CITATION.cff

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title: >-
  Signal Propagation: The Library for Forward Learning in
  Neural Networks
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Adam
    family-names: Kohan
    email: akohanumass@gmail.com
identifiers:
  - type: doi
    value: 10.1109/TNNLS.2022.3230914
  - type: url
    value: 'https://arxiv.org/abs/2204.01723'
    description: Arxiv Paper
repository-code: 'https://github.com/amassivek/signalpropagation/'
url: 'https://github.com/amassivek/signalpropagation/'
abstract: >-
  Signal Propgation is the Library for Learning in a Forward
  Pass for Neural Networks. Wrap any PyTorch model using
  this library to use only forward passes for learning,
  instead backpropagation. Forward Pass Learning is a least
  constrained learning method, efficient in memory and
  computation, and compatible with edge and neuromorphic
  devices.
keywords:
  - forward learning
  - signal propagation
  - neural networks
  - local learning
  - deep learning
  - artificial intelligence
  - spiking neural networks
  - recurrent neural networks
  - continuous neural networks
  - biological neural networks