TY - GEN
T1 - Sparseness controls the receptive field characteristics of V4 neurons
T2 - 23rd International Conference on Artificial Neural Networks, ICANN 2013
AU - Hatori, Yasuhiro
AU - Mashita, Tatsuroh
AU - Sakai, Ko
PY - 2013
Y1 - 2013
N2 - Physiological studies have reported that the intermediate-level visual area represents primitive shape by the selectivity to curvature and its direction. However, it has not been revealed that what coding scheme underlies the construction of the selectivity with complex characteristics. We propose that sparse representation is crucial for the construction so that a sole control of sparseness is capable of generating physiological characteristics. To test the proposal, we applied component analysis with sparseness constraint to activities of model neurons, and investigated whether the computed bases reproduce the characteristics of the selectivity. To evaluate the learned bases quantitatively, we computed the tuning properties of single bases and the population, as similar to the physiological reports. The basis functions reproduced the physiological characteristics when sparseness was medium (0.6-0.8). These results indicate that sparse representation is crucial for the curvature selectivity, and that a sole control of sparseness is capable of constructing the representation.
AB - Physiological studies have reported that the intermediate-level visual area represents primitive shape by the selectivity to curvature and its direction. However, it has not been revealed that what coding scheme underlies the construction of the selectivity with complex characteristics. We propose that sparse representation is crucial for the construction so that a sole control of sparseness is capable of generating physiological characteristics. To test the proposal, we applied component analysis with sparseness constraint to activities of model neurons, and investigated whether the computed bases reproduce the characteristics of the selectivity. To evaluate the learned bases quantitatively, we computed the tuning properties of single bases and the population, as similar to the physiological reports. The basis functions reproduced the physiological characteristics when sparseness was medium (0.6-0.8). These results indicate that sparse representation is crucial for the curvature selectivity, and that a sole control of sparseness is capable of constructing the representation.
KW - computational model
KW - curvature
KW - shape representation
KW - sparse coding
UR - http://www.scopus.com/inward/record.url?scp=84884941559&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84884941559&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40728-4_41
DO - 10.1007/978-3-642-40728-4_41
M3 - Conference contribution
AN - SCOPUS:84884941559
SN - 9783642407277
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 327
EP - 334
BT - Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings
Y2 - 10 September 2013 through 13 September 2013
ER -