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Synaptic Signaling

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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.

Synaptic Signaling

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Related Experiment Video

Updated: Apr 3, 2026

Fast Micro-iontophoresis of Glutamate and GABA: A Useful Tool to Investigate Synaptic Integration

Fast Micro-iontophoresis of Glutamate and GABA: A Useful Tool to Investigate Synaptic Integration

Published on: August 1, 2013

Scale-free topologies and activatory-inhibitory interactions.

J Gómez-Gardeñes¹, Y Moreno, L M Floría

¹Departamento de Física de la Materia Condensada, Instituto de Biocomputación y Física de Sistemas Complejos BIFI, C.S.I.C.-Universidad de Zaragoza, 50009 Zaragoza, Spain.

Chaos (Woodbury, N.Y.)

|April 8, 2006

Summary

This study explores a simple activatory-inhibitory model on scale-free networks, revealing complex temporal fluctuations. Findings illuminate the interplay between network structure and dynamics, with potential applications.

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Published on: January 10, 2015

Area of Science:

Complex Systems Science
Network Science
Dynamical Systems Theory

Background:

Understanding emergent behavior in complex networks is crucial.
Activatory-inhibitory interactions are fundamental in many natural and artificial systems.
Scale-free networks exhibit unique structural properties influencing dynamics.

Purpose of the Study:

To investigate a simple model of activatory-inhibitory interactions in scale-free networks.
To characterize the dynamical features, including fragmentation and multistability.
To analyze temporal fluctuations in network activity and their underlying complexity.

Main Methods:

Development of a simple dynamical model with saturated response rules.
Thorough study of model dynamics on a scale-free network.
Characterization of temporal fluctuations (periodic and chaotic) in quasi-stasis states.
Numerical analysis of system complexity and correlation structure-function.

Main Results:

Identified fragmentation and multistability as key dynamical features.
Characterized periodic and chaotic temporal fluctuations in asymptotic network states.
Demonstrated a double source (structural and dynamical) for entangled complexity in temporal fluctuations.
Provided insights into the structure-function correlation problem.

Conclusions:

The model exhibits rich dynamical behavior arising from simple rules on complex networks.
Temporal fluctuations in network activity are complex, driven by both network structure and dynamics.
Results offer a framework for understanding emergent complexity and have potential applications.