2,662 research outputs found
A ferrofluid based neural network: design of an analogue associative memory
We analyse an associative memory based on a ferrofluid, consisting of a
system of magnetic nano-particles suspended in a carrier fluid of variable
viscosity subject to patterns of magnetic fields from an array of input and
output magnetic pads. The association relies on forming patterns in the
ferrofluid during a trainingdphase, in which the magnetic dipoles are free to
move and rotate to minimize the total energy of the system. Once equilibrated
in energy for a given input-output magnetic field pattern-pair the particles
are fully or partially immobilized by cooling the carrier liquid. Thus produced
particle distributions control the memory states, which are read out
magnetically using spin-valve sensors incorporated in the output pads. The
actual memory consists of spin distributions that is dynamic in nature,
realized only in response to the input patterns that the system has been
trained for. Two training algorithms for storing multiple patterns are
investigated. Using Monte Carlo simulations of the physical system we
demonstrate that the device is capable of storing and recalling two sets of
images, each with an accuracy approaching 100%.Comment: submitted to Neural Network
Projective simulation for artificial intelligence
We propose a model of a learning agent whose interaction with the environment
is governed by a simulation-based projection, which allows the agent to project
itself into future situations before it takes real action. Projective
simulation is based on a random walk through a network of clips, which are
elementary patches of episodic memory. The network of clips changes
dynamically, both due to new perceptual input and due to certain compositional
principles of the simulation process. During simulation, the clips are screened
for specific features which trigger factual action of the agent. The scheme is
different from other, computational, notions of simulation, and it provides a
new element in an embodied cognitive science approach to intelligent action and
learning. Our model provides a natural route for generalization to
quantum-mechanical operation and connects the fields of reinforcement learning
and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes
retaine
Hierarchical Associative Memory Based on Oscillatory Neural Network
In this thesis we explore algorithms and develop architectures based on emerging nano-device technologies for cognitive computing tasks such as recognition, classification, and vision. In particular we focus on pattern matching in high dimensional vector spaces to address the nearest neighbor search problem. Recent progress in nanotechnology provides us novel nano-devices with special nonlinear response characteristics that fit cognitive tasks better than general purpose computing. We build an associative memory (AM) by weakly coupling nano-oscillators as an oscillatory neural network and design a hierarchical tree structure to organize groups of AM units. For hierarchical recognition, we first examine an architecture where image patterns are partitioned into different receptive fields and processed by individual AM units in lower levels, and then abstracted using sparse coding techniques for recognition at higher levels. A second tree structure model is developed as a more scalable AM architecture for large data sets. In this model, patterns are classified by hierarchical k-means clustering and organized in hierarchical clusters. Then the recognition process is done by comparison between the input patterns and centroids identified in the clustering process. The tree is explored in a "depth-only" manner until the closest image pattern is output. We also extend this search technique to incorporate a branch-and-bound algorithm. The models and corresponding algorithms are tested on two standard face recognition data-sets. We show that the depth-only hierarchical model is very data-set dependent and performs with 97% or 67% recognition when compared to a single large associative memory, while the branch and bound search increases time by only a factor of two compared to the depth-only search
Perspective: Organic electronic materials and devices for neuromorphic engineering
Neuromorphic computing and engineering has been the focus of intense research
efforts that have been intensified recently by the mutation of Information and
Communication Technologies (ICT). In fact, new computing solutions and new
hardware platforms are expected to emerge to answer to the new needs and
challenges of our societies. In this revolution, lots of candidates
technologies are explored and will require leveraging of the pro and cons. In
this perspective paper belonging to the special issue on neuromorphic
engineering of Journal of Applied Physics, we focus on the current achievements
in the field of organic electronics and the potentialities and specificities of
this research field. We highlight how unique material features available
through organic materials can be used to engineer useful and promising
bioinspired devices and circuits. We also discuss about the opportunities that
organic electronic are offering for future research directions in the
neuromorphic engineering field
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Inducing Mild Traumatic Brain Injury in C. elegans via Cavitation-Free Surface Acoustic Wave-Driven Ultrasonic Irradiation.
Mild traumatic brain injury is an all-too-common outcome from modern warfare and sport, and lacks a reproducible model for assessment of potential treatments and protection against it. Here we consider the use of surface acoustic wave (SAW) irradiation of C. elegans worms-without cavitation-as a potential, ethically reasonable animal-on-a-chip model for inducing traumatic brain injury in an animal, producing significant effects on memory and learning that could prove useful in a model that progress from youth to old age in but a few weeks. We show a significant effect by SAW on the ability of worms to learn post-exposure through associative learning chemotaxis. At higher SAW intensity, we find immediate, thorough, but temporary paralysis of the worms. We further explore the importance of homogeneous exposure of the worms to the SAW-driven ultrasound, an aspect poorly controlled in past efforts, if at all, and demonstrate the absence of cavitation through a change in fluids from a standard media for the worms to the exceedingly viscous polyvinyl alcohol. Likewise, we demonstrate that acoustic streaming, when present, is not directly responsible for paralysis nor learning disabilities induced in the worm, but is beneficial at low amplitudes to ensuring homogeneous ultrasound exposure
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