9,820 research outputs found
Design and Analysis of Genetically Constructed Logic Gates
Synthetic biology, comprising many aspects including in vivo, in vitro and in silico techniques, models and methods, programming paradigms and tools, is a rapidly growing field with promising potential in building new synthetically constructed devices and systems. Synthetic biology features unconventional biological systems that do not naturally exist in nature. In this paper, we discuss a software platform, Infobiotics Workbench, developed to perform in silico experiments for synthetic biology systems. We utilise the tool on an unconventional system, a genetic logic gate
Towards heterotic computing with droplets in a fully automated droplet-maker platform
The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations
Reservoir Computing Approach to Robust Computation using Unreliable Nanoscale Networks
As we approach the physical limits of CMOS technology, advances in materials
science and nanotechnology are making available a variety of unconventional
computing substrates that can potentially replace top-down-designed
silicon-based computing devices. Inherent stochasticity in the fabrication
process and nanometer scale of these substrates inevitably lead to design
variations, defects, faults, and noise in the resulting devices. A key
challenge is how to harness such devices to perform robust computation. We
propose reservoir computing as a solution. In reservoir computing, computation
takes place by translating the dynamics of an excited medium, called a
reservoir, into a desired output. This approach eliminates the need for
external control and redundancy, and the programming is done using a
closed-form regression problem on the output, which also allows concurrent
programming using a single device. Using a theoretical model, we show that both
regular and irregular reservoirs are intrinsically robust to structural noise
as they perform computation
Modeling adaptation with a tuple-based coordination language
In recent years, it has been argued that systems and applications, in order to deal with their increasing complexity, should be able to adapt their behavior according to new requirements or environment conditions. In this paper, we present a preliminary investigation aiming at studying how coordination languages and formal methods can contribute to a better understanding, implementation and usage of the mechanisms and techniques for adaptation currently proposed in the literature. Our study relies on the formal coordination language Klaim as a common framework for modeling some adaptation techniques, namely the MAPE-K loop, aspect- and context-oriented programming
Concepts and Paradigms for Neuromorphic Programming
The value of neuromorphic computers depends crucially on our ability to
program them for relevant tasks. Currently, neuromorphic computers are mostly
limited to machine learning methods adapted from deep learning. However,
neuromorphic computers have potential far beyond deep learning if we can only
make use of their computational properties to harness their full power.
Neuromorphic programming will necessarily be different from conventional
programming, requiring a paradigm shift in how we think about programming in
general. The contributions of this paper are 1) a conceptual analysis of what
"programming" means in the context of neuromorphic computers and 2) an
exploration of existing programming paradigms that are promising yet overlooked
in neuromorphic computing. The goal is to expand the horizon of neuromorphic
programming methods, thereby allowing researchers to move beyond the shackles
of current methods and explore novel directions
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