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Neural Circuits

Neural Circuits

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...

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Related Experiment Video

Updated: Sep 2, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

A Spiking Neural Network Builder for Systematic Data-to-Model Workflow.

Carlos Enrique Gutierrez¹, Henrik Skibbe², Hugo Musset¹

¹Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan.

Frontiers in Neuroinformatics

|August 1, 2022

Summary

This summary is machine-generated.

This study introduces a novel framework for building spiking neural network (SNN) models using a database system. It enhances collaborative development and ensures data traceability for computational neuroscience research.

Keywords:

collective intelligence computational brain modeling data-to-model workflow neural simulation spiking neural networks web application

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Area of Science:

Computational Neuroscience
Neuroscience
Data Science

Background:

Efficiently converting diverse biological data into computational neural network model parameters is essential.
Web-based tools can improve transparency, reproducibility, and collaboration in computational modeling.
Systematic parameter estimation requires referencing experimental observations.

Purpose of the Study:

To present a framework for collaborative, data-driven development of spiking neural network (SNN) models.
To organize diverse neuroscientific data using an Entity-Relationship (ER) data description approach.
To facilitate the integration of anatomical and physiological datasets for systematic SNN modeling.

Main Methods:

Utilized an Entity-Relationship (ER) data description within a database management system (DBMS).
Organized data attributes (species, brain regions, neuron types, etc.) into tables and relations.
Provided GUI interfaces for data registration, visualization, and NEST simulation code generation.

Main Results:

Successfully tested the data-to-model framework in cortical and striatal network models.
Demonstrated the ability to combine data from literature with existing neuron and synapse models.
Generated NEST simulation codes for various network sizes and facilitated data integrity checks.

Conclusions:

The framework supports collaborative SNN model building with robust data representation and traceability.
It enables data comparisons across species and the modeling of any brain region.
The system is being deployed for integrating large-scale datasets, such as from the brain/MINDS project.