677 research outputs found
A Role for Bottom-Up Synthetic Cells in the Internet of Bio-Nano Things?
The potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting "molecular communication" (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, "bottom-up" SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT
A Role for Bottom-Up Synthetic Cells in the Internet of Bio-Nano Things?
he potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting “molecular communication” (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, “bottom-up” SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Shortcuts to Thermodynamic Computing: The Cost of Fast and Faithful Erasure
Landauer's Principle states that the energy cost of information processing
must exceed the product of the temperature and the change in Shannon entropy of
the information-bearing degrees of freedom. However, this lower bound is
achievable only for quasistatic, near-equilibrium computations -- that is, only
over infinite time. In practice, information processing takes place in finite
time, resulting in dissipation and potentially unreliable logical outcomes. For
overdamped Langevin dynamics, we show that counterdiabatic potentials can be
crafted to guide systems rapidly and accurately along desired computational
paths, providing shortcuts that allows for the precise design of finite-time
computations. Such shortcuts require additional work, beyond Landauer's bound,
that is irretrievably dissipated into the environment. We show that this
dissipated work is proportional to the computation rate as well as the square
of the information-storing system's length scale. As a paradigmatic example, we
design shortcuts to erase a bit of information metastably stored in a
double-well potential. Though dissipated work generally increases with erasure
fidelity, we show that it is possible perform perfect erasure in finite time
with finite work. We also show that the robustness of information storage
affects the energetic cost of erasure---specifically, the dissipated work
scales as the information lifetime of the bistable system. Our analysis exposes
a rich and nuanced relationship between work, speed, size of the
information-bearing degrees of freedom, storage robustness, and the difference
between initial and final informational statistics.Comment: 19 pages, 7 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/scte.ht
Correlation-powered Information Engines and the Thermodynamics of Self-Correction
Information engines can use structured environments as a resource to generate
work by randomizing ordered inputs and leveraging the increased Shannon entropy
to transfer energy from a thermal reservoir to a work reservoir. We give a
broadly applicable expression for the work production of an information engine,
generally modeled as a memoryful channel that communicates inputs to outputs as
it interacts with an evolving environment. The expression establishes that an
information engine must have more than one memory state in order to leverage
input environment correlations. To emphasize this functioning, we designed an
information engine powered solely by temporal correlations and not by
statistical biases, as employed by previous engines. Key to this is the
engine's ability to synchronize---the engine automatically returns to a desired
dynamical phase when thrown into an unwanted, dissipative phase by corruptions
in the input---that is, by unanticipated environmental fluctuations. This
self-correcting mechanism is robust up to a critical level of corruption,
beyond which the system fails to act as an engine. We give explicit analytical
expressions for both work and critical corruption level and summarize engine
performance via a thermodynamic-function phase diagram over engine control
parameters. The results reveal a new thermodynamic mechanism based on
nonergodicity that underlies error correction as it operates to support
resilient engineered and biological systems.Comment: 22 pages, 13 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/tos.ht
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