Leabra7: a Python package for modeling recurrent, biologically-realistic neural networks
September 11, 2018 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
C. Daniel Greenidge, Noam Miller, Kenneth A. Norman
arXiv ID
1809.04166
Category
cs.NE: Neural & Evolutionary
Cross-listed
q-bio.NC
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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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