emergent provides a toolkit / framework for implementing neural network models, written in Go, with an optional Python interface that is automatically generated from the Go code. See the github site for all the code and extensive documentation.

The primary application of emergent is for the biologically-based Leabra algorithm, which simulates point neuron equations with either rate-code or spiking activations, and a biologically-based form of error-backpropagation based on bidirectional excitatory projections. This more complex, many-state-variable model is not well-supported by existing backpropagation-oriented tools such as PyTorch or TensorFlow, which operate fundamentally on simple tensors of floating-point numbers.

A primary goal of emergent is to provide effective GUI interfaces that make it easier to understand the functioning of complex neural models, including an interactive 3D NetView, interactive 2D line and bar plots, heat-map grids of tensors, and tables of data. The eTorch repository provides these interactive GUI elements for PyTorch-based models.

Most existing Python-based simulation systems rely on C / C++ for the high-performance back-end computation, accessed via the higher-level but much slower Python wrapper language. Go provides an attractive alternative language framework, featuring the runtime speed of complied C code, but a very rapid compilation speed approaching that of interpreted languages like Python (just a few seconds per compile / run iteration). Furthermore, the language is very elegant and well-designed, with an extensive standard library, resulting in a high-level of programmer productivity. The garbage-collection runtime system eliminates a huge amount of complexity and headache, and has essentially no noticible effect on running simulations which do not typically allocate much memory once they have started.