Spike-Timing-Dependent Plasticity With Axonal Delay Tunes Networks of Izhikevich Neurons to the Edge of Synchronization Transition With Scale-Free Avalanches

Khoshkhou, Mahsa; Montakhab, Afshin

doi:10.3389/fnsys.2019.00073

Cited by 27 publications

(38 citation statements)

References 58 publications

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“…All the mechanisms discussed above lead to the selforganization to the vicinity of a non-equilibrium absorbingactive phase transition. Nevertheless, similar mechanisms have also been described in other contexts, such as self-organization to the edge of a synchronization phase transition in the context of models of neuronal dynamics [163][164][165]. This mechanism is similar in spirit to those above, operating differentially in the two alternative phases; in particular, the synaptic strengths (which play the role of "energy variable") tend to be reinforced when the system is in the asynchronous phase and weakened when it is exceedingly synchronous (which is achieved by a synaptic plasticity mechanism such as "spike-time dependent plasticity" [166]), thus, leading the system to the edge of a synchronization phase transition in a self-organized way.…”

Section: Summary and Discussionmentioning

confidence: 66%

Feedback Mechanisms for Self-Organization to the Edge of a Phase Transition

et al. 2020

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Scale-free outbursts of activity are commonly observed in physical, geological, and biological systems. The idea of self-organized criticality (SOC), introduced back in 1987 by Bak, Tang, and Wiesenfeld suggests that, under certain circumstances, natural systems can seemingly self-tune to a critical state with its concomitant power-laws and scaling. Theoretical progress allowed for a rationalization of how SOC works by relating its critical properties to those of a standard non-equilibrium second-order phase transition that separates an active state in which dynamical activity reverberates indefinitely, from an absorbing or quiescent state where activity eventually ceases. The basic mechanism underlying SOC is the alternation of a slow driving process and fast dynamics with dissipation, which generates a feedback loop that tunes the system to the critical point of an absorbing-active continuous phase transition. Here, we briefly review these ideas as well as a recent closely-related concept: self-organized bistability (SOB). In SOB, the very same type of feedback operates in a system characterized by a discontinuous phase transition, which has no critical point but instead presents bistability between active and quiescent states. SOB also leads to scale-invariant avalanches of activity but, in this case, with a different type of scaling and coexisting with anomalously large outbursts. Moreover, SOB explains experiments with real sandpiles more closely than SOC. We review similarities and differences between SOC and SOB by presenting and analyzing them under a common theoretical framework, covering recent results as well as possible future developments. We also discuss other related concepts for "imperfect" self-organization such as "self-organized quasi-criticality" and "self-organized collective oscillations," of relevance in e.g., neuroscience, with the aim of providing an overview of feedback mechanisms for self-organization to the edge of a phase transition.

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Section: Summary and Discussionmentioning

confidence: 66%

Feedback Mechanisms for Self-Organization to the Edge of a Phase Transition

et al. 2020

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“…The end result is robust against different initial topologies and changes to the underlying parameters, such as average connectivity. Even when initializing a network from a random topology, STDP is sufficient in some models to drive the network toward critical dynamics (Teixeira and Shanahan, 2014 ; Li et al, 2017 ; Khoshkhou and Montakhab, 2019 ). Li et al ( 2017 ) have also investigated the computational benefit of these STDP-trained networks, which showed improved input-to-output transformation performance at criticality (see also Bertschinger and Natschläger, 2004 ; Siri et al, 2007 , 2008 for the computational benefits of criticality and Hebbian plasticity in recurrent neural networks).…”

Section: Plasticity Is Necessary To Achieve and Maintain Criticalitymentioning

confidence: 99%

Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation

Heiney

Ramstad

Fiskum

et al. 2021

Front. Comput. Neurosci.

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It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed “neuronal avalanches.” The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.

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“…Khoshkhou and Montakhab [114] studied a random network with K 〈K i 〉 neighbors. The cells are Izhikevich neurons described by…”

Section: Edge Of Synchronizationmentioning

confidence: 99%

Mechanisms of Self-Organized Quasicriticality in Neuronal Network Models

2020

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The critical brain hypothesis states that there are information processing advantages for neuronal networks working close to the critical region of a phase transition. If this is true, we must ask how the networks achieve and maintain this critical state. Here, we review several proposed biological mechanisms that turn the critical region into an attractor of a dynamics in network parameters like synapses, neuronal gains, and firing thresholds. Since neuronal networks (biological and models) are not conservative but dissipative, we expect not exact criticality but self-organized quasicriticality, where the system hovers around the critical point.

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Spike-Timing-Dependent Plasticity With Axonal Delay Tunes Networks of Izhikevich Neurons to the Edge of Synchronization Transition With Scale-Free Avalanches

Cited by 27 publications

References 58 publications

Feedback Mechanisms for Self-Organization to the Edge of a Phase Transition

Feedback Mechanisms for Self-Organization to the Edge of a Phase Transition

Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation

Mechanisms of Self-Organized Quasicriticality in Neuronal Network Models

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