Long-term stability of avalanche scaling and integrative network organization in prefrontal and premotor cortex

Miller, Stephanie R.; Pajevic, Sinisa; Yu, Shan; Plenz, Dietmar

doi:10.1101/2020.11.17.386615

Cited by 4 publications

(6 citation statements)

References 72 publications

(182 reference statements)

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“…These original scaling operations for avalanches involved >10 h of continuous recordings in vitro, which is difficult to achieve under standard experimental conditions. Recently, Miller et al [71,77] extended this scaling analysis of LFP-based avalanches. They identified a scaling exponent of two for an avalanche waveform and a mean size vs. duration relationship in line with predictions for a critical branching process.…”

Section: Temporal and Spatial Scaling Links Size Distribution Slope −3/2 To A Critical Branching Parameter For Neuronal Avalanchesmentioning

confidence: 99%

“…Since network connectivity was found to support avalanche dynamics in dissociated cultures, it could be considered a control parameter as well [119]. On the other hand, measurements in organotypic cortex cultures and in nonhuman primates in vivo demonstrate that avalanches establish integrative network architectures that are robust to certain plastic changes [77,120,121]. Of note, in vivo studies have shown avalanches to be exquisitely sensitive to the sleep/wakefulness transition [122][123][124][125][126], suggesting sleep [127,128] and sleep-arousal transitions [129] as a behavioral state control parameter.…”

Section: Control Parameters Identified In the Regulation Of Neuronal Avalanchesmentioning

confidence: 99%

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Self-Organized Criticality in the Brain

et al. 2021

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Self-organized criticality (SOC) refers to the ability of complex systems to evolve toward a second-order phase transition at which interactions between system components lead to scale-invariant events that are beneficial for system performance. For the last two decades, considerable experimental evidence has accumulated that the mammalian cortex with its diversity in cell types, interconnectivity, and plasticity might exhibit SOC. Here, we review the experimental findings of isolated, layered cortex preparations to self-organize toward four dynamical motifs presently identified in the intact cortex in vivo: up-states, oscillations, neuronal avalanches, and coherence potentials. During up-states, the synchronization observed for nested theta/gamma oscillations embeds scale-invariant neuronal avalanches, which can be identified by robust power law scaling in avalanche sizes with a slope of −3/2 and a critical branching parameter of 1. This precise dynamical coordination, tracked in the negative transients of the local field potential (nLFP) and spiking activity of pyramidal neurons using two-photon imaging, emerges autonomously in superficial layers of organotypic cortex cultures and acute cortex slices, is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies. A subclass of avalanches, the coherence potentials, exhibits precise maintenance of the time course in propagated local synchrony. Avalanches emerge in superficial layers of the cortex under conditions of strong external drive. The balance of excitation and inhibition (E/I), as well as neuromodulators such as dopamine, establishes powerful control parameters for avalanche dynamics. This rich dynamical repertoire is not observed in dissociated cortex cultures, which lack the differentiation into cortical layers and exhibit a dynamical phenotype expected for a first-order phase transition. The precise interactions between up-states, nested oscillations, and avalanches in superficial layers of the cortex provide compelling evidence for SOC in the brain.

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Section: Temporal and Spatial Scaling Links Size Distribution Slope −3/2 To A Critical Branching Parameter For Neuronal Avalanchesmentioning

confidence: 99%

Section: Control Parameters Identified In the Regulation Of Neuronal Avalanchesmentioning

confidence: 99%

Self-Organized Criticality in the Brain

et al. 2021

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“…But most importantly, reoccurring sequences of spike patterns with a particular composition of participating units were indeed observed in experiments [64][65][66][67][68][69][70][71], indicating a robust formation of assemblies during signal processing.…”

Section: Analytical Avalanche Size Distributions In Small or Structur...mentioning

confidence: 82%

A rigorous stochastic theory for spike pattern formation in recurrent neural networks with arbitrary connection topologies

Schünemann¹,

Ernst²,

Kesseböhmer³

2022

Preprint

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Cortical networks exhibit synchronized activity which often occurs in spontaneous events in the form of spike avalanches. Since synchronization has been causally linked to central aspects of brain function such as selective signal processing and integration of stimulus information, participating in an avalanche is a form of a transient synchrony which temporarily creates neural assemblies and hence might especially be useful for implementing flexible information processing. For understanding how assembly formation supports neural computation, it is therefore essential to establish a comprehensive theory of how network structure and dynamics interact to generate specific avalanche patterns and sequences. Here we derive exact avalanche distributions for a finite network of recurrently coupled spiking neurons with arbitrary non-negative interaction weights, which is made possible by formally mapping the model dynamics to a linear, random dynamical system on the N -torus and by exploiting self-similarities inherent in the phase space. We introduce the notion of relative unique ergodicity and show that this property is guaranteed if the system is driven by a time-invariant Bernoulli process. This approach allows us not only to provide closed-form analytical expressions for avalanche size, but also to determine the detailed set(s) of units firing in an avalanche (i.e., the avalanche assembly). The underlying dependence between network structure and dynamics is made transparent by expressing the distribution of avalanche assemblies in terms of the induced graph Laplacian. We explore analytical consequences of this dependence and provide illustrating examples.In summary, our framework provides a major extension of previous analytical work which was restricted to regularly coupled or discrete state networks in the infinite network limit. For systems with a sufficiently homogeneous or translationally invariant coupling topology, we make an explicit link to critical states and the existence of scale-free distributions.

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“…It has been Brain Sci. 2021, 11, 1125 2 of 16 reported that primary motor cortex (M1) neuronal signals can be used to predict the direction and velocity of arm movements [5,6], based on which a brain-machine interface was developed to help those who suffer from paralysis. However, such systems still present a series of issues regarding the flexibility and robustness of control.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have investigated the innervation relationships between neuronal activity patterns from various brain structures and different behaviors [ 2 , 3 , 4 ]. It has been reported that primary motor cortex (M1) neuronal signals can be used to predict the direction and velocity of arm movements [ 5 , 6 ], based on which a brain–machine interface was developed to help those who suffer from paralysis. However, such systems still present a series of issues regarding the flexibility and robustness of control.…”

Section: Introductionmentioning

confidence: 99%

Neurons in Primary Motor Cortex Encode External Perturbations during an Orientation Reaching Task

Sun

Zhou

et al. 2021

Brain Sciences

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When confronting an abrupt external perturbation force during movement, subjects continuously adjust their behaviors to adapt to changes. Such adaptation is of great importance for realizing flexible motor control in varied environments, but the potential cortical neuronal mechanisms behind it have not yet been elucidated. Aiming to reveal potential neural control system compensation for external disturbances, we applied an external orientation perturbation while monkeys performed an orientation reaching task and simultaneously recorded the neural activity in the primary motor cortex (M1). We found that a subpopulation of neurons in the primary motor cortex specially created a time-locked activity in response to a “go” signal in the adaptation phase of the impending orientation perturbation and did not react to a “go” signal under the normal task condition without perturbation. Such neuronal activity was amplified as the alteration was processed and retained in the extinction phase; then, the activity gradually faded out. The increases in activity during the adaptation to the orientation perturbation may prepare the system for the impending response. Our work provides important evidence for understanding how the motor cortex responds to external perturbations and should advance research about the neurophysiological mechanisms underlying motor learning and adaptation.

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Long-term stability of avalanche scaling and integrative network organization in prefrontal and premotor cortex

Cited by 4 publications

References 72 publications

Self-Organized Criticality in the Brain

Self-Organized Criticality in the Brain

A rigorous stochastic theory for spike pattern formation in recurrent neural networks with arbitrary connection topologies

Neurons in Primary Motor Cortex Encode External Perturbations during an Orientation Reaching Task

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