38,204 research outputs found
How single neuron properties shape chaotic dynamics and signal transmission in random neural networks
While most models of randomly connected networks assume nodes with simple
dynamics, nodes in realistic highly connected networks, such as neurons in the
brain, exhibit intrinsic dynamics over multiple timescales. We analyze how the
dynamical properties of nodes (such as single neurons) and recurrent
connections interact to shape the effective dynamics in large randomly
connected networks. A novel dynamical mean-field theory for strongly connected
networks of multi-dimensional rate units shows that the power spectrum of the
network activity in the chaotic phase emerges from a nonlinear sharpening of
the frequency response function of single units. For the case of
two-dimensional rate units with strong adaptation, we find that the network
exhibits a state of "resonant chaos", characterized by robust, narrow-band
stochastic oscillations. The coherence of stochastic oscillations is maximal at
the onset of chaos and their correlation time scales with the adaptation
timescale of single units. Surprisingly, the resonance frequency can be
predicted from the properties of isolated units, even in the presence of
heterogeneity in the adaptation parameters. In the presence of these
internally-generated chaotic fluctuations, the transmission of weak,
low-frequency signals is strongly enhanced by adaptation, whereas signal
transmission is not influenced by adaptation in the non-chaotic regime. Our
theoretical framework can be applied to other mechanisms at the level of single
nodes, such as synaptic filtering, refractoriness or spike synchronization.
These results advance our understanding of the interaction between the dynamics
of single units and recurrent connectivity, which is a fundamental step toward
the description of biologically realistic network models in the brain, or, more
generally, networks of other physical or man-made complex dynamical units
Noise-induced volatility of collective dynamics
"Noise-induced volatility" refers to a phenomenon of increased level of
fluctuations in the collective dynamics of bistable units in the presence of a
rapidly varying external signal, and intermediate noise levels. The
archetypical signature of this phenomenon is that --beyond the increase in the
level of fluctuations-- the response of the system becomes uncorrelated with
the external driving force, making it different from stochastic resonance.
Numerical simulations and an analytical theory of a stochastic dynamical
version of the Ising model on regular and random networks demonstrate the
ubiquity and robustness of this phenomenon, which is argued to be a possible
cause of excess volatility in financial markets, of enhanced effective
temperatures in a variety of out-of-equilibrium systems and of strong selective
responses of immune systems of complex biological organisms. Extensive
numerical simulations are compared with a mean-field theory for different
network topologies
Stochastic resonance in electrical circuits—II: Nonconventional stochastic resonance.
Stochastic resonance (SR), in which a periodic signal in a nonlinear system can be amplified by added noise, is discussed. The application of circuit modeling techniques to the conventional form of SR, which occurs in static bistable potentials, was considered in a companion paper. Here, the investigation of nonconventional forms of SR in part using similar electronic techniques is described. In the small-signal limit, the results are well described in terms of linear response theory. Some other phenomena of topical interest, closely related to SR, are also treate
Dynamical amplification of magnetoresistances and Hall currents up to the THz regime
Spin-orbit-related effects offer a highly promising route for reading and
writing information in magnetic units of future devices. These phenomena rely
not only on the static magnetization orientation but also on its dynamics to
achieve fast switchings that can reach the THz range. In this work, we consider
Co/Pt and Fe/W bilayers to show that accounting for the phase difference
between different processes is crucial to the correct description of the
dynamical currents. By tuning each system towards its ferromagnetic resonance,
we reveal that dynamical spin Hall angles can non-trivially change sign and be
boosted by over 500%, reaching giant values. We demonstrate that charge and
spin pumping mechanisms can greatly magnify or dwindle the currents flowing
through the system, influencing all kinds of magnetoresistive and Hall effects,
thus impacting also dc and second harmonic experimental measurements.Comment: 19 pages, 4 figures, Supplementary Informatio
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