STDP-driven networks and the \emph{C. elegans} neuronal network

We study the dynamics of the structure of a formal neural network wherein the strengths of the synapses are governed by spike-timing-dependent plasticity (STDP). For properly chosen input signals, there exists a steady state with a residual network. We compare the motif profile of such a network with that of a real neural network of \emph{C. elegans} and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting network is robust under variations of the model parameters.Comment: 16 pages, 14 figure

The Gap between Intelligence and Mind

The feeling (quale) brings the "Hard Problem" to philosophy of mind. Does the subjective feeling have a non-ignorable impact on Intelligence? If so, can the feeling be realized in Artificial Intelligence (AI)? To discuss the problems, we have to figure out what the feeling means, by giving a clear definition. In this paper, we primarily give some mainstream perspectives on the topic of the mind, especially the topic of the feeling (or qualia, subjective experience, etc.). Then, a definition of the feeling is proposed through a thought experiment, the "semi-transparent room". The feeling, roughly to say, is defined as "a tendency of changing input representations by representing its inner state". Also, a formalized definition is given. The definition does not help to verify "having the feeling", but it helps to provide evidence. Based on the definition, we think these are the hard problems of intelligence — whether the "innate" feeling plays an important role in Intelligence, whether the difference between the "simulated" feeling and the "innate" feeling will have a significant influence on Artificial General Intelligence (AGI), and, if so, where the "innate" feeling comes from and how to make an artificial agent possess it

Fingerprint of topology in quantum oscillations at elevated temperatures

A versatile methodology to detect Dirac or Weyl fermions in topological semimetals by transport or thermodynamic measurements remains an open problem. It is often argued that a $\pi$ phase shift in quantum oscillations directly corresponds to the Berry phase of topological semimetals. However, the oscillation phase is complicated by multiple contributing factors including the orbital magnetic moment, rendering such correspondences ambiguous for a substantial fraction of topological semimetals. Here we propose the temperature dependence of the frequency, ${F}(T)$, rather than the oscillation phase, as a hallmark signature of topology in quantum oscillations. At temperatures comparable to the cyclotron energy, $F(T)$ encodes the energy-derivative of the cyclotron mass -- a quantity that vanishes for conventional Schr\"odinger-type fermions, yet equals the inverse square of the Fermi velocity for Dirac/Weyl fermions. We experimentally observe this temperature dependent frequency in the Dirac semimetal Cd$_3$As$_2$, and quantitatively describe it by a fitting-parameter-free model of Dirac Fermions. It is absent in the topologically trivial metal Bi$_2$O$_2$Se as expected while the material shows a $\pi$ shift of the quantum oscillation phase without any topological origin. We further identify Dirac fermions in LaRhIn$_5$, despite their co-existence with multiple, topologically trivial Fermi pockets contributing the vast majority of transport carriers. This approach requires no ab-initio calculation as input, and is able to identify topological Fermi pockets which are small compared to the Brillouin-zone volume -- both attributes being ideally suited to identify the topological character of heavy fermion materials

Reply to: Low-frequency quantum oscillations in LaRhIn5_5: Dirac point or nodal line?

We thank G.P. Mikitik and Yu.V. Sharlai for contributing this note and the cordial exchange about it. First and foremost, we note that the aim of our paper is to report a methodology to diagnose topological (semi)metals using magnetic quantum oscillations. Thus far, such diagnosis has been based on the phase offset of quantum oscillations, which is extracted from a "Landau fan plot". A thorough analysis of the Onsager-Lifshitz-Roth quantization rules has shown that the famous $\pi$-phase shift can equally well arise from orbital- or spin magnetic moments in topologically trivial systems with strong spin-orbit coupling or small effective masses. Therefore, the "Landau fan plot" does not by itself constitute a proof of a topologically nontrivial Fermi surface. In the paper at hand, we report an improved analysis method that exploits the strong energy-dependence of the effective mass in linearly dispersing bands. This leads to a characteristic temperature dependence of the oscillation frequency which is a strong indicator of nontrivial topology, even for multi-band metals with complex Fermi surfaces. Three materials, Cd$_3$As$_2$, Bi$_2$O$_2$Se and LaRhIn$_5$ served as test cases for this method. Linear band dispersions were detected for Cd$_3$As$_2$, as well as the $F$ $\approx$ 7 T pocket in LaRhIn$_5$.Comment: Response to Matter arising for Nature Communications 12, 6213 (2021

Using an adaptive scheme to reduce the coupling cost in chaotic phase synchronization of complex networks

A new adaptive coupling scheme is introduced into chaotic phase synchronization in complex networks. The coupling could adjust adaptively according to the energy difference between two chaotic oscillators. Thus it needs less total and net energy to compensate the energy exchange of chaotic systems with its environment in synchronization. In complex networks, the adaptive coupling could suppress the negative influence of the heterogeneities in parameter and degree distributions. We investigate the scheme in three kinds of network topologies, i.e. coupled two oscillators, global coupled lattice, and scale-free complex network. The adaptive coupling scheme has better performance in synchronization ability compared with four other schemes

Building a self-learning memristor-based spiking neural network on handwritten digit recognition and orientation extraction

In this paper, we present a memristor-based spiking neural network to identify handwritten digit figure and extract orientation. Digit figures are from MNIST database. Orientation spikes are generated in response to relative changes in illumination at the pixel level and transmitted to the spiking neural network. The network is a two-layered structure consisting of integrate-and-fire neurons and memristor. Memristors are used as synapses in this neural network performing learning. The memristors learn through an adaptation of spike-time-dependent plasticity (STDP) for training. Spiking neurons are arranged in a winner-take-all (WTA) circuit, which is one of the most frequently studied connectivity patterns. Neurons become sensitive to different patterns of pixels through learning. The simulation results illustrate memristors perform well on unsupervised learning and the biologically inspired learning scheme is capable of generating selective to different patterns on digit recognition and orientation extraction. ? 2014 WIT Press.EI

A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons

We apply the Empirical Mode Decomposition (EMD) algorithm and the Time Convolutional Network (TCN) structure, predicated on Convolutional Neural Networks, to successfully enable feature extraction within high-precision optical time-frequency signals, and provide effective identification and alerts for abnormal link states. Experimental validation confirms that the proposed method not only delivers an efficacy on par with traditional manual techniques, but also excels in swiftly identifying anomalies that typically elude conventional approaches. This investigation furnishes novel theoretical backing and forecasting tools for high-precision optical transmission