TY - GEN
T1 - Biologically Plausible Complex-Valued Neural Networks and Model Optimization
AU - Yu, Ryan
AU - Wood, Andrew
AU - Cohen, Sarel
AU - Hershcovitch, Moshick
AU - Waddington, Daniel
AU - Chin, Peter
N1 - Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.
PY - 2022
Y1 - 2022
N2 - Artificial Neural Networks (ANNs) are thinly based on biological neural pathways. In an ANN, each node computes its activation by applying a non-linearity to a weighted sum of its inputs. While this formulation has been wildly successful for a variety of tasks, it is still a far cry from its biological counterpart, largely due to ANNs lack of phase information during computation. In this paper, we adapt ANNs to operate on complex values which naturally allows the inclusion of phase information during the forward pass. We demonstrate that our complex-valued architecture generally performs better compared to real-valued and other complex-valued networks in similar conditions. Additionally, we couple our model with a biologically inspired form of dimensionality reduction and present our findings on the MNIST and MusicNet data sets.
AB - Artificial Neural Networks (ANNs) are thinly based on biological neural pathways. In an ANN, each node computes its activation by applying a non-linearity to a weighted sum of its inputs. While this formulation has been wildly successful for a variety of tasks, it is still a far cry from its biological counterpart, largely due to ANNs lack of phase information during computation. In this paper, we adapt ANNs to operate on complex values which naturally allows the inclusion of phase information during the forward pass. We demonstrate that our complex-valued architecture generally performs better compared to real-valued and other complex-valued networks in similar conditions. Additionally, we couple our model with a biologically inspired form of dimensionality reduction and present our findings on the MNIST and MusicNet data sets.
UR - http://www.scopus.com/inward/record.url?scp=85133268920&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-08333-4_30
DO - 10.1007/978-3-031-08333-4_30
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AN - SCOPUS:85133268920
SN - 9783031083327
T3 - IFIP Advances in Information and Communication Technology
SP - 369
EP - 382
BT - Artificial Intelligence Applications and Innovations - 18th IFIP WG 12.5 International Conference, AIAI 2022, Proceedings
A2 - Maglogiannis, Ilias
A2 - Iliadis, Lazaros
A2 - Macintyre, John
A2 - Cortez, Paulo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022
Y2 - 17 June 2022 through 20 June 2022
ER -