Purchase this article with an account.
Stefan Roth, Daniel Kiper, Paul F. M. J. Verschure; Visual segmentation in a biomorphic neural network. Journal of Vision 2006;6(6):888. doi: 10.1167/6.6.888.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Based on our earlier work on the encoding of complex stimuli by cortical networks (Wyss et al., 2003) we designed a neural network to classify visual stimuli. The transmission delays of the connections among the excitatory neurons, which are proportional to length, result in a complex spatial and temporal activity pattern when a visual stimulus is presented to the network. The pooled neuronal activity, or temporal population code - TPC, represents a stimulus-specific fingerprint that is invariant to translations, rotations, and small distortions. The TPC thus allows a highly accurate and robust stimulus classification in situations where a single target is presented to the network. However, classification fails when the scene also includes a closely positioned distractor. We show here that by introducing dendritic attenuation in the model, we are able to increase classification performance in the presence of a distractor, i.e. segmentation. Moreover, by combining codes of several attenuation levels, we obtain a segmentation model that is robust and scalable both with and without distractor. Hence, our results suggest that both dense lateral connectivity and dendritic structure provide a complementary computational substrate for the encoding of complex stimuli.
This project is supported by the Swiss Federal Institute of Technology (ETH) Zurich.
This PDF is available to Subscribers Only