Designing biologically inspired receptive fields for neural pattern recognition technology

The paper describes a new method to incorporate biologically inspired receptive fields in feedforward neural networks to enhance pattern recognition performance. We propose a neural architecture composed of two networks in cascade: a feature extraction network followed by a neural classifier. A genetic algorithm is used to search for the receptive field configuration in the problem of handwritten digit recognition. The proposed network, with properly designed receptive fields, shows an improvement in the classification performance relative to other neural network models with fully connected architectures where receptive fields are not explicitly defined. A self organizing map is used to show the relative distance among patterns before and after the transformation performed by the network with biologically inspired receptive fields