Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Reanalysis of EphA3 Knock-In Double Maps in Mouse Suggests That Stochasticity in Topographic Map Formation Acts at the Retina Rather than between Competing Mechanisms at the Colliculus.

eNeuro·2023

Same author

Toward standard practices for sharing computer code and programs in neuroscience.

Nature neuroscience·2017

Same author

Modelling Feedback Excitation, Pacemaker Properties and Sensory Switching of Electrically Coupled Brainstem Neurons Controlling Rhythmic Activity.

PLoS computational biology·2016

Same author

Modelling the Effects of Electrical Coupling between Unmyelinated Axons of Brainstem Neurons Controlling Rhythmic Activity.

PLoS computational biology·2015

Same author

Quantitative assessment of computational models for retinotopic map formation.

Developmental neurobiology·2014

Same author

Analysis of local and global topographic order in mouse retinocollicular maps.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2014

Search research articles

Related Experiment Video

Updated: May 3, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

morphforge: a toolbox for simulating small networks of biologically detailed neurons in Python.

Michael J Hull¹, David J Willshaw¹

¹Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh Edinburgh, UK.

Frontiers in Neuroinformatics

|January 31, 2014

Summary

Morphforge simplifies complex biophysical neuron simulations. This Python toolbox streamlines the creation and management of multicompartmental neuron models, accelerating neuroscience research.

Keywords:

Python biophysical modeling code-generation multicompartmental modeling small neuronal network toolbox

More Related Videos

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Related Experiment Videos

Last Updated: May 3, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Area of Science:

Computational Neuroscience
Biophysics
Software Development

Background:

Developing complex biophysical neuron models is time-consuming.
Software ecosystem complexities hinder efficient simulation workflows.
Managing files, data formats, and code errors consume significant researcher time.

Purpose of the Study:

Introduce morphforge, a Python toolbox for building and managing simulations of multicompartmental biophysical model neurons.
Simplify the process of creating and running in silico experiments.
Facilitate parameter space exploration and component reuse.

Main Methods:

Utilizes a high-level, object-oriented Python interface.
Enables definition of neuronal morphologies, channel properties, stimuli, and analysis within a single script.
Supports multiple independent simulations and parameter variations.

Main Results:

Allows entire in silico experiments to be defined in a single Python script.
Facilitates investigation of parameter spaces through multiple simulations.
Includes features like automatic documentation generation and transparent unit handling.

Conclusions:

Morphforge significantly reduces the effort required to build and manage complex neuron simulations.
The toolbox accelerates neuroscience research by streamlining the simulation workflow.
Provides a flexible platform for further development of simulation tools.