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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 III’ (left, D = 100 pA) and IV (right, D = 150 pA).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4507401&req=5

f4: Same as in Fig. 3 but for III’ (left, D = 100 pA) and IV (right, D = 150 pA).

Mentions: (III’) As D is further increased for not too high excitability values, one may observe the situation which is illustrated by the left panels in Fig. 4. This is also a memory phase, but the involved neurons are now firing, due to the extra noise, in a random asynchronous manner, instead of synchronized which is the main feature of phase III.


Brain Performance versus Phase Transitions.

Torres JJ, Marro J - Sci Rep (2015)

Same as in Fig. 3 but for III’ (left, D = 100 pA) and IV (right, D = 150 pA).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Same as in Fig. 3 but for III’ (left, D = 100 pA) and IV (right, D = 150 pA).
Mentions: (III’) As D is further increased for not too high excitability values, one may observe the situation which is illustrated by the left panels in Fig. 4. This is also a memory phase, but the involved neurons are now firing, due to the extra noise, in a random asynchronous manner, instead of synchronized which is the main feature of phase III.

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