TY - JOUR
T1 - A neuronal network for the logic of Limax learning
AU - Goel, Pranay
AU - Gelperin, Alan
N1 - Funding Information:
Acknowledgments P. Goel would like to acknowledge that this material is based upon work supported by the National Science Foundation under Agreement No. 0112050. A. Gelperin would like to acknowledge support from the Army Research Office and NIH Grant MH56090.
PY - 2006/12
Y1 - 2006/12
N2 - We construct a neuronal network to model the logic of associative conditioning as revealed in experimental results using the terrestrial mollusk Limax maximus. We show, in particular, how blocking to a previously conditioned stimulus in the presence of the unconditional stimulus, can emerge as a dynamical property of the network. We also propose experiments to test the new model.
AB - We construct a neuronal network to model the logic of associative conditioning as revealed in experimental results using the terrestrial mollusk Limax maximus. We show, in particular, how blocking to a previously conditioned stimulus in the presence of the unconditional stimulus, can emerge as a dynamical property of the network. We also propose experiments to test the new model.
KW - Associative conditioning
KW - Limax
KW - Logic of learning behavior
KW - Neuronal network
UR - https://www.scopus.com/pages/publications/33749531915
UR - https://www.scopus.com/pages/publications/33749531915#tab=citedBy
U2 - 10.1007/s10827-006-8097-7
DO - 10.1007/s10827-006-8097-7
M3 - Article
C2 - 16927210
AN - SCOPUS:33749531915
SN - 0929-5313
VL - 21
SP - 259
EP - 270
JO - Journal of Computational Neuroscience
JF - Journal of Computational Neuroscience
IS - 3
ER -