AnkurMali/ContinualPTNCN
We introduce Local recurrent Predictive coding model termed as Parallel temporal Neural Coding Network. Unlike classical RNNs, our model is pure local and doesn't require computing gradients backward in time; thus computationally more efficient compared to BPTT and can be used for online learning
This project offers a specialized neural network model, the Parallel Temporal Neural Coding Network (P-TNCN), that processes sequences of information more efficiently than traditional recurrent neural networks. It takes sequential data as input and learns patterns to make predictions or classify future elements in the sequence. It's designed for researchers and machine learning engineers working on advanced neural network architectures, particularly those interested in brain-inspired AI and online learning.
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Use this if you are a researcher or AI engineer looking for a computationally efficient, brain-inspired recurrent neural network that supports online learning and does not require backpropagation through time.
Not ideal if you are looking for a plug-and-play solution for standard sequence modeling tasks without diving into the specifics of novel neural network architectures.
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12
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3
Language
Python
License
MIT
Category
Last pushed
Nov 29, 2023
Commits (30d)
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