Emergence of synchronised and amplified oscillations in neuromorphic networks with long-range interactions

Apicella, Ilenia; Busiello, Daniel Maria; Scarpetta, Silvia; Suweis, Samir

doi:10.1016/j.neucom.2020.04.162

“…In fact, the emerging collective dynamics in the brain is shaped both by the underlying connectome and by neural population activities [25][26][27]. Despite a direct relation between structural and functional networks, to what extent structure does determine the neural dynamics and its critical signatures has still to be clarified [28,29].…”

Section: Introductionmentioning

confidence: 99%

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

Barzon

¹

,

Nicoletti

²

,

Mariani

³

et al. 2022

View full text Add to dashboard Cite

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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“…To this end, we explore analytically the presence of oscillations in the model through the stochastic linearization obtained via a system size expansion [38], from which we derive the temporal evolution of the fluctuations around the equilibria (see Appendix C). Indeed, this approach has proven to be effective in other neuronal models [27,41,42,43]. We find that the power spectrum in the super-critical regime is given by…”

Section: Power Spectrummentioning

confidence: 89%

“…Models of large-scale neuronal dynamics are fundamental in explaining and predicting neuronal activity at the macroscopic scale [30]. Such models, describing the collective behavior of populations of neurons and biophysically-inspired, often replicate observed patterns of brain dynamics, e.g., scale-free avalanches [10, 15, 49], long-range correlations [18, 19], global oscillations [25, 26, 27]. However, the collective dynamics is crucially determined both by the dynamical rules that model the inter-neuronal activations and by the geometry of their connections [34].…”

Section: Discussionmentioning

confidence: 99%

“…In the empirical connectome, the dynamics is typically richer and oscillates close to the nullcline predicted by the mean-field (9). On the other hand, in the Erdős–Rényi case, the trajectories display large transient dynamics away from the ( y − , x − ) equilibrium, reminiscent of noise-induced oscillations [27, 41, 42, 43] or non-normal systems [51, 52]. This suggests that, although both models display emerging oscillations, the underlying dynamical features might be different.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, the emerging collective dynamics in the brain is shaped both by the underlying connectome and by the neural population activities [25, 26, 27]. Despite a direct relation between structural and functional networks, to what extent structure does determine the neural dynamics and its critical signatures has still to be clarified [28, 29].…”

Section: Introductionmentioning

confidence: 99%

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

Barzon

¹

,

Nicoletti

²

,

Mariani

³

et al. 2022

Preprint

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View full text Add to dashboard Cite

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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Modelling co-evolution of resource feedback and social network dynamics in human-environmental systems

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et al. 2024

New J. Phys.

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Games with environmental feedback have become a crucial area of study across various scientific domains, modelling the dynamic interplay between human decisions and environmental changes, and highlighting the consequences of our choices on natural resources and biodiversity. In this work, we propose a co-evolutionary model for human-environment systems that incorporates the effects of knowledge feedback and social interaction on the sustainability of common pool resources. The model represents consumers as agents who adjust their resource extraction based on the resource's state. These agents are connected through social networks, where links symbolize either affinity or aversion among them. The interplay between social dynamics and resource dynamics is explored, with the system's evolution analyzed across various network topologies and initial conditions.We find that knowledge feedback can independently sustain common pool resources. However, the impact of social interactions on sustainability is dual-faceted: it can either support or impede sustainability, influenced by the network's connectivity and heterogeneity. A notable finding is the identification of a critical network mean degree, beyond which a depletion/repletion transition parallels an absorbing/active state transition in social dynamics, i.e., individual agents and their connections are/are not prone to being frozen in their social states.Furthermore, the study examines the evolution of the social network, revealing the emergence of two polarized groups where agents within each community have the same affinity. Finally, we observe an inverse relationship between system complexity and sustainability. Comparative analyses using Monte-Carlo simulations and rate equations are employed, along with analytical arguments, to reinforce the study's findings. The model successfully captures key aspects of the human-environment system, offering valuable insights to understand how both the spread of information and social dynamics may impact the sustainability of common pool resources.

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Emergence of synchronised and amplified oscillations in neuromorphic networks with long-range interactions

Cited by 3 publications

References 41 publications

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

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

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

Modelling co-evolution of resource feedback and social network dynamics in human-environmental systems

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