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Neural Circuits

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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.
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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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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...

Electrical Synapses

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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.
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Chemical Synapses

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...

Chemical Synapses

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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Related Experiment Video

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Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains.

Chris Trengove¹, Markus Diesmann^2,3,4, Cees van Leeuwen^5,6

¹Perceptual Dynamics Laboratory, University of Leuven, Leuven, Belgium. trengove.c@gmail.com.

Journal of Computational Neuroscience

|November 13, 2015

Summary

This study shows that coupled synfire chains in neural networks can generate diverse ongoing activity patterns. These patterns are explained by a dynamic effective connectivity structure, revealing insights into cortical computation.

Keywords:

Background synaptic noise Combinatorial representation Effective connectivity Metastability Recurrent network dynamics Synfire chains

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Area of Science:

Computational Neuroscience
Neural Network Modeling

Background:

Synfire chains are a proposed mechanism for neural representation.
Balanced recurrent cortical networks can embed large numbers of synfire chains.

Purpose of the Study:

To investigate a model of coupled synfire chains in a recurrent cortical network.
To explore the emergent activity patterns and their underlying connectivity structure.

Main Methods:

Developed a recurrent system model with multiple, variable-strength synfire chains.
Implemented both large-scale integrate-and-fire neuron networks and a reduced binary-state pool model.
Analyzed ongoing activity, including synfire waves, meta-stability, and steady states.

Main Results:

The model sustains endogenous activity (synfire waves) regulated by collateral noise feedback.
Observed diverse activity repertoires, including meta-stability and multiple steady states.
Identified an effective connectivity structure (ECS) with dynamic effective connectivity graphs (ECGs) that explain observed states.

Conclusions:

Coupled synfire chains in balanced networks support complex dynamics and diverse activity patterns.
Dynamic effective connectivity governs neural computation in these systems.
The findings have implications for understanding complex cortical circuitry and neural computation.