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Title: Sparse balance: Excitatory-inhibitory networks with small bias currents and broadly distributed synaptic weights
Cortical circuits generate excitatory currents that must be cancelled by strong inhibition to assure stability. The resulting excitatory-inhibitory (E-I) balance can generate spontaneous irregular activity but, in standard balanced E-I models, this requires that an extremely strong feedforward bias current be included along with the recurrent excitation and inhibition. The absence of experimental evidence for such large bias currents inspired us to examine an alternative regime that exhibits asynchronous activity without requiring unrealistically large feedforward input. In these networks, irregular spontaneous activity is supported by a continually changing sparse set of neurons. To support this activity, synaptic strengths must be drawn from high-variance distributions. Unlike standard balanced networks, these sparse balance networks exhibit robust nonlinear responses to uniform inputs and non-Gaussian input statistics. Interestingly, the speed, not the size, of synaptic fluctuations dictates the degree of sparsity in the model. In addition to simulations, we provide a mean-field analysis to illustrate the properties of these networks.  more » « less
Award ID(s):
1707398
NSF-PAR ID:
10338003
Author(s) / Creator(s):
; ;
Editor(s):
Soltani, Alireza
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
18
Issue:
2
ISSN:
1553-7358
Page Range / eLocation ID:
e1008836
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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