Network
The network component of Simbrain represents a simulated neural circuit. For a quick dive in see the quick start or this video, which contains a series of links to short clips describing common network operations (adding neurons, connecting neurons, editing neuron groups, etc.).
Networks contain a variety of objects (“network models”), including free-floating neurons and synapses, but also neuron arrays and weight matrices for array-based networks, and other more complex structures. This page contains a brief overview of the main object types. Most objects can be linked to one another with synapses, synapse groups, or weight matrices. The Simbrain philosophy is to allow for arbitrary combinations of network models. Many tools and utilities exist for organizing, arranging, and training these linked network models.
Main Object Types
Free neurons and synapses
Free neurons and synapses are part of “classic” Simbrain, allowing nodes and connections to be organized in arbitrary ways using a familiar point-and-click interface. Both nodes and synapses can be equipped with arbitrary rules. See neurons and synapses.
Neuron Groups, Synapse Groups, and Neuron Collections
Free neurons and weights can be aggregated in various ways but are moved and adjusted as a group, via a yellow interaction box. See neuron groups and synapse groups.
Note that neuron collections (like supervised models) are transient wrappers. If you delete them the underlying objects remain in place. They have green interaction boxes. In cases where the surrounded objects are dependent on the group they are part of, a yellow interaction box is used.
Neuron Arrays and Weight Matrices
An alternative to free nodes and weights (and collections of them) is neuron arrays and weight matrices, which support array-based operations, as is standard in modern neural networks. These are much faster and more conventional, though somewhat less intuitive at first. See arrays and matrices.
Neuron collections and groups can be linked to neuron arrays with weight matrices or synapse groups.
Subnetworks
These are customized collections of network models (neurons, neuron groups, neuron arrays, etc.) that can be associated with data and other objects, and that are updated in a customized way. Examples include backprop networks and restricted boltzmann machines. See subnetworks.
See Also
- Building Networks - Basic network construction
- Network Update - How networks are updated
- Learning - Training and learning approaches
- Evolution - Evolving neural networks
- Network Menu - Menu commands