In:
Neural Computation, MIT Press, Vol. 31, No. 11 ( 2019-11), p. 2252-2265
Abstract:
In computational neural network models, neurons are usually allowed to excite some and inhibit other neurons, depending on the weight of their synaptic connections. The traditional way to transform such networks into networks that obey Dale's law (i.e., a neuron can either excite or inhibit) is to accompany each excitatory neuron with an inhibitory one through which inhibitory signals are mediated. However, this requires an equal number of excitatory and inhibitory neurons, whereas a realistic number of inhibitory neurons is much smaller. In this letter, we propose a model of nonlinear interaction of inhibitory synapses on dendritic compartments of excitatory neurons that allows the excitatory neurons to mediate inhibitory signals through a subset of the inhibitory population. With this construction, the number of required inhibitory neurons can be reduced tremendously.
Type of Medium:
Online Resource
ISSN:
0899-7667
,
1530-888X
DOI:
10.1162/neco_a_01230
Language:
English
Publisher:
MIT Press
Publication Date:
2019
detail.hit.zdb_id:
1025692-1
detail.hit.zdb_id:
1498403-9