Emergence of Multistability

Pisarchik, Alexander N.; Hramov, Alexander E.

doi:10.1007/978-3-030-98396-3_2

Cited by 4 publications

(2 citation statements)

References 180 publications

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“…Similarly, a better understanding of the longer time scales of brain dynamics that govern the recurrence of seizures would profit from considering mechanisms that can give rise to various long term, fluctuating behavior. We here mention switching phenomena related to the different types of intermittency ( Perez Velazquez et al, 1999 ; Rizzi et al, 2016 ; Pisarchik et al, 2018 ), switching in fast-slow systems ( Kuehn, 2011 ) and in heteroclinic networks ( Kirst and Timme, 2008 ; Aguiar et al, 2011 ; Bick and Field, 2017 ; Morrison and Young, 2022 ; Meyer-Ortmanns, 2023 ) multistability ( Lopes da Silva et al, 2003 ; Takeshita et al, 2007 ; Rothkegel and Lehnertz, 2009 ; Breakspear, 2017 ; Pisarchik and Hramov, 2022 ), and metastability ( Kelso, 2012 ; Tognoli and Kelso, 2014 ; Rossi et al, 2023 ). The validity of such models could be tested if continuous long-term recordings of brain dynamics—covering weeks to months [see, e.g., Weisdorf et al (2019) ; Duun-Henriksen et al (2020) ]—would be publically available.…”

Section: Current Limitations and Potential Prospectsmentioning

confidence: 99%

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Bröhl,

Rings,

Pukropski

et al. 2024

Front. Netw. Physiol.

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Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus—a discrete cortical area from which seizures originate—to a widespread epileptic network—spanning lobes and hemispheres—considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

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Section: Current Limitations and Potential Prospectsmentioning

confidence: 99%

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Bröhl,

Rings,

Pukropski

et al. 2024

Front. Netw. Physiol.

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“…Synchronization processes have played a crucial role in encoding and decoding neural dynamics, revealing the importance of self-organization processes in neuronal dynamics [ 6 , 7 , 8 , 9 ]. Furthermore, it has been demonstrated over time that the foundation of neuronal dynamics lies in the self-organization process of complex systems [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Living-Neuron-Based Autogenerator

Gerasimova

Beltyukova

Fedulina

et al. 2023

Sensors

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We present a novel closed-loop system designed to integrate biological and artificial neurons of the oscillatory type into a unified circuit. The system comprises an electronic circuit based on the FitzHugh-Nagumo model, which provides stimulation to living neurons in acute hippocampal mouse brain slices. The local field potentials generated by the living neurons trigger a transition in the FitzHugh–Nagumo circuit from an excitable state to an oscillatory mode, and in turn, the spikes produced by the electronic circuit synchronize with the living-neuron spikes. The key advantage of this hybrid electrobiological autogenerator lies in its capability to control biological neuron signals, which holds significant promise for diverse neuromorphic applications.

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