthesis implications. At the end, I present the general digital neural circuit design principles and the long-term social impacts of the presented work
What role should non-academics have in evaluating the potential impact of new research projects?
Non-academics with extensive experience of particular sectors and industries can provide unique insights into the potential pathways to impact for new research projects. Drawing on a quasi-natural experiment comparing assessment panels with and without non-academic experts, Junwen Luo discusses the how these skills were perceived by academics and how the inclusion of non-academics might benefit from clearer definitions of impact in applied research grants
Single channel based interference-free and self-powered human-machine interactive interface using eigenfrequency-dominant mechanism
The recent development of wearable devices is revolutionizing the way of
human-machine interaction (HMI). Nowadays, an interactive interface that
carries more embedded information is desired to fulfil the increasing demand in
era of Internet of Things. However, present approach normally relies on sensor
arrays for memory expansion, which inevitably brings the concern of wiring
complexity, signal differentiation, power consumption, and miniaturization.
Herein, a one-channel based self-powered HMI interface, which uses the
eigenfrequency of magnetized micropillar (MMP) as identification mechanism, is
reported. When manually vibrated, the inherent recovery of the MMP caused a
damped oscillation that generates current signals because of Faraday's Law of
induction. The time-to-frequency conversion explores the MMP-related
eigenfrequency, which provides a specific solution to allocate diverse commands
in an interference-free behavior even with one electric channel. A cylindrical
cantilever model was built to regulate the MMP eigenfrequencies via precisely
designing the dimensional parameters and material properties. We show that
using one device and two electrodes, high-capacity HMI interface can be
realized when the MMPs with different eigenfrequencies have been integrated.
This study provides the reference value to design the future HMI system
especially for situations that require a more intuitive and intelligent
communication experience with high-memory demand.Comment: 35 pages, 6 figure
Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning
Depth-aware panoptic segmentation is an emerging topic in computer vision
which combines semantic and geometric understanding for more robust scene
interpretation. Recent works pursue unified frameworks to tackle this challenge
but mostly still treat it as two individual learning tasks, which limits their
potential for exploring cross-domain information. We propose a deeply unified
framework for depth-aware panoptic segmentation, which performs joint
segmentation and depth estimation both in a per-segment manner with identical
object queries. To narrow the gap between the two tasks, we further design a
geometric query enhancement method, which is able to integrate scene geometry
into object queries using latent representations. In addition, we propose a
bi-directional guidance learning approach to facilitate cross-task feature
learning by taking advantage of their mutual relations. Our method sets the new
state of the art for depth-aware panoptic segmentation on both Cityscapes-DVPS
and SemKITTI-DVPS datasets. Moreover, our guidance learning approach is shown
to deliver performance improvement even under incomplete supervision labels.Comment: to be published in ICCV 202
A Consumer-tier based Visual-Brain Machine Interface for Augmented Reality Glasses Interactions
Objective.Visual-Brain Machine Interface(V-BMI) has provide a novel
interaction technique for Augmented Reality (AR) industries. Several
state-of-arts work has demonstates its high accuracy and real-time interaction
capbilities. However, most of the studies employ EEGs devices that are rigid
and difficult to apply in real-life AR glasseses application sceniraros. Here
we develop a consumer-tier Visual-Brain Machine Inteface(V-BMI) system
specialized for Augmented Reality(AR) glasses interactions. Approach. The
developed system consists of a wearable hardware which takes advantages of fast
set-up, reliable recording and comfortable wearable experience that
specificized for AR glasses applications. Complementing this hardware, we have
devised a software framework that facilitates real-time interactions within the
system while accommodating a modular configuration to enhance scalability. Main
results. The developed hardware is only 110g and 120x85x23 mm, which with 1
Tohm and peak to peak voltage is less than 1.5 uV, and a V-BMI based angry bird
game and an Internet of Thing (IoT) AR applications are deisgned, we
demonstrated such technology merits of intuitive experience and efficiency
interaction. The real-time interaction accuracy is between 85 and 96
percentages in a commercial AR glasses (DTI is 2.24s and ITR 65 bits-min ).
Significance. Our study indicates the developed system can provide an essential
hardware-software framework for consumer based V-BMI AR glasses. Also, we
derive several pivotal design factors for a consumer-grade V-BMI-based AR
system: 1) Dynamic adaptation of stimulation patterns-classification methods
via computer vision algorithms is necessary for AR glasses applications; and 2)
Algorithmic localization to foster system stability and latency reduction.Comment: 15 pages,10 figure
Optogenetics in silicon: A neural processor for predicting optically active neural networks
We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin-Huxley CA3 neuron integrated with a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-built computing data-path, a separate data management system and a memory approach based router. Advancements over previous work include the incorporation of short and long-term calcium and light-dependent ion channels in reconfigurable hardware. Also, the developed processor is computationally efficient, requiring only 0.03 ms processing time per sub-frame for a single neuron and 9.7 ms for a fully connected network of 500 neurons with a given FPGA frequency of 56.7 MHz. It can therefore be utilized for exploration of closed loop processing and tuning of biologically realistic optogenetic circuitry
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