Related Experiment Video
Updated: Mar 2, 2026
10:50
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
Published on: June 21, 2022
2.2K
Modeling of inter-neuronal coupling medium and its impact on neuronal synchronization
Muhammad Iqbal1, Muhammad Rehan2, Keum-Shik Hong3
1Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
Plos One
|May 10, 2017
Summary
This study models neuronal coupling, revealing a natural proportional integral derivative (PID) controller responsible for neuron synchronization. A particle swarm optimization method compensates for coupling strength deficiencies in neural networks.
Area of Science:
- Computational Neuroscience
- Neural Networks
- Control Theory
Background:
- Neuronal synchronization is crucial for brain information processing.
- Understanding inter-neuronal coupling mechanisms is key to deciphering neural network behavior.
Purpose of the Study:
- To model the coupling medium between two neurons.
- To investigate the influence of model parameters on neuronal synchronization.
- To develop methods for compensating coupling strength deficiencies.
Main Methods:
- Derived a novel electrical model of the coupling medium using RLC circuit properties (resistance, inductance, capacitance).
- Identified the proportional integral derivative (PID) controller as an intrinsic control strategy within the coupling medium.
- Studied the effects of PID controller parameters on neuronal synchronization.
- Proposed a supervisory mechanism using particle swarm optimization for adaptive compensation.
Main Results:
- The RLC model demonstrates that intrinsic properties of the coupling medium naturally implement a PID control strategy.
- PID controller parameters significantly affect neuronal synchronization.
- The proposed particle swarm optimization algorithm effectively compensates for coupling strength deficiencies.
Conclusions:
- Brain information processing may utilize numerous PID controllers inherent to neuronal coupling.
- The derived model provides insights into neuronal information transmittance and neural network coherence.
- Adaptive control strategies can enhance the robustness of neuronal synchronization.
Related Concept Videos
01:28Neuronal Communication
3.9K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
3.9K
01:25Neural Circuits
3.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.0K
01:19The Role of Ion Channels in Neuronal Computation
4.0K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
4.0K
01:29Excitatory and Inhibitory Effects of Neurotransmitters
13.8K
When an action potential reaches the presynaptic axon terminal, it releases neurotransmitters from the neuron into the synaptic cleft at a chemical synapse. The released neurotransmitter can be excitatory or inhibitory. The critical criteria commonly used to determine whether a molecule is a neurotransmitter at a chemical synapse are the molecule's presence in the presynaptic neuron. Second, its release is in response to strong presynaptic depolarization. And lastly, the presence of...
13.8K
01:28Electrical Synapses
11.3K
Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
11.3K