Noise-driven neuromorphic tuned amplifier

Fanelli, Duccio; Ginelli, Francesco; Livi, Roberto; Zagli, Niccolò; Zankoc, Clement

doi:10.1103/physreve.96.062313

Cited by 16 publications

(33 citation statements)

References 35 publications

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“…Single-cell oscillations are believed to be driven by molecular noise, induced by the small number of molecules present in each cell, and therefore disappearing in the thermodynamic limit [107]. Recently, another possible mechanism leading to fluctuation amplification in a feedforward chain has been suggested as a pacemaking mechanism for biological systems; in this context the amplitude of the oscillations grows with the system size [108]. Instead, in our case, the dynamical balance provides intrinsic noise and oscillations of constant amplitude, essentially independent from the number of synaptic inputs (in-degree) and from the number of neurons in the network.…”

Section: Discussionmentioning

confidence: 99%

Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons

Segneri

Volo

et al. 2020

Phys. Rev. Research

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Oscillations are a hallmark of neural population activity in various brain regions with a spectrum covering a wide range of frequencies. Within this spectrum γ oscillations have received particular attention due to their ubiquitous nature and their correlation with higher brain functions. Recently, it has been reported that γ oscillations in the hippocampus of behaving rodents are segregated in two distinct frequency bands: slow and fast. These two γ rhythms correspond to different states of the network, but their origin has been not yet clarified.Here we show theoretically and numerically that a single inhibitory population can give rise to coexisting slow and fast γ rhythms corresponding to collective oscillations of a balanced spiking network. The slow and fast γ rhythms are generated via two different mechanisms: the fast one being driven by the coordinated tonic neural firing and the slow one by endogenous fluctuations due to irregular neural activity. We show that almost instantaneous stimulations can switch the collective γ oscillations from slow to fast and vice versa. Furthermore, to draw a connection with the experimental observations, we consider the modulation of the γ rhythms induced by a slower (θ) rhythm driving the network dynamics. In this context, depending on the strength of the forcing and the noise amplitude, we observe phase-amplitude and phase-phase coupling between the fast and slow γ oscillations and the θ forcing. Phase-phase coupling reveals on average different θ-phase preferences for the two coexisting γ rhythms joined to a wide cycle-to-cycle variability.

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Section: Discussionmentioning

confidence: 99%

Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons

Segneri

Volo

et al. 2020

Phys. Rev. Research

View full text Add to dashboard Cite

show abstract

“…Non-normal networks appear ubiquitous, with strong non-normality having been found in food webs, transport, and biological, social, communication, and citation networks (22). In addition, we mention that, besides information transfer, non-normality turns out to be the key to explaining and understanding a variety of other equally important phenomena, for instance, the process of pattern formation in natural and biological systems (21,35), the selective amplification of cortical activity patterns in the brain (32), and the emergence of giant oscillations in noise-driven dynamical systems (36)(37)(38).…”

Section: Discussionmentioning

confidence: 99%

Efficient communication over complex dynamical networks: The role of matrix non-normality

et al. 2020

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In both natural and engineered systems, communication often occurs dynamically over networks ranging from highly structured grids to largely disordered graphs. To use, or comprehend the use of, networks as efficient communication media requires understanding of how they propagate and transform information in the face of noise. Here, we develop a framework that enables us to examine how network structure, noise, and interference between consecutive packets jointly determine transmission performance in complex networks governed by linear dynamics. Mathematically, normal networks, which can be decomposed into separate low-dimensional information channels, suffer greatly from readout noise. Most details of their wiring have no impact on transmission quality. Non-normal networks, however, can largely cancel the effect of noise by transiently amplifying select input dimensions while ignoring others, resulting in higher net information throughput. Our theory could inform the design of new communication networks, as well as the optimal use of existing ones.

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“…To this end, we explore analytically the presence of oscillations in the model through the stochastic linearization given by a system size expansion [39], from which we obtain the temporal evolution of the fluctuations around the equilibria (see Appendix C). Indeed, this approach has proven to be effective in other neuronal models [28,44,45,46], and leads to the power spectra…”

Section: Power Spectrummentioning

confidence: 99%

Criticality and network structure drive emergent oscillations in a stochastic whole-brain model

Barzon

Nicoletti

Mariani

et al. 2022

Preprint

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Understanding the relation between the structure of brain networks and its functions is a fundamental open question. Simple models of neural activity based on real anatomical networks have proven effective in describing features of whole-brain spontaneous activity when tuned at their critical point. In this work, we show that indeed structural networks are a crucial ingredient in the emergence of synchronized oscillations in a whole-brain stochastic model at criticality. We study such model in the mean-field limit, providing an analytical understanding of the associated first-order phase transition, arising from the presence of a bistable region in the parameters space. Then, we derive the power spectrum in the linear noise approximation and we show that, in the mean-field limit, no global oscillations emerge. Finally, by adding back an underlying brain network structure with homeostatic normalization, we numerically show how the bi-stability region is disrupted and concomitantly a synchronized phase with maximal dynamic range is observed. Hence, both the structure of brain networks and criticality are fundamental in driving the collective coordinated responses and maximal sensitivity of whole-brain stochastic models.

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Noise-driven neuromorphic tuned amplifier

Cited by 16 publications

References 35 publications

Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons

Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons

Efficient communication over complex dynamical networks: The role of matrix non-normality

Criticality and network structure drive emergent oscillations in a stochastic whole-brain model

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