Leabra7: a Python package for modeling recurrent, biologically-realistic neural networks
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. L...
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| Veröffentlicht in: | arXiv.org 2018-09 |
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| creator | Greenidge, C Daniel Miller, Noam Norman, Kenneth A |
| description | 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. |
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| subjects | Algorithms Computer simulation Machine learning Neural networks Python Recurrent neural networks Software development |
| title | Leabra7: a Python package for modeling recurrent, biologically-realistic neural networks |
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