From brain states to mental phenomena via phase space transitions and renormalization group transformation: proposal of a theory

Werner, Gerhard

doi:10.1007/s11571-011-9187-4

Cited by 16 publications

(16 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They derived correct power laws by iterating the network. Applications of RG to neural computation are known: image processing (Gidas, 1989;Mehta and Schwab, 2014;Saremi and Sejnowski, 2016), brain and behavior (Freeman and Cao, 2008), emergent consciousness (Werner, 2012;Fingelkurts et al, 2013;Laughlin, 2014), and hierarchical modular networks (Lee et al, 1986;Willcox, 1991) important for criticality (Moretti and Muñoz, 2013). Furthermore, our model's RG-like features are crucial to reproducing experimental results.…”

Section: Discussionmentioning

confidence: 88%

Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality

Johnson

Xià

et al. 2019

J. Neurosci.

View full text Add to dashboard Cite

What information single neurons receive about general neural circuit activity is a fundamental question for neuroscience. Somatic membrane potential (V m ) fluctuations are driven by the convergence of synaptic inputs from a diverse cross-section of upstream neurons. Furthermore, neural activity is often scale-free, implying that some measurements should be the same, whether taken at large or small scales. Together, convergence and scale-freeness support the hypothesis that single V m recordings carry useful information about high-dimensional cortical activity. Conveniently, the theory of "critical branching networks" (one purported explanation for scale-freeness) provides testable predictions about scale-free measurements that are readily applied to V m fluctuations. To investigate, we obtained whole-cell current-clamp recordings of pyramidal neurons in visual cortex of turtles with unknown genders. We isolated fluctuations in V m below the firing threshold and analyzed them by adapting the definition of "neuronal avalanches" (i.e., spurts of population spiking). The V m fluctuations which we analyzed were scale-free and consistent with critical branching. These findings recapitulated results from large-scale cortical population data obtained separately in complementary experiments using microelectrode arrays described previously (Shew et al., 2015). Simultaneously recorded single-unit local field potential did not provide a good match, demonstrating the specific utility of V m . Modeling shows that estimation of dynamical network properties from neuronal inputs is most accurate when networks are structured as critical branching networks. In conclusion, these findings extend evidence of critical phenomena while also establishing subthreshold pyramidal neuron V m fluctuations as an informative gauge of high-dimensional cortical population activity. The relationship between membrane potential (V m ) dynamics of single neurons and population dynamics is indispensable tounderstanding cortical circuits. Just as important to the biophysics of computation are emergent properties such as scale-freeness, where critical branching networks offer insight. This report makes progress on both fronts by comparing statistics from singleneuron whole-cell recordings with population statistics obtained with microelectrode arrays. Not only are fluctuations of somatic V m scale-free, they match fluctuations of population activity. Thus, our results demonstrate appropriation of the brain's own subsampling method (convergence of synaptic inputs) while extending the range of fundamental evidence for critical phenomena in neural systems from the previously observed mesoscale (fMRI, LFP, population spiking) to the microscale, namely, V m fluctuations.

show abstract

Section: Discussionmentioning

confidence: 88%

Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality

Johnson

Xià

et al. 2019

J. Neurosci.

View full text Add to dashboard Cite

show abstract

“…They derived correct power-laws by iterating the network. Applications of RG to neural computation are widespread: image processing (Gidas, 1989;Mehta and Schwab, 2014;Saremi and Sejnowski, 2016), brain and behavior (Freeman and Cao, 2008), emergent consciousness (Werner, 2012;Fingelkurts et al, 2013;Laughlin, 2014), and hierarchical modular networks (Lee et al, 1986;Willcox, 1991) important for criticality (Moretti and Munoz, 2013). Furthermore, our model's RG-like features are crucial to reproducing experimental results.…”

Section: Neuronal Avalanches and Neural Input Fluctuations Similaritymentioning

confidence: 86%

Single-cell membrane potential fluctuations evince network scale-freeness and quasicriticality

Johnson

Xià

et al. 2018

Preprint

View full text Add to dashboard Cite

What information single neurons receive about general neural circuit activity is a fundamental question for neuroscience. Somatic membrane potential fluctuations are driven by the convergence of synaptic inputs from a diverse cross section of upstream neurons. Furthermore, neural activity is often scale-free implying that some measurements should be the same, whether taken at large or small scales. Together, convergence and scalefreeness support the hypothesis that single membrane potential recordings carry useful information about highdimensional cortical activity. Conveniently, the theory of "critical branching networks" (a purported explanation for scale-freeness) provides testable predictions about scale-free measurements which are readily applied to membrane potential fluctuations. To investigate, we obtained whole-cell current clamp recordings of pyramidal neurons in visual cortex of turtles with unknown genders. We isolated fluctuations in membrane potential below the firing threshold and analyzed them by adapting the definition of "neuronal avalanches" (spurts of population spiking). The membrane potential fluctuations we analyzed were scale-free and consistent with critical branching. These findings recapitulated results from large-scale cortical population data obtained separately in complementary experiments using microelectrode arrays (previously published (Shew et al., 2015)). Simultaneously recorded single-unit local field potential did not provide a good match; demonstrating the specific utility of membrane potential. Modeling shows that estimation of dynamical network properties from neuronal inputs is most accurate when networks are structured as critical branching networks. In conclusion, these findings extend evidence for critical branching while also establishing subthreshold pyramidal neuron membrane potential fluctuations as an informative gauge of high-dimensional cortical population activity. Significance StatementThe relationship between membrane potential dynamics of single neurons and population dynamics is indispensable to understanding cortical circuits. Just as important to the biophysics of computation are emergent properties such as scale-freeness, where critical branching networks offer insight. This report makes progress on both fronts by comparing statistics from single-neuron whole-cell recordings to population statistics obtained with microelectrode arrays. Not only are fluctuations of somatic membrane potential scale-free, they match fluctuations of population activity. Thus, our results demonstrate appropriation of the brain's own subsampling method (convergence of synaptic inputs), while extending the range of fundamental evidence for critical branching in neural systems from the previously observed mesoscale (fMRI, LFP, population spiking) to the microscale, namely, membrane potential fluctuations.

show abstract

“…Examples of such ubiquitous structures has been the objects of numerous studies in the physical and biological sciences (Glansdorff and Prigogin 1971;Haken 1983;Nicolis 1986) and in cybernetics (Ashby 1947;von Foerster 1960von Foerster , 1999von Foerster and Pask 1960;Wiener 1961;von Glasserfeld 1996;Labini et al 2009) as well as, to a lesser degree, in social sciences (Schelling 1978;Krugman 1996;Brock and Hommes 1997;Galam 2012). More recently, great attention is being paid to the processes of self-organization in various networks, including neuron networks in the brain (Mainzer 2007;Chialvo 2010;Werner 2012;Fingelkurts et al 2012) and of self-organization of various swarms and flocks (Vanni et al 2011;Turalska et al 2011), where self-organization is understood as the unexpected appearance of collective or coherent behavior that is termed swarm intelligence. Complex behaviors of simple physical systems, imitating a kind of trivial intelligence, can be due to entropic forces (Wissner-Gross and Freer 2013).…”

Section: Introductionmentioning

confidence: 99%

Self-Organization in Complex Systems as Decision Making

Yukalov

Sornette

2014

Advs. Complex Syst.

View full text Add to dashboard Cite

The idea is advanced that self-organization in complex systems can be treated as decision making (as it is performed by humans) and, vice versa, decision making is nothing but a kind of self-organization in the decision maker nervous systems. A mathematical formulation is suggested based on the definition of probabilities of system states, whose particular cases characterize the probabilities of structures, patterns, scenarios, or prospects. In this general framework, it is shown that the mathematical structures of self-organization and of decision making are identical. This makes it clear how self-organization can be seen as an endogenous decision making process and, reciprocally, decision making occurs via an endogenous self-organization. The approach is illustrated by phase transitions in large statistical systems, crossovers in small statistical systems, evolutions and revolutions in social and biological systems, structural self-organization in dynamical systems, and by the probabilistic formulation of classical and behavioral decision theories. In all these cases, self-organization is described as the process of evaluating the probabilities of macroscopic states or prospects in the search for a state with the largest probability. The general way of deriving the probability measure for classical systems is the principle of minimal information, that is, the conditional entropy maximization under given constraints. Behavioral biases of decision makers can be characterized in the same way as analogous to quantum fluctuations in natural systems.

show abstract

From brain states to mental phenomena via phase space transitions and renormalization group transformation: proposal of a theory

Cited by 16 publications

References 22 publications

Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality

Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality

Single-cell membrane potential fluctuations evince network scale-freeness and quasicriticality

Self-Organization in Complex Systems as Decision Making

Contact Info

Product

Resources

About