" ... We propose here an oscillatory neural network model that performs the function of an autoencoder. The model is a hybrid of rate-coded neurons and neural oscillators. Input signals modulate the frequency of the neural encoder oscillators. These signals are then multiplexed using a network of rate-code neurons that has afferent Hebbian and lateral anti-Hebbian connectivity, termed as Lateral Anti Hebbian Network (LAHN). Finally the LAHN output is de-multiplexed using an output neural layer which is a combination of adaptive Hopf and Kuramoto oscillators for the signal reconstruction. The Kuramoto-Hopf combination performing demodulation is a novel way of describing a neural phase-locked loop. The proposed model is tested using both synthetic signals and real world EEG signals. The proposed model arises out of the general motivation to construct biologically inspired, oscillatory versions of some of the standard neural network models, and presents itself as an autoencoder network based on oscillatory neurons applicable to time series signals. As a demonstration, the model is applied to compression of EEG signals."
Model Type: Connectionist Network
Model Concept(s): Oscillations
Simulation Environment: MATLAB
Implementer(s): Soman, Karthik [karthi.soman at gmail.com]
References:
Soman K, Muralidharan V, Chakravarthy VS. (2018). An Oscillatory Neural Autoencoder Based on Frequency Modulation and Multiplexing. Frontiers in computational neuroscience. 12 [PubMed]