TY - GEN
T1 - Low level modeling of the development of directionally selective microcircuits in cat striate cortex
AU - Fagg, Andrew H.
AU - Fiser, József
N1 - Publisher Copyright:
© 1993 IEEE.
PY - 1993
Y1 - 1993
N2 - In this paper development of a directionally selective structure in cat striate cortex is used as a paradigm for exploring issues of biologically plausible and computationally interesting learning algorithms. A compartmental model of a "canonical microcircuit" is used to represent functional units of the cortex. Initially, the thalamus projects in a random fashion to a pair of cortical microcircuits. Through the use of a sliding threshold model of LTP and LTD, these projections develop biologically plausible, directionally selective responses to randomly moving visual stimuli Possible implications of the learning algorithm on self-organization in the developing cortex are discussed.
AB - In this paper development of a directionally selective structure in cat striate cortex is used as a paradigm for exploring issues of biologically plausible and computationally interesting learning algorithms. A compartmental model of a "canonical microcircuit" is used to represent functional units of the cortex. Initially, the thalamus projects in a random fashion to a pair of cortical microcircuits. Through the use of a sliding threshold model of LTP and LTD, these projections develop biologically plausible, directionally selective responses to randomly moving visual stimuli Possible implications of the learning algorithm on self-organization in the developing cortex are discussed.
UR - http://www.scopus.com/inward/record.url?scp=84943329725&partnerID=8YFLogxK
U2 - 10.1109/ICNN.1993.298653
DO - 10.1109/ICNN.1993.298653
M3 - Conference contribution
AN - SCOPUS:84943329725
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 772
EP - 777
BT - 1993 IEEE International Conference on Neural Networks, ICNN 1993
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Neural Networks, ICNN 1993
Y2 - 28 March 1993 through 1 April 1993
ER -