2,311 research outputs found
Easy attention: A simple self-attention mechanism for Transformers
To improve the robustness of transformer neural networks used for
temporal-dynamics prediction of chaotic systems, we propose a novel attention
mechanism called easy attention. Due to the fact that self attention only makes
usage of the inner product of queries and keys, it is demonstrated that the
keys, queries and softmax are not necessary for obtaining the attention score
required to capture long-term dependencies in temporal sequences. Through
implementing singular-value decomposition (SVD) on the softmax attention score,
we further observe that the self attention compresses contribution from both
queries and keys in the spanned space of the attention score. Therefore, our
proposed easy-attention method directly treats the attention scores as
learnable parameters. This approach produces excellent results when
reconstructing and predicting the temporal dynamics of chaotic systems
exhibiting more robustness and less complexity than the self attention or the
widely-used long short-term memory (LSTM) network. Our results show great
potential for applications in more complex high-dimensional dynamical systems.Comment: 12 pages and 8 figure
Chimera states: Coexistence of coherence and incoherence in networks of coupled oscillators
A chimera state is a spatio-temporal pattern in a network of identical
coupled oscillators in which synchronous and asynchronous oscillation coexist.
This state of broken symmetry, which usually coexists with a stable spatially
symmetric state, has intrigued the nonlinear dynamics community since its
discovery in the early 2000s. Recent experiments have led to increasing
interest in the origin and dynamics of these states. Here we review the history
of research on chimera states and highlight major advances in understanding
their behaviour.Comment: 26 pages, 3 figure
A Compact CMOS Memristor Emulator Circuit and its Applications
Conceptual memristors have recently gathered wider interest due to their
diverse application in non-von Neumann computing, machine learning,
neuromorphic computing, and chaotic circuits. We introduce a compact CMOS
circuit that emulates idealized memristor characteristics and can bridge the
gap between concepts to chip-scale realization by transcending device
challenges. The CMOS memristor circuit embodies a two-terminal variable
resistor whose resistance is controlled by the voltage applied across its
terminals. The memristor 'state' is held in a capacitor that controls the
resistor value. This work presents the design and simulation of the memristor
emulation circuit, and applies it to a memcomputing application of maze solving
using analog parallelism. Furthermore, the memristor emulator circuit can be
designed and fabricated using standard commercial CMOS technologies and opens
doors to interesting applications in neuromorphic and machine learning
circuits.Comment: Submitted to International Symposium of Circuits and Systems (ISCAS)
201
A chaotic spread spectrum system for underwater acoustic communication
The work is supported in part by NSFC (Grant no. 61172070), IRT of Shaanxi Province (2013KCT-04), EPSRC (Grant no.Ep/1032606/1).Peer reviewedPostprin
Wave-based extreme deep learning based on non-linear time-Floquet entanglement
Wave-based analog signal processing holds the promise of extremely fast,
on-the-fly, power-efficient data processing, occurring as a wave propagates
through an artificially engineered medium. Yet, due to the fundamentally weak
non-linearities of traditional wave materials, such analog processors have been
so far largely confined to simple linear projections such as image edge
detection or matrix multiplications. Complex neuromorphic computing tasks,
which inherently require strong non-linearities, have so far remained
out-of-reach of wave-based solutions, with a few attempts that implemented
non-linearities on the digital front, or used weak and inflexible non-linear
sensors, restraining the learning performance. Here, we tackle this issue by
demonstrating the relevance of Time-Floquet physics to induce a strong
non-linear entanglement between signal inputs at different frequencies,
enabling a power-efficient and versatile wave platform for analog extreme deep
learning involving a single, uniformly modulated dielectric layer and a
scattering medium. We prove the efficiency of the method for extreme learning
machines and reservoir computing to solve a range of challenging learning
tasks, from forecasting chaotic time series to the simultaneous classification
of distinct datasets. Our results open the way for wave-based machine learning
with high energy efficiency, speed, and scalability.Comment: 23 pages, 9 figure
Spontaneous and stimulus-induced coherent states of critically balanced neuronal networks
How the information microscopically processed by individual neurons is
integrated and used in organizing the behavior of an animal is a central
question in neuroscience. The coherence of neuronal dynamics over different
scales has been suggested as a clue to the mechanisms underlying this
integration. Balanced excitation and inhibition may amplify microscopic
fluctuations to a macroscopic level, thus providing a mechanism for generating
coherent multiscale dynamics. Previous theories of brain dynamics, however,
were restricted to cases in which inhibition dominated excitation and
suppressed fluctuations in the macroscopic population activity. In the present
study, we investigate the dynamics of neuronal networks at a critical point
between excitation-dominant and inhibition-dominant states. In these networks,
the microscopic fluctuations are amplified by the strong excitation and
inhibition to drive the macroscopic dynamics, while the macroscopic dynamics
determine the statistics of the microscopic fluctuations. Developing a novel
type of mean-field theory applicable to this class of interscale interactions,
we show that the amplification mechanism generates spontaneous, irregular
macroscopic rhythms similar to those observed in the brain. Through the same
mechanism, microscopic inputs to a small number of neurons effectively entrain
the dynamics of the whole network. These network dynamics undergo a
probabilistic transition to a coherent state, as the magnitude of either the
balanced excitation and inhibition or the external inputs is increased. Our
mean-field theory successfully predicts the behavior of this model.
Furthermore, we numerically demonstrate that the coherent dynamics can be used
for state-dependent read-out of information from the network. These results
show a novel form of neuronal information processing that connects neuronal
dynamics on different scales.Comment: 20 pages 12 figures (main text) + 23 pages 6 figures (Appendix); Some
of the results have been removed in the revision in order to reduce the
volume. See the previous version for more result
Robust chimera states in SQUID metamaterials with local interactions
We report on the emergence of robust multi-clustered chimera states in a
dissipative-driven system of symmetrically and locally coupled identical SQUID
oscillators. The "snake-like" resonance curve of the single SQUID
(Superconducting QUantum Interference Device) is the key to the formation of
the chimera states and is responsible for the extreme multistability exhibited
by the coupled system that leads to attractor crowding at the geometrical
resonance (inductive-capacitive) frequency. Until now, chimera states were
mostly believed to exist for nonlocal coupling. Our findings provide
theoretical evidence that nearest neighbor interactions are indeed capable of
supporting such states in a wide parameter range. SQUID metamaterials are the
subject of intense experimental investigations and we are highly confident that
the complex dynamics demonstrated in this manuscript can be confirmed in the
laboratory
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