about
A Framework for Quantitative Modeling of Neural Circuits Involved in Sleep-to-Wake TransitionData-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobeEvent- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.Computational models to understand decision making and pattern recognition in the insect brain.Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays.Simultaneous long-term recordings at two neuronal processing stages in behaving honeybees.A neural network model for familiarity and context learning during honeybee foraging flights.Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.
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P2860
description
2013 nî lūn-bûn
@nan
2013 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
Gain control network conditions in early sensory coding
@ast
Gain control network conditions in early sensory coding
@en
Gain control network conditions in early sensory coding
@nl
type
label
Gain control network conditions in early sensory coding
@ast
Gain control network conditions in early sensory coding
@en
Gain control network conditions in early sensory coding
@nl
prefLabel
Gain control network conditions in early sensory coding
@ast
Gain control network conditions in early sensory coding
@en
Gain control network conditions in early sensory coding
@nl
P2860
P50
P1476
Gain control network conditions in early sensory coding
@en
P2093
Brian H Smith
P2860
P304
P356
10.1371/JOURNAL.PCBI.1003133
P577
2013-07-18T00:00:00Z