# Learning in Competitive Network with Haeusslers Equation adapted using FIREFLY algorithm

## TL;DR

This paper introduces a biologically inspired learning method for competitive neural networks using Haeussler's equation and a Firefly algorithm-based wiring scheme, enabling automatic learning without hand-wired connections.

## Contribution

It presents a novel approach combining Haeussler's equation with a Firefly algorithm to learn network connections dynamically, removing the need for predefined wiring.

## Key findings

- Network learns from input patterns without hand-wiring.
- The method adapts connections based on data, improving flexibility.
- Demonstrates effective learning in recurrent competitive networks.

## Abstract

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.