1,206 research outputs found
Process Calculi Abstractions for Biology
Several approaches have been proposed to model biological systems by means of the formal techniques and tools available in computer science. To mention just a few of them, some representations are inspired by Petri Nets theory, and some other by stochastic processes. A most recent approach consists in interpreting the living entities as terms of process calculi where the behavior of the represented systems can be inferred by applying syntax-driven rules. A comprehensive picture of the state of the art of the process calculi approach to biological modeling is still missing. This paper goes in the direction of providing such a picture by presenting a comparative survey of the process calculi that have been used and proposed to describe the behavior of living entities. This is the preliminary version of a paper that was published in Algorithmic Bioprocesses. The original publication is available at http://www.springer.com/computer/foundations/book/978-3-540-88868-
Cognitive computation using neural representations of time and space in the Laplace domain
Memory for the past makes use of a record of what happened when---a function
over past time. Time cells in the hippocampus and temporal context cells in the
entorhinal cortex both code for events as a function of past time, but with
very different receptive fields. Time cells in the hippocampus can be
understood as a compressed estimate of events as a function of the past.
Temporal context cells in the entorhinal cortex can be understood as the
Laplace transform of that function, respectively. Other functional cell types
in the hippocampus and related regions, including border cells, place cells,
trajectory coding, splitter cells, can be understood as coding for functions
over space or past movements or their Laplace transforms. More abstract
quantities, like distance in an abstract conceptual space or numerosity could
also be mapped onto populations of neurons coding for the Laplace transform of
functions over those variables. Quantitative cognitive models of memory and
evidence accumulation can also be specified in this framework allowing
constraints from both behavior and neurophysiology. More generally, the
computational power of the Laplace domain could be important for efficiently
implementing data-independent operators, which could serve as a basis for
neural models of a very broad range of cognitive computations.First author draf
The Lost Art of Mathematical Modelling
We provide a critique of mathematical biology in light of rapid developments
in modern machine learning. We argue that out of the three modelling activities
-- (1) formulating models; (2) analysing models; and (3) fitting or comparing
models to data -- inherent to mathematical biology, researchers currently focus
too much on activity (2) at the cost of (1). This trend, we propose, can be
reversed by realising that any given biological phenomena can be modelled in an
infinite number of different ways, through the adoption of an open/pluralistic
approach. We explain the open approach using fish locomotion as a case study
and illustrate some of the pitfalls -- universalism, creating models of models,
etc. -- that hinder mathematical biology. We then ask how we might rediscover a
lost art: that of creative mathematical modelling.
This article is dedicated to the memory of Edmund Crampin
06031 Abstracts Collection -- Organic Computing -- Controlled Emergence
Organic Computing has emerged recently as a challenging vision for
future information processing systems, based on the insight that we
will soon be surrounded by large collections of autonomous systems
equipped with sensors and actuators to be aware of their environment,
to communicate freely, and to organize themselves in order to perform
the actions and services required. Organic Computing Systems will
adapt dynamically to the current conditions of its environment, they
will be self-organizing, self-configuring, self-healing,
self-protecting, self-explaining, and context-aware.
From 15.01.06 to 20.01.06, the Dagstuhl Seminar 06031 ``Organic
Computing -- Controlled Emergence\u27\u27 was held in the International
Conference and Research Center (IBFI), Schloss Dagstuhl.
The seminar was characterized by the very constructive search for
common ground between engineering and natural sciences, between
informatics on the one hand and biology, neuroscience, and chemistry
on the other. The common denominator was the objective to build
practically usable self-organizing and emergent systems or their
components.
An indicator for the practical orientation of the seminar was the
large number of OC application systems, envisioned or already under
implementation, such as the Internet, robotics, wireless sensor
networks, traffic control, computer vision, organic systems on chip,
an adaptive and self-organizing room with intelligent sensors or
reconfigurable guiding systems for smart office buildings. The
application orientation was also apparent by the large number of
methods and tools presented during the seminar, which might be used as
building blocks for OC systems, such as an evolutionary design
methodology, OC architectures, especially several implementations of
observer/controller structures, measures and measurement tools for
emergence and complexity, assertion-based methods to control
self-organization, wrappings, a software methodology to build
reflective systems, and components for OC middleware.
Organic Computing is clearly oriented towards applications but is
augmented at the same time by more theoretical bio-inspired and
nature-inspired work, such as chemical computing, theory of complex
systems and non-linear dynamics, control mechanisms in insect swarms,
homeostatic mechanisms in the brain, a quantitative approach to
robustness, abstraction and instantiation as a central metaphor for
understanding complex systems.
Compared to its beginnings, Organic Computing is coming of age. The OC
vision is increasingly padded with meaningful applications and usable
tools, but the path towards full OC systems is still complex. There is
progress in a more scientific understanding of emergent processes. In
the future, we must understand more clearly how to open the
configuration space of technical systems for on-line
modification. Finally, we must make sure that the human user remains
in full control while allowing the systems to optimize
An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing
The authors would like to thank the support on this research by the CRISP Project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.Peer reviewedPublisher PD
Contractive Autoencoding for Hierarchical Temporal Memory and Sparse Distributed Representation Binding
Hierarchical Temporal Memory is a brain inspired memory prediction framework modeled after the uniform structure and connectivity of pyramidal neurons found in the human neocortex. Similar to the neocortex, Hierarchical Temporal Memory processes spatiotemporal information for anomaly detection and prediction. A critical component in the Hierarchical Temporal Memory algorithm is the Spatial Pooler, which is responsible for processing feedforward data into sparse distributed representations.
This study addresses three fundamental research questions for Hierarchical Temporal Memory algorithms. What are the metrics for understanding the semantic content of sparse distributed representations? The semantic content and relationships between representations was visualized with uniqueness matrices and dimensionality reduction techniques. How can spatial semantic information in images be encoded into binary representations for the Hierarchical Temporal Memory\u27s Spatial Pooler? A Contractive Autoencoder was exploited to create binary representations containing spatial information from image data. The uniqueness matrix shows that the Contractive Autoencoder encodes spatial information with strong spatial semantic relationships. The final question is how can vector operations of sparse distributed representations be enhanced to produce separable representations? A binding operation that results in a novel vector was implemented as a circular bit shift between two binary vectors. Binding of labeled sparse distributed representations was shown to create separable representations, but more robust representations are limited by vector density
Grammar-Based Set-Theoretic Formalization of Emergence in Complex Systems
Master'sMASTER OF SCIENC
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