Cogent Confabulation neural network

Hi all…I’m new to the forum and have a basic question.


I’m interested in using PyTorch to develop a new type of neural network. It is based on the theory of Cogent Confabulation by the late Robert Hecht-Nielsen. I recently published a paper in the Neural Networks journal showing improved entity recognition using a measure of ontology cogency that I developed (my PhD dissertation results). Not asking anyone to explore the theory of confabulation to respond to my question, just providing context. But, if interested, please see

Question related to PyTorch:

I’m new to PyTorch, and don’t quite know where to start, so I’m looking for ideas on PyTorch customization.

Basically I want to develop a custom recurrent layer where the output of the layer is the product of conditional probabilities.

Right now the conditional probabilities are computed from a corpora and ontology prior to training a neural network. To keep life simple, let’s assume that will not change.

How does one go about developing a customer layer in PyTorch that computes the product of the conditional probabilities?

Thanks in advance for your advice.