33,187 research outputs found
A biologically inspired computational model of the Block Copying Task
We present in this paper a biologically inspired model of the Basal Ganglia which deals with Block Copying as a sequence learning task. By breaking a relatively complex task into simpler operations with well-defined skills, an approach which is termed as a skill-based machine design is used in the device of computational models to complete such tasks. Basal Ganglia are critically involved in sensorimotor control. From the learning aspects, Actor-Critic architectures have been proposed to model the Basal Ganglia and Temporal difference has been proposed as a learning algorithm. The model is implemented and simulation results are presented to show the capability of our model to successfully complete the task
Computational Capacity and Energy Consumption of Complex Resistive Switch Networks
Resistive switches are a class of emerging nanoelectronics devices that
exhibit a wide variety of switching characteristics closely resembling
behaviors of biological synapses. Assembled into random networks, such
resistive switches produce emerging behaviors far more complex than that of
individual devices. This was previously demonstrated in simulations that
exploit information processing within these random networks to solve tasks that
require nonlinear computation as well as memory. Physical assemblies of such
networks manifest complex spatial structures and basic processing capabilities
often related to biologically-inspired computing. We model and simulate random
resistive switch networks and analyze their computational capacities. We
provide a detailed discussion of the relevant design parameters and establish
the link to the physical assemblies by relating the modeling parameters to
physical parameters. More globally connected networks and an increased network
switching activity are means to increase the computational capacity linearly at
the expense of exponentially growing energy consumption. We discuss a new
modular approach that exhibits higher computational capacities and energy
consumption growing linearly with the number of networks used. The results show
how to optimize the trade-off between computational capacity and energy
efficiency and are relevant for the design and fabrication of novel computing
architectures that harness random assemblies of emerging nanodevices
Computational model of MST neuron receptive field and interaction effect for the perception of self-motion
Biologically plausible approach is an alternative to conventional engineering approaches when developing algorithms for intelligent systems. It is apparent that biologically inspired algorithms may yield more expensive calculations when comparing its run time to the more commonly used engineering algorithms. However, biologically inspired approaches have great potential in generating better and more accurate outputs as healthy human brains. Therefore more and more new and exciting researches are being experimented everyday in hope to develop better models of our brain that can be utilized by the machines. This thesis work is an effort to design and implement a computational model of neurons from the visual cortex\u27s MST area (medial superior temporal area). MST\u27s primary responsibility is detecting self-motion from optic flow stimulus that are segmented from the visual input. The computational models are to be built with dual Gaussian functions and genetic algorithm as its principle training method, from the data collected through lab monkey\u27s MST neurons. The resulting computational models can be used in further researches as part of motion detection mechanism by machine vision applications, which may prove to be an effective alternative motion detection algorithm in contrast to the conventional computer vision algorithms such as frame differencing. This thesis work will also explore the interaction effect that has been discovered from the newly gathered data, provided by University of Rochester Medical Center, Neurology Department
Mechanical prions: Self-assembling microstructures
Prions are misfolded proteins that transmit their structural arrangement to
neighboring proteins. In biological systems, prion dynamics can produce a
variety of complex functional outcomes. Yet, an understanding of prionic causes
has been hampered by the fact that few computational models exist that allow
for experimental design, hypothesis testing, and control. Here, we identify
essential prionic properties and present a biologically inspired model of
prions using simple mechanical structures capable of undergoing complex
conformational change. We demonstrate the utility of our approach by designing
a prototypical mechanical prion and validating its properties experimentally.
Our work provides a design framework for harnessing and manipulating prionic
properties in natural and artificial systems.Comment: Added supplements, 25 pages, 11 figure
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
- …