453 research outputs found
Deep Neural Networks - A Brief History
Introduction to deep neural networks and their history.Comment: 14 pages, 14 figure
Ethics of Artificial Intelligence Demarcations
In this paper we present a set of key demarcations, particularly important
when discussing ethical and societal issues of current AI research and
applications. Properly distinguishing issues and concerns related to Artificial
General Intelligence and weak AI, between symbolic and connectionist AI, AI
methods, data and applications are prerequisites for an informed debate. Such
demarcations would not only facilitate much-needed discussions on ethics on
current AI technologies and research. In addition sufficiently establishing
such demarcations would also enhance knowledge-sharing and support rigor in
interdisciplinary research between technical and social sciences.Comment: Proceedings of the Norwegian AI Symposium 2019 (NAIS 2019),
Trondheim, Norwa
Sequence learning in Associative Neuronal-Astrocytic Network
The neuronal paradigm of studying the brain has left us with limitations in
both our understanding of how neurons process information to achieve biological
intelligence and how such knowledge may be translated into artificial
intelligence and its most brain-derived branch, neuromorphic computing.
Overturning our fundamental assumptions of how the brain works, the recent
exploration of astrocytes is revealing that these long-neglected brain cells
dynamically regulate learning by interacting with neuronal activity at the
synaptic level. Following recent experimental evidence, we designed an
associative, Hopfield-type, neuronal-astrocytic network and analyzed the
dynamics of the interaction between neurons and astrocytes. We show that
astrocytes were sufficient to trigger transitions between learned memories in
the neuronal component of the network. Further, we mathematically derived the
timing of the transitions that was governed by the dynamics of the
calcium-dependent slow-currents in the astrocytic processes. Overall, we
provide a brain-morphic mechanism for sequence learning that is inspired by,
and aligns with, recent experimental findings. To evaluate our model, we
emulated astrocytic atrophy and showed that memory recall becomes significantly
impaired after a critical point of affected astrocytes was reached. This
brain-inspired and brain-validated approach supports our ongoing efforts to
incorporate non-neuronal computing elements in neuromorphic information
processing.Comment: 8 pages, 5 figure
An Assessment of Students’ Satisfaction in Higher Education
Student’s Satisfaction (SS) with a particular subject may impact the learning process, being the figure of attentiveness of the utmost importance over time, and also a very difficult undertaking to accomplish. To go forward with such exercise, a workable methodology for problem solving had to be built and tested. It is based on a thermodynamic approach to Knowledge Representation and Reasoning, which is the ultimate goal of SS assessment when working on a particular topic
Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings
Explicit Logic Circuits Discriminate Neural States
The magnitude and apparent complexity of the brain's connectivity have left explicit networks largely unexplored. As a result, the relationship between the organization of synaptic connections and how the brain processes information is poorly understood. A recently proposed retinal network that produces neural correlates of color vision is refined and extended here to a family of general logic circuits. For any combination of high and low activity in any set of neurons, one of the logic circuits can receive input from the neurons and activate a single output neuron whenever the input neurons have the given activity state. The strength of the output neuron's response is a measure of the difference between the smallest of the high inputs and the largest of the low inputs. The networks generate correlates of known psychophysical phenomena. These results follow directly from the most cost-effective architectures for specific logic circuits and the minimal cellular capabilities of excitation and inhibition. The networks function dynamically, making their operation consistent with the speed of most brain functions. The networks show that well-known psychophysical phenomena do not require extraordinarily complex brain structures, and that a single network architecture can produce apparently disparate phenomena in different sensory systems
Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective
Deep learning is presently attracting extra ordinary attention from
both the industry and the academia. The application of deep learning in computer
vision has recently gain popularity. The optimization of deep learning models
through nature inspired algorithms is a subject of debate in computer science. The
application areas of the hybrid of natured inspired algorithms and deep learning
architecture includes: machine vision and learning, image processing, data science,
autonomous vehicles, medical image analysis, biometrics, etc. In this paper,
we present recent progress on the application of nature inspired algorithms in
deep learning. The survey pointed out recent development issues, strengths,
weaknesses and prospects for future research. A new taxonomy is created based
on natured inspired algorithms for deep learning. The trend of the publications in
this domain is depicted; it shows the research area is growing but slowly. The
deep learning architectures not exploit by the nature inspired algorithms for
optimization are unveiled. We believed that the survey can facilitate synergy
between the nature inspired algorithms and deep learning research communities.
As such, massive attention can be expected in a near future
Boolean Dynamics with Random Couplings
This paper reviews a class of generic dissipative dynamical systems called
N-K models. In these models, the dynamics of N elements, defined as Boolean
variables, develop step by step, clocked by a discrete time variable. Each of
the N Boolean elements at a given time is given a value which depends upon K
elements in the previous time step.
We review the work of many authors on the behavior of the models, looking
particularly at the structure and lengths of their cycles, the sizes of their
basins of attraction, and the flow of information through the systems. In the
limit of infinite N, there is a phase transition between a chaotic and an
ordered phase, with a critical phase in between.
We argue that the behavior of this system depends significantly on the
topology of the network connections. If the elements are placed upon a lattice
with dimension d, the system shows correlations related to the standard
percolation or directed percolation phase transition on such a lattice. On the
other hand, a very different behavior is seen in the Kauffman net in which all
spins are equally likely to be coupled to a given spin. In this situation,
coupling loops are mostly suppressed, and the behavior of the system is much
more like that of a mean field theory.
We also describe possible applications of the models to, for example, genetic
networks, cell differentiation, evolution, democracy in social systems and
neural networks.Comment: 69 pages, 16 figures, Submitted to Springer Applied Mathematical
Sciences Serie
Probing the relaxation towards equilibrium in an isolated strongly correlated 1D Bose gas
The problem of how complex quantum systems eventually come to rest lies at
the heart of statistical mechanics. The maximum entropy principle put forward
in 1957 by E. T. Jaynes suggests what quantum states one should expect in
equilibrium but does not hint as to how closed quantum many-body systems
dynamically equilibrate. A number of theoretical and numerical studies
accumulate evidence that under specific conditions quantum many-body models can
relax to a situation that locally or with respect to certain observables
appears as if the entire system had relaxed to a maximum entropy state. In this
work, we report the experimental observation of the non-equilibrium dynamics of
a density wave of ultracold bosonic atoms in an optical lattice in the regime
of strong correlations. Using an optical superlattice, we are able to prepare
the system in a well-known initial state with high fidelity. We then follow the
dynamical evolution of the system in terms of quasi-local densities, currents,
and coherences. Numerical studies based on the time-dependent density-matrix
renormalization group method are in an excellent quantitative agreement with
the experimental data. For very long times, all three local observables show a
fast relaxation to equilibrium values compatible with those expected for a
global maximum entropy state. We find this relaxation of the quasi-local
densities and currents to initially follow a power-law with an exponent being
significantly larger than for free or hardcore bosons. For intermediate times
the system fulfills the promise of being a dynamical quantum simulator, in that
the controlled dynamics runs for longer times than present classical algorithms
based on matrix product states can efficiently keep track of.Comment: 8 pages, 6 figure
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