.png){kind=icon}
.png){kind=icon}
Return
Brain Performance versus Phase Transitions
.png){kind=icon}
OA
AI
DOI：10.1038/srep12216.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
NEURAL-NETWORKS
NEURONAL AVALANCHES
CRITICALITY
EMERGENCE
SYNAPSES
DYNAMICS
BEHAVIOR
RANGE
MODEL
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
27.4W
Citations:
83.5W
Organization
No organization information available