147 research outputs found
Formal concept matching and reinforcement learning in adaptive information retrieval
The superiority of the human brain in information retrieval (IR) tasks seems to come firstly
from its ability to read and understand the concepts, ideas or meanings central to documents, in
order to reason out the usefulness of documents to information needs, and secondly from its
ability to learn from experience and be adaptive to the environment. In this work we attempt to
incorporate these properties into the development of an IR model to improve document
retrieval. We investigate the applicability of concept lattices, which are based on the theory of
Formal Concept Analysis (FCA), to the representation of documents. This allows the use of
more elegant representation units, as opposed to keywords, in order to better capture
concepts/ideas expressed in natural language text. We also investigate the use of a
reinforcement leaming strategy to learn and improve document representations, based on the
information present in query statements and user relevance feedback. Features or concepts of
each document/query, formulated using FCA, are weighted separately with respect to the
documents they are in, and organised into separate concept lattices according to a subsumption
relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure
known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the
concepts in the lattice representation. This avoids implementation drawbacks faced by other
FCA-based approaches. Retrieval of a document for an information need is based on concept
matching between concept lattice representations of a document and a query. The learning
strategy works by making the similarity of relevant documents stronger and non-relevant
documents weaker for each query, depending on the relevance judgements of the users on
retrieved documents. Our approach is radically different to existing FCA-based approaches in
the following respects: concept formulation; weight assignment to object-attribute pairs; the
representation of each document in a separate concept lattice; and encoding concept lattices in
BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our
learning strategy makes use of relevance feedback information to enhance document
representations, thus making the document representations dynamic and adaptive to the user
interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are
presented and compared with published results. In particular, the performance of the system is
shown to improve significantly as the system learns from experience.The School of Computing,
University of Plymouth, UK
Three Highly Parallel Computer Architectures and Their Suitability for Three Representative Artificial Intelligence Problems
Virtually all current Artificial Intelligence (AI) applications are designed to run on sequential (von Neumann) computer architectures. As a result, current systems do not scale up. As knowledge is added to these systems, a point is reached where their performance quickly degrades. The performance of a von Neumann machine is limited by the bandwidth between memory and processor (the von Neumann bottleneck). The bottleneck is avoided by distributing the processing power across the memory of the computer. In this scheme the memory becomes the processor (a smart memory ).
This paper highlights the relationship between three representative AI application domains, namely knowledge representation, rule-based expert systems, and vision, and their parallel hardware realizations. Three machines, covering a wide range of fundamental properties of parallel processors, namely module granularity, concurrency control, and communication geometry, are reviewed: the Connection Machine (a fine-grained SIMD hypercube), DADO (a medium-grained MIMD/SIMD/MSIMD tree-machine), and the Butterfly (a coarse-grained MIMD Butterflyswitch machine)
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
The neuropharmacological basis of psychedelic-induced visual hallucinations
Psychedelic substances such as psilocybin, LSD and DMT are known for their perceptual effects, mainly the experience of visual hallucinations that can vary from simple geometrical patterns to more complex imagery; these can happen either with open or closed eyes. It is widely accepted that these drugs exert their effects by acting as agonists on serotoninergic synapses at 5-HT2A receptor subtype, which has been proposed to be mainly sited in primary visual cortex. Although, recent studies pointed out that different types of visual hallucinations depend on the dosage ranges and that with higher doses the agonist action on 5-HT2A is seen more broadly on the brain, suggesting a top-down control during complex visual imagery. In this review previous knowledge is integrated with recent literature that includes new approaches and methods to better understand the underlying neurophysiological mechanisms that follow pharmacological interactions of selective 5-HT2A hallucinogens. These methods includes resting state fMRI, HD-EEG/MEG and the neural mechanisms investigated are: functional connectivity changes and plasticity across and within cortical networks; disinhibition, brainwave synchronization and phase-coupling.Psychedelic substances such as psilocybin, LSD and DMT are known for their perceptual effects, mainly the experience of visual hallucinations that can vary from simple geometrical patterns to more complex imagery; these can happen either with open or closed eyes. It is widely accepted that these drugs exert their effects by acting as agonists on serotoninergic synapses at 5-HT2A receptor subtype, which has been proposed to be mainly sited in primary visual cortex. Although, recent studies pointed out that different types of visual hallucinations depend on the dosage ranges and that with higher doses the agonist action on 5-HT2A is seen more broadly on the brain, suggesting a top-down control during complex visual imagery. In this review previous knowledge is integrated with recent literature that includes new approaches and methods to better understand the underlying neurophysiological mechanisms that follow pharmacological interactions of selective 5-HT2A hallucinogens. These methods includes resting state fMRI, HD-EEG/MEG and the neural mechanisms investigated are: functional connectivity changes and plasticity across and within cortical networks; disinhibition, brainwave synchronization and phase-coupling
Self Organisation and Hierarchical Concept Representation in Networks of Spiking Neurons
The aim of this work is to introduce modular processing mechanisms for cortical functions implemented
in networks of spiking neurons. Neural maps are a feature of cortical processing
found to be generic throughout sensory cortical areas, and self-organisation to the fundamental
properties of input spike trains has been shown to be an important property of cortical organisation.
Additionally, oscillatory behaviour, temporal coding of information, and learning through
spike timing dependent plasticity are all frequently observed in the cortex. The traditional
self-organising map (SOM) algorithm attempts to capture the computational properties of this
cortical self-organisation in a neural network. As such, a cognitive module for a spiking SOM
using oscillations, phasic coding and STDP has been implemented. This model is capable of
mapping to distributions of input data in a manner consistent with the traditional SOM algorithm,
and of categorising generic input data sets. Higher-level cortical processing areas appear
to feature a hierarchical category structure that is founded on a feature-based object representation.
The spiking SOM model is therefore extended to facilitate input patterns in the form of
sets of binary feature-object relations, such as those seen in the field of formal concept analysis.
It is demonstrated that this extended model is capable of learning to represent the hierarchical
conceptual structure of an input data set using the existing learning scheme. Furthermore,
manipulations of network parameters allow the level of hierarchy used for either learning or
recall to be adjusted, and the network is capable of learning comparable representations when
trained with incomplete input patterns. Together these two modules provide related approaches
to the generation of both topographic mapping and hierarchical representation of input spaces
that can be potentially combined and used as the basis for advanced spiking neuron models of
the learning of complex representations
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