| Full text | |
| Author(s): |
Garimella, Rama Murthy
;
Valle, Marcos Eduardo
;
Vieira, Guilherme
;
Rayala, Anil
;
Munugoti, Dileep
Total Authors: 5
|
| Document type: | Journal article |
| Source: | NEURAL PROCESSING LETTERS; v. N/A, p. 20-pg., 2022-09-23. |
| Abstract | |
The Hopfield network is an example of an artificial neural network used to implement associative memories. A binary digit represents the neuron's state of a traditional Hopfield neural network. Inspired by the human brain's ability to cope simultaneously with multiple sensorial inputs, this paper presents three multi-modal Hopfield-type neural networks that treat multi-dimensional data as a single entity. In the first model, called the vector-valued Hopfield neural network, the neuron's state is a vector of binary digits. Synaptic weights are modeled as finite impulse response (FIR) filters in the second model, yielding the so-called convolutional associative memory. Finally, the synaptic weights are modeled by linear time-varying (LTV) filters in the third model. Besides their potential applications for multi-modal intelligence, the new associative memories may also be used for signal and image processing and solve optimization and classification tasks. (AU) | |
| FAPESP's process: | 19/02278-2 - Mathematical Morphology and Morphological Neural Networks for Multivalued Data |
| Grantee: | Marcos Eduardo Ribeiro Do Valle Mesquita |
| Support Opportunities: | Regular Research Grants |