Name
Abstract | ||
|---|---|---|
Even though artificial vision has been in development for over half a century it still fares poorly when compared to biological vision. The processing capabilities of biological visual systems are vastly superior in terms of power, speed, and performance. Inspired by this robust performance artificial vision systems have sought to take inspiration from biology by modeling aspects of biological vision systems. Existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. In this work we use state-of-the-art fuzzy neural network techniques to accurately model the responses of retinal ganglion cells. We illustrate how a self-organising fuzzy neural network can accurately model ganglion cell behaviour, and are a viable alternative to traditional system identification techniques. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | Retinal ganglion,Neuro-fuzzy,Structured systems analysis and design method,Machine vision,Computer science,Human visual system model,Computational model,Artificial intelligence,System identification,Artificial neural network,Machine learning |
DocType | ISSN | Citations |
Conference | 2161-4393 | 1 |
PageRank | References | Authors |
0.37 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Scott McDonald | 1 | 1 | 0.37 |
| Dermot Kerr | 2 | 50 | 13.84 |
| Sonya Coleman | 3 | 216 | 36.84 |
| Philip J. Vance | 4 | 41 | 4.92 |
| T. Martin Mcginnity | 5 | 518 | 66.30 |