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Brain Performance versus Phase Transitions.

Torres JJ, Marro J - Sci Rep (2015)

Bottom Line: Analyzing to what extent a weak signal endures in noisy environments, we identify the underlying mechanisms, and it results a description of how the excitability associated to (non-equilibrium) phase changes and criticality optimizes the processing of the signal.Our setting is a network of integrate-and-fire nodes in which connections are heterogeneous with rapid time-varying intensities mimicking fatigue and potentiation.Emergence then becomes quite robust against wiring topology modification--in fact, we considered from a fully connected network to the Homo sapiens connectome--showing the essential role of synaptic flickering on computations.

View Article: PubMed Central - PubMed

Affiliation: Institute Carlos I for Theoretical and Computational Physics, Granada, E-18071, Spain.

ABSTRACT
We here illustrate how a well-founded study of the brain may originate in assuming analogies with phase-transition phenomena. Analyzing to what extent a weak signal endures in noisy environments, we identify the underlying mechanisms, and it results a description of how the excitability associated to (non-equilibrium) phase changes and criticality optimizes the processing of the signal. Our setting is a network of integrate-and-fire nodes in which connections are heterogeneous with rapid time-varying intensities mimicking fatigue and potentiation. Emergence then becomes quite robust against wiring topology modification--in fact, we considered from a fully connected network to the Homo sapiens connectome--showing the essential role of synaptic flickering on computations. We also suggest how to experimentally disclose significant changes during actual brain operation.

No MeSH data available.


Related in: MedlinePlus

Same as in Fig. 3 but for IV’ (left, D = 300 pA) and V (right, D = 600 pA).
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f5: Same as in Fig. 3 but for IV’ (left, D = 300 pA) and V (right, D = 600 pA).

Mentions: (IV′) There is a value of D at which the metastable states become almost totally asynchronous since their start. The left panels in Fig. 5 illustrate this new condition. Note that, instead of the essential irregularity in case IV (which is obvious in the behavior of both s(t) and mv(t) in the right panels of Fig. 4), some periodicity now emerges spontaneously (there is no signal in the case illustrated here) which produces a maximum of correlation, as we shall see later on. The changing color of the overlaps in this case suggest periodic approaches to the neighborhoods of the stored patterns.


Brain Performance versus Phase Transitions.

Torres JJ, Marro J - Sci Rep (2015)

Same as in Fig. 3 but for IV’ (left, D = 300 pA) and V (right, D = 600 pA).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507401&req=5

f5: Same as in Fig. 3 but for IV’ (left, D = 300 pA) and V (right, D = 600 pA).
Mentions: (IV′) There is a value of D at which the metastable states become almost totally asynchronous since their start. The left panels in Fig. 5 illustrate this new condition. Note that, instead of the essential irregularity in case IV (which is obvious in the behavior of both s(t) and mv(t) in the right panels of Fig. 4), some periodicity now emerges spontaneously (there is no signal in the case illustrated here) which produces a maximum of correlation, as we shall see later on. The changing color of the overlaps in this case suggest periodic approaches to the neighborhoods of the stored patterns.

Bottom Line: Analyzing to what extent a weak signal endures in noisy environments, we identify the underlying mechanisms, and it results a description of how the excitability associated to (non-equilibrium) phase changes and criticality optimizes the processing of the signal.Our setting is a network of integrate-and-fire nodes in which connections are heterogeneous with rapid time-varying intensities mimicking fatigue and potentiation.Emergence then becomes quite robust against wiring topology modification--in fact, we considered from a fully connected network to the Homo sapiens connectome--showing the essential role of synaptic flickering on computations.

View Article: PubMed Central - PubMed

Affiliation: Institute Carlos I for Theoretical and Computational Physics, Granada, E-18071, Spain.

ABSTRACT
We here illustrate how a well-founded study of the brain may originate in assuming analogies with phase-transition phenomena. Analyzing to what extent a weak signal endures in noisy environments, we identify the underlying mechanisms, and it results a description of how the excitability associated to (non-equilibrium) phase changes and criticality optimizes the processing of the signal. Our setting is a network of integrate-and-fire nodes in which connections are heterogeneous with rapid time-varying intensities mimicking fatigue and potentiation. Emergence then becomes quite robust against wiring topology modification--in fact, we considered from a fully connected network to the Homo sapiens connectome--showing the essential role of synaptic flickering on computations. We also suggest how to experimentally disclose significant changes during actual brain operation.

No MeSH data available.


Related in: MedlinePlus