TY - JOUR
T1 - Solutions of the BCM learning rule in a network of lateral interacting nonlinear neurons
AU - Castellani, G. C.
AU - Intrator, N.
AU - Shouval, H.
AU - Cooper, L. N.
N1 - Funding Information:
The authors thank the members of the Institute for Brain and Neural Systems for many fruitful conversations. This research was supported by the Charles A Dana Foundation, the Office of Naval Research and the National Science Foundation; GCC was partially supported by an exchange program between Bologna and Brown Universities.
PY - 1999/5
Y1 - 1999/5
N2 - We introduce a new method for obtaining the fixed points for neurons that follow the BCM learning rule. The new formalism, which is based on the objective function formulation, permits analysis of a laterally connected network of nonlinear neurons and allows explicit calculation of the fixed points under various network conditions. We show that the stable fixed points, in terms of the postsynaptic activity, are not altered by the lateral connectivity or nonlinearity. We show that the lateral connectivity alters the probability of attaining different states in a network of interacting neurons. We further show the exact alteration in presynaptic weights as a result of the neuronal nonlinearity.
AB - We introduce a new method for obtaining the fixed points for neurons that follow the BCM learning rule. The new formalism, which is based on the objective function formulation, permits analysis of a laterally connected network of nonlinear neurons and allows explicit calculation of the fixed points under various network conditions. We show that the stable fixed points, in terms of the postsynaptic activity, are not altered by the lateral connectivity or nonlinearity. We show that the lateral connectivity alters the probability of attaining different states in a network of interacting neurons. We further show the exact alteration in presynaptic weights as a result of the neuronal nonlinearity.
UR - https://www.scopus.com/pages/publications/0042214286
U2 - 10.1088/0954-898x/10/2/001
DO - 10.1088/0954-898x/10/2/001
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AN - SCOPUS:0042214286
SN - 0954-898X
VL - 10
SP - 111
EP - 121
JO - Network: Computation in Neural Systems
JF - Network: Computation in Neural Systems
IS - 2
ER -