Criticality between Cortical States

Fontenele, Antonio J.; Vasconcelos, Nivaldo A. P. de; Feliciano, Thaís; Aguiar, Leandro A. A.; Soares‐Cunha, Carina; Coimbra, Bárbara; Porta, Leonardo Dalla; Ribeiro, Sidarta; Rodrigues, Ana João; Sousa, Nuno; Carelli, Pedro V.; Copelli, Mauro

doi:10.1103/physrevlett.122.208101

Cited by 208 publications

(330 citation statements)

References 37 publications

Supporting

Mentioning

240

Contrasting

Unclassified

Order By: Relevance

“…When this method was first introduced, Atasoy (2016) compellingly demonstrated how reaction-diffusion simulations of spreading activation could generate resting state networks as stable modes-or standing wavesso recapitulating well-known patterns of neuronal organization with minimal assumptions. Intriguingly, hallucinogenic compounds expanded the repertoire of these harmonic modes (Atasoy et al, 2017), increasing spectral diversity and shifting the distribution of modes towards power-law distributions, a putative-albeit controversial (Touboul and Destexhe, 2017)-hallmark of criticality (Fontenele et al, 2019). This finding is consistent with other studies of psychedelic compounds (Tagliazucchi et al, 2014;Schartner et al, 2017;Viol et al, 2017), supporting the hypothesis that brains may enhance dynamical reconfigurability by being 'tuned' towards nearcritical regimes (Pletzer et al, 2010;Haimovici et al, 2013;Carhart-Harris, 2018).…”

Section: Self-organizing Harmonic Modes (Sohms)supporting

confidence: 83%

An Integrated World Modeling Theory (IWMT) of consciousness: Combining Integrated Information and Global Neuronal Workspace Theories with the Free Energy Principle and Active Inference Framework; towards solving the Hard problem and characterizing agentic causation

Safron¹

2019

Preprint

View full text Add to dashboard Cite

The Free Energy Principle and Active Inference Framework (FEP-AI) begins with the understanding that persisting systems must regulate environmental exchanges and prevent entropic accumulation. In FEP-AI, minds and brains are predictive controllers for autonomous systems, where action-driven perception is realized as probabilistic inference. Integrated Information Theory (IIT) begins with considering the preconditions for a system to intrinsically exist, as well as axioms regarding the nature of consciousness. IIT has produced controversy because of its surprising entailments: quasi-panpsychism; subjectivity without referents or dynamics; and the possibility of fully-intelligent-yet-unconscious brain simulations. Here, I describe how these controversies might be resolved by integrating IIT with FEP-AI, where integrated information only entails consciousness for systems with perspectival reference frames capable of generating models with spatial, temporal, and causal coherence for self and world. Without that connection with external reality, systems could have arbitrarily high amounts of integrated information, but nonetheless would not entail subjective experience. I further describe how an integration of these frameworks may contribute to their evolution as unified systems theories and models of emergent causation. Then, inspired by both Global Neuronal Workspace Theory (GNWT) and the Harmonic Brain Modes framework, I describe how streams of consciousness may emerge as an evolving generation of sensorimotor predictions, with the precise composition of experiences depending on the integration abilities of synchronous complexes as self-organizing harmonic modes (SOHMs). These integrating dynamics may be particularly likely to occur via richly connected subnetworks affording body-centric sources of phenomenal binding and executive control. Along these connectivity backbones, SOHMs are proposed to implement loopy message passing (cf. turbo-codes) over predictive (autoencoding) networks, so generating maximum a posteriori estimates as coherent vectors governing neural evolution, with alpha frequencies generating basic awareness, and cross-frequency phase-coupling within theta frequencies for access consciousness and volitional control. These dynamic cores of integrated information also function as global workspaces, centered on posterior cortices, but capable of being entrained with frontal cortices and interoceptive hierarchies, so affording agentic causation. Integrated World Modeling Theory (IWMT) represents a synthetic approach to understanding minds that reveals compatibility between leading theories of consciousness, so enabling inferential synergy.

show abstract

Section: Self-organizing Harmonic Modes (Sohms)supporting

confidence: 83%

An Integrated World Modeling Theory (IWMT) of consciousness: Combining Integrated Information and Global Neuronal Workspace Theories with the Free Energy Principle and Active Inference Framework; towards solving the Hard problem and characterizing agentic causation

Safron¹

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The importance of criticality for neuromorphic computing was discussed in previous work on silver nanowire networks (13), but criticality in self-organized nanodevices has never been investigated systematically. Here, we investigate criticality in percolating nanoparticle networks using analysis techniques similar to those previously used on recordings of neuronal signals (24)(25)(26)(27). Figure 1 and fig.…”

Section: Behavior Of Critical Networkmentioning

confidence: 99%

“…important feature of both in vivo and in vitro recordings of neuronal signals (24)(25)(26)(27), and substantial evidence has accumulated that the brain itself operates at a self-organized critical point (20), which optimizes information processing, memory, and information transfer (15)(16)(17)(18)(19). Therefore, in designing brain-inspired computational systems, it is natural to look for systems that might exhibit similar critical behavior and, especially, systems that would allow electronic signal processing.…”

Section: Introductionmentioning

confidence: 99%

Avalanches and criticality in self-organized nanoscale networks

et al. 2019

View full text Add to dashboard Cite

Current efforts to achieve neuromorphic computation are focused on highly organized architectures, such as integrated circuits and regular arrays of memristors, which lack the complex interconnectivity of the brain and so are unable to exhibit brain-like dynamics. New architectures are required, both to emulate the complexity of the brain and to achieve critical dynamics and consequent maximal computational performance. We show here that electrical signals from self-organized networks of nanoparticles exhibit brain-like spatiotemporal correlations and criticality when fabricated at a percolating phase transition. Specifically, the sizes and durations of avalanches of switching events are power law distributed, and the power law exponents satisfy rigorous criteria for criticality. These signals are therefore qualitatively and quantitatively similar to those measured in the cortex. Our self-organized networks provide a low-cost platform for computational approaches that rely on spatiotemporal correlations, such as reservoir computing, and are an important step toward creating neuromorphic device architectures.

show abstract

“…However, recent theoretical [15,16] as well as experimental [17] studies show that the criticality may be associated with a synchronization transition. This possibility provides a stronger motivation to study various types of synchronization transition that may occur in network models of biological neurons.…”

Section: Introductionmentioning

confidence: 99%

“…Brain oscillations are categorized in various frequency bands. For example, beta-band (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) are typically associated with cognitive task performance.…”

Section: Introductionmentioning

confidence: 99%

Explosive, continuous and frustrated synchronization transition in spiking Hodgkin–Huxley neural networks: The role of topology and synaptic interaction

Khoshkhou

Montakhab

2020

Physica D: Nonlinear Phenomena

View full text Add to dashboard Cite

Synchronization is an important collective phenomenon in interacting oscillatory agents. Many functional features of the brain are related to synchronization of neurons. The type of synchronization transition that may occur (explosive vs. continuous) has been the focus of intense attention in recent years, mostly in the context of phase oscillator models for which collective behavior is independent of the mean-value of natural frequency. However, synchronization properties of biologically-motivated neural models depend on the firing frequencies. In this study we report a systematic study of gamma-band synchronization in spiking Hodgkin-Huxley neurons which interact via electrical or chemical synapses.We use various network models in order to define the connectivity matrix. We find that the underlying mechanisms and types of synchronization transitions in gamma-band differs from beta-band. In gamma-band, network regularity suppresses transition while randomness promotes a continuous transition. Heterogeneity in the underlying topology does not lead to any change in the order of transition, however, correlation between number of synapses and frequency of a neuron will lead to explosive synchronization in heterogenous networks with electrical synapses. Furthermore, small-world networks modeling a fine balance between clustering and randomness (as in the cortex), lead to explosive synchronization with electrical synapses, but a smooth transition in the case of chemical synapses. We also find that hierarchical modular networks, such as the connectome, lead to frustrated transitions. We explain our results based on various properties of the network, paying particular attention to the competition between clustering and long-range synapses.

show abstract

Criticality between Cortical States

Cited by 208 publications

References 37 publications

An Integrated World Modeling Theory (IWMT) of consciousness: Combining Integrated Information and Global Neuronal Workspace Theories with the Free Energy Principle and Active Inference Framework; towards solving the Hard problem and characterizing agentic causation

An Integrated World Modeling Theory (IWMT) of consciousness: Combining Integrated Information and Global Neuronal Workspace Theories with the Free Energy Principle and Active Inference Framework; towards solving the Hard problem and characterizing agentic causation

Avalanches and criticality in self-organized nanoscale networks

Explosive, continuous and frustrated synchronization transition in spiking Hodgkin–Huxley neural networks: The role of topology and synaptic interaction

Contact Info

Product

Resources

About