In a multiscale stochastic network, combining signals before the nonlinearity (integration) encodes more input-output information than combining them after the nonlinearity (summation), with fast processing and certain layer sizes giving the largest gains.
Mochizuki, An analytical study of the number of steady states in gene regulatory networks, Journal of Theoretical Biology 236, 291 (2005)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.stat-mech 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Multiscale nonlinear integration drives accurate encoding of input information
In a multiscale stochastic network, combining signals before the nonlinearity (integration) encodes more input-output information than combining them after the nonlinearity (summation), with fast processing and certain layer sizes giving the largest gains.