Learning in Competitive Network with Haeusslers Equation adapted using FIREFLY algorithm
This work addresses a domain-specific issue for researchers in neural networks by providing an incremental improvement to reduce manual intervention in network design.
The paper tackled the problem of hand-wiring local excitatory connections in competitive neural networks by introducing a learning approach using Haeusslers equation and a Firefly algorithm-based wiring scheme, enabling learning from input patterns without predefined topology.
Many of the competitive neural network consists of spatially arranged neurons. The weigh matrix that connects cells represents local excitation and long-range inhibition. They are known as soft-winner-take-all networks and shown to exhibit desirable information-processing. The local excitatory connections are many times predefined hand-wired based depending on spatial arrangement which is chosen using the previous knowledge of data. Here we present learning in recurrent network through Haeusslers equation and modified wiring scheme based on biologically based Firefly algorithm. Following results show learning in such network from input patterns without hand-wiring with fixed topology.