Work

Changes in firing variability depend on network resilience

Year: 2011

Type: conference-paper

Abstract: In general, increase in the fluctuations in dynamical systems serves as symptoms of state transitions. However, whether increases in the variability of firing in neural systems are caused by losses in stability before transitions remains poorly understood. We demonstrated that firing variability monotonically increases as a function of the degree of instability in attractor neural networks by measuring the resilience of such systems.

Source: Institutional Repositories DataBase (IRDB)

Authors: Kazuhiro Sakamoto, Yuichi Katori, Kazuyuki Aihara, Hajime Mushiake

Institution: Tohoku University

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W2597109702

FWCI:

Cites: 5

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Related to: 10