Neuronal Synchrony in Complex-Valued Deep Networks
Authors: David Reichert, Thomas Serre
ICLR 2014

Abstract: Deep learning has recently lead to great successes in tasks such as image recognition (e.g Krizhevsky et al., 2012). However, deep networks are still outmatched by the power and versatility of the brain, perhaps in part due to the richer neuronal computations available to real cortical circuits. The challenge is to identify which neural mechanisms are relevant, and to find suitable abstractions to model them. Here, we show how aspects of spike timing, long hypothesized to play a crucial role in cortical information processing, could be incorporated into deep networks to build richer, versatile deep representations. 
We introduce a neural network formulation based on complex-valued neuronal units that is not only biologically meaningful but also amenable to a variety of deep learning frameworks. Here, units are attributed both a firing rate and a phase, the latter indicating properties of spike timing. We show how this formulation qualitatively captures several aspects thought to be related to neuronal synchrony, including gating of information processing and dynamic binding of distributed object representations. Focusing on the latter aspect, we demonstrate the potential of the approach in several simple experiments. Thus, synchrony could implement a flexible mechanism that fulfills multiple functional roles in deep networks.


Peer review:
[Review]:
Thanks for the comment.

Just to clarify, in the broader context there is plenty of relevant work that we did not discuss, due to limited space (we only discussed closely related work based on complex-valued nets). This includes models using coupled oscillators for segmentation. In particular, see also (and references therein):

Yu, G., & Slotine, J.-J. (2009). Visual Grouping by Neural Oscillator Networks. IEEE Transactions on Neural Networks, 20(12), 1871–1884. doi:10.1109/TNN.2009.2031678