Leabra7: a Python package for modeling recurrent, biologically-realistic neural networks
This provides a modern, Python-based tool for researchers in computational neuroscience and machine learning to model recurrent networks with biological realism, but it is incremental as it reimplements existing algorithms.
The authors introduced Leabra7, a Python package implementing the AdEx neural dynamics model and LEABRA learning algorithm for simulating recurrent, biologically-realistic neural networks, and demonstrated its functionality on pattern-association tasks and classifying the IRIS dataset.
Emergent is a software package that uses the AdEx neural dynamics model and LEABRA learning algorithm to simulate and train arbitrary recurrent neural network architectures in a biologically-realistic manner. We present Leabra7, a complementary Python library that implements these same algorithms. Leabra7 is developed and distributed using modern software development principles, and integrates tightly with Python's scientific stack. We demonstrate recurrent Leabra7 networks using traditional pattern-association tasks and a standard machine learning task, classifying the IRIS dataset.