Design

Go emergent Design

• In general, emergent works by compiling programs into executables which you then run like any other executable. This is very different from the C++ version of emergent which was a single monolithic program attempting to have all functionality built-in. Instead, the new model is the more prevalent approach of writing more specific code to achieve more specific goals, which is more flexible and allows individuals to be more in control of their own destiny..

  • To make your own simulations, start with e.g., the examples/ra25/ra25.go code (or that of a more appropriate example) and copy that to your own repository, and edit accordingly.

• The emergent repository contains a collection of packages supporting the implementation of biologically-based neural networks. The main package is emer which specifies a minimal abstract interface for a neural network.

• Go uses interfaces to represent abstract collections of functionality (i.e., sets of methods). The emer package provides a set of minimal interfaces for each structural level (e.g., emer.Layer etc), along with concrete "Base" types that implement a lot of shared functionality (e.g., emer.LayerBase), which are available as AsEmer() from the interface. Each algorithm must implement the interface methods to support the Network view, logging, parameter setting and other shared emergent functionality.

• The emer interfaces are designed to support generic access to network state, e.g., for the 3D network viewer, but specifically avoid anything algorithmic or structural, so that most of the algorithm-specific code uses direct slices and methods that return algorithm-specific types.

• There are 3 main levels of structure: Network, Layer and Path (pathway). The network calls methods on its Layers, and Layers iterate over both Neuron data structures (which are purely data structures) and the Paths, to implement the relevant computations. The Path fully manages everything about a pathway of connectivity between two layers, including the full list of Synapse elements in the connection. There is no "ConGroup" or "ConState" level as was used in C++, which greatly simplifies many things. The Layer also has a set of Pool elements, one for each level at which inhibition is computed (there is always one for the Layer, and then optionally one for each Sub-Pool of units (Pool is the new simpler term for "Unit Group" from C++ emergent).

• Layers have a Shape property, using the tensor.Shape type (see cogent core tensor package), which specifies their n-dimensional (tensor) shape. Standard layers are expected to use a 2D Y*X shape (note: dimension order is now outer-to-inner or RowMajor now), and a 4D shape then enables Pools ("unit groups") as hypercolumn-like structures within a layer that can have their own local level of inihbition, and are also used extensively for organizing patterns of connectivity.