The Role of Criticality in Flexible Visual Information Processing

Tömen, Nergis; Ernst, Udo

doi:10.1007/978-3-030-20965-0_12

Cited by 6 publications

(9 citation statements)

References 74 publications

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“…Rajkai et al (2008), have reported increased activity in the visual cortex associated with eye movements. These findings have been interpreted as potentially reflecting higher levels of excitability, effectively enhancing and priming the visual system for optimal sensitivity; an observation consistent with the idea that cortical regions become quickly and adaptively tuned towards criticality to broaden their dynamic range of sensitivity, facilitate metastability (Werner, 2007) and optimize information processing (Beggs, 2008;Haldeman & Beggs, 2005;Kinouchi & Copelli, 2006;Palva & Palva, 2018;Shew et al, 2015;Tomen & Ernst, 2019). Review articles by Shriki and Yellin (2016), Wilting and Priesemann (2019) and Zimmerman (2020) offer excellent summaries of the role of criticality and its significance in the behavioral neurosciences.…”

Section: Criticalitymentioning

confidence: 66%

Computational annealing as mechanism in eye movement desensitization and reprocessing (EMDR)

Stwertka¹

2021

Preprint

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The computational annealing model offers a plausible neurophysiological mechanism to account for eye movement desensitization and reprocessing’s (EMDR) psychotherapeutic effectiveness. The model is informed by analogy to the physical annealing of metals and the related computational method of simulated annealing. It proposes that observed instances of chaotic activity and criticality within the nervous system serve to loosen constraints within memory networks, allowing them to reorganize in a direction of optimal internal coherence and harmony. The role of eye movements is to generate corollary discharge within relevant brain areas, inducing transient increases in criticality within working memory; effectively raising the computational temperature and facilitating informational and energetic flow towards optimal resolution of trauma related memories. Raising the computational temperature overcomes constraints maintaining fragmented and discordant cognitive schemas, enabling fuller integration with the broader sense of self. Computational annealing provides an outline of a conceptual bridge between observations made at the microscopic neurophysiological level, and those made at the macroscopic, behavioral level of description.

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Section: Criticalitymentioning

confidence: 66%

Computational annealing as mechanism in eye movement desensitization and reprocessing (EMDR)

Stwertka¹

2021

Preprint

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“…Thus one can interpret Eq. (12) for the equilibrium firing rates as summing the influences between units over all possible paths in the network. Moreover, the weighted sum of all paths from node k to node i gives the probability that unit i fires in an avalanche started by unit k (see Theorem D.1):…”

Section: B Equilibrium Rates Spike Covariance and Mean Avalanche Sizesmentioning

confidence: 99%

“…In consequence, power laws and criticality played only a secondary role in our study, while instead we focused on detailed assembly formation. Combined with structured inputs from 'meaningful' external stimuli [12] we expect our tools in future studies to provide new insights into how avalanche formation and -potentially -criticality serve information processing and brain function.…”

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

confidence: 99%

“…For instance, synchronization can indicate global dependencies among distributed local information in complex sensory scenes (e. g. [12]), while mutual synchronization between different brain areas can rapidly establish or suppress communication channel for selective information processing in dependence on task demands (e. g. [4,13,14]).…”

Section: Introductionmentioning

confidence: 99%

“…For this purpose we employ the framework originally introduced by Eurich, Herrmann, and Ernst [28] (in the following termed EHE model), which has successfully been used to formally study neural avalanches in globally coupled homogeneous networks, i.e. with constant or block constant coupling matrices [12,40,41], and whose basic mathematical properties are well understood [42,43]. We extend the EHE model to arbitrary positive coupling matrices and derive closed-form expressions for the probabilities of arbitrary cell assemblies becoming transiently active in form of an avalanche [20].…”

Section: Introductionmentioning

confidence: 99%

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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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Cognitive science as complexity science

Favela

2020

WIRES Cognitive Science

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It is uncontroversial to claim that cognitive science studies many complex phenomena. What is less acknowledged are the contradictions among many traditional commitments of its investigative approaches and the nature of cognitive systems. Consider, for example, methodological tensions that arise due to the fact that like most natural systems, cognitive systems are nonlinear; and yet, traditionally cognitive science has relied on linear statistical data analyses. Cognitive science as complexity science is offered as an interdisciplinary framework for the investigation of cognition that can dissolve such contradictions and tensions. Here, cognition is treated as exhibiting the following four key features: emergence, nonlinearity, self‐organization, and universality. This framework integrates concepts, methods, and theories from such disciplines as systems theory, nonlinear dynamical systems theory, and synergetics. By adopting this approach, the cognitive sciences benefit from a common set of practices to investigate, explain, and understand cognition in its varied and complex forms. This article is categorized under: Computer Science > Neural Networks Psychology > Theory and Methods Philosophy > Foundations of Cognitive Science Neuroscience > Cognition

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The Role of Criticality in Flexible Visual Information Processing

Cited by 6 publications

References 74 publications

Computational annealing as mechanism in eye movement desensitization and reprocessing (EMDR)

Computational annealing as mechanism in eye movement desensitization and reprocessing (EMDR)

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

Cognitive science as complexity science

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